Reviewerly/exHarmony huggingface dataset:
Research on Deep Learning Techniques in Breaking Text-Based Captchas and Designing Image-Based Captcha

Optical character recognition (OCR) is a process of text recognition in images (one word). The input images are taken from the dataset. The collected text images are implemented to pre-processing. In pre-processing, we can implement the image resize process. Image resizing is necessary when you need to increase or decrease the total number of pixels, whereas remapping can occur when you are zooming refers to increase the quantity of pixels, so that when you zoom an image, you will see clear content. After that, we can implement the segmentation process. In segmentation, we can segment the each characters in one word. We can extract the features values from the image that means test feature. In classification process, we have to classify the text from the image. Image classification is performed the images in order to identify which image contains text. A classifier is used to identify the image containing text. The experimental results shows that the accuracy.
An innovative approach to the development of intellectual potential in primary school

The article discusses innovative teaching methods in primary school. The widespread introduction of new forms and methods of teaching, including modern information technologies, to ensure the possibility of individual development of each child. The development of the ability to motivate actions, independently navigate the information received, the formation of creative non-standard thinking, the development of children through the maximum disclosure of their natural abilities, using the latest achievements of science and practice, are the main goals of innovation. Innovative activity in education as a socially significant practice aimed at moral self-improvement of a person is important because it is able to ensure the transformation of all existing types of practices in society.
미취학 자녀를 둔 기혼 여군 장교의 일·가정 양립 지원제도 활용 정도와 재직의도 영향요인

Purpose: This study aimed to identify utilization of the work-family support policy (WFSP) and factors affecting retention intention among Korean female military officers.Methods: This cross-sectional survey recruited 103 married female officers from the Korean Army, Navy, and Air force with preschool-aged children through convenience and snowball sampling. Via online surveys from June to November, 2018, the participants self-reported retention intention, work-family conflict, job satisfaction, and utilization of the WFSP. The data were analyzed using descriptive statistics, independent t-tests, one-way analysis of variance, Pearson''s correlation coefficient, and multiple linear regression.Results: Retention intention (22.29±5.98) was reported at the mid-level, lower than scores reported in the literature for female workers. Work-family conflict (32.51±5.29) and job satisfaction (63.10±7.45) were above the midpoint levels. Use of maternity leave (100.0%) and parental leave (92.2%) was high, especially compared to the rates of child-care day off (20.4%) and parenting time (20.4%). ‘Noticeable increases in childcare services within the army’ (22.8%) was reported as the supportive measure needed the most by female military officers. Job satisfaction (β=.43, p≤.001), the use of parenting time (β=-0.29, p=.002), living type (β=-.18, p=.043), and service type (β=-.16, p=.035) significantly influenced retention intention.Conclusions: The findings highlight the priority areas of importance within the WFSP and suggest that a family-friendly culture can improve female officers'' retention intention. Accordingly, policy changes at the Ministry of National Defense improving the system to enhance a family-friendly culture in the military is expected to strengthen the retention intention of female officers and contribute to excellence in the military workforce.
Elementary Education in Assam, India- Examining the trend and Progress

Primary education (or elementary education) is the building block of the entire education system in India and the first step of formal education for a child. In this study, an attempt has been made to examine the state of elementary education in Assam for the period 2006 to 2016. Progress of elementary education in the state over a decade is evaluated in terms of certain selected indicators based on secondary data. For reference or comparison purpose, we take the national average of the different parameters.For educational inputs we have taken school indicators and teacher indicators and for output we have taken enrolment indicators for Assam and then compared that with the national level i.e. with India. Further, we analysed the percentage enrolment as per the different social groups in Assam. Graphs and tables are used as tools to support the discussion.
Holistic Approach Employing Different Optimizers for the Recognition of District Names Using CNN Model

A Holistic approach is proposed for the recognition of Handwritten district names of Punjab state which are written in Gurmukhi Script. For the purpose of recognition, a Convolutional Neural Network(CNN) using deep learning is employed.  Initially, the dataset of 22000 of images is prepared for all the 22 district names of Punjab state and later a CNNis employed. The proposed  CNN architecture having 12 layers is developed and employed using three optimizers: Adam, SGD and RMSprop for the recognition task. Best Average Validation Accuracy achieved for the proposed CNN architecture is 95% and maximum achieved validation accuracy is 99% achieved by employing Adam Optimizer.
Enforcement an Evidence and Quality of Query Services in the Cost-Effective Cloud

Cloud outburst is dangerous as information gets leaked, thereby allowing sensitive data to a bunch of trusted agents to an unauthorized place of location. Security practitioners are always interested in data cloud leakage issues that arise from various sources, like e-mail correspondence and different network channels. In this paper emphasis is laid on how to assess the probability that the leaked information came from one or more agents. Thus, the proposed system can identify those parties who are guilty of such cloud leakage even if the information is altered. Data allocations are used by the system for this purpose which can induce data records that are realistic but not fake and to improve the identification of cloud leakage through e-mails. Filtering of these e-mails is done by blocking e-mails that contain pictures, videos or sensitive data in a corporation. Classification of e-mail is primarily done on the basis of fingerprints of message bodies. The white and black listing of mail addresses is the principle used in e-mail filtering and therefore converting the words specified to spam.
Empirical Sentiment Analysis of Social Pages

Sentiment analysis has gained a great consideration recently. With the growing data on the internet, we have the opportunity to use that data for analyzing the opinions about politics, products and the latest gadgets. Probably the data of social sites is most used for sentiment analysis. Facebook and Twitter are the most popular social media platforms, where people express their opinions freely. Humans have ability to understand written sentiments based on understanding the polarity of words. For a computer system we have to construct lexical-based systems in order to judge the sentiments in text data.
A Composite Chain Structure Blockchain Storage Method Based on Blockchain Technology

Blockchain is an effective means to store data securely. Existing blockchain systems mostly adopt the equal mining mode, and all bookkeepers (entities) record ledger books on a single main chain, resulting in random data storage in the whole blockchain. Moreover, in complex or classification scenarios, data in a single main chain cannot be correlated or regularly stored, resulting in low storage efficiency. To solve these problems, this paper proposes a composite chain structure blockchain storage method based on blockchain technology. This method firstly proposes the blockchain model of composite chain structure, then constructs the private chain and alliance chain respectively, and finally realizes the adaptive data association storage in complex or classified scenarios. The experimental results show that a composite chain structure blockchain storage method based on blockchain technology proposed in this paper has great advantages in storage efficiency, storage overhead, security performance, availability and other aspects.
Free Speech and Social Media in Academia

Purpose – This chapter explores the topic of free speech protections and social media use in academia through an examination of the current legal landscape as it applies to various stakeholders on university campuses in the United States. The authors focus this examination primarily on public universities. Methodology/Approach – Legal research methods were utilized, including an analysis of relevant United States federal and state laws, case law, and secondary sources such as law reviews. Non-legal sources, such as academic journals, were also reviewed, with particular emphasis on topics such as university policies, tenure protections, academic freedom, as well as current events. Findings – The law regarding personal social media communications in a university setting is a series of complex and interconnected legal questions. Courts are still flushing out how free speech protections, personal social media use, and other relevant legal protections (e.g., employment law) may interface in a university-related case. Outcomes of cases are highly fact driven, and legal precedent is still being established. Originality/Value – This chapter offers a comprehensive examination of the topic of free speech and social media use in United States academia by (1) examining legal protections as applied to various stakeholders on a college campus and (2) analyzing the current legal landscape of social media cases involving universities.
DEVELOPING LEARNING MATERIAL FOR TEACHING WRITING RECOUNT TEXT USING STAD TECHNIQUE FOR THE TENTH-GRADE STUDENT

This study was aimed to develop learning material for teaching writing recount text using STAD technique to the tenth-grade Students. The researcher developed the learning material because there were conditions in the class which do not support the situation in the learning process, The Learning process in writing skill, some students passive in the class so make the students not motivated and the learning process is not going well, and also the material used in the school is not complete enough. The procedure was based on ADDIE but because of the situation in this era (COVID-19), the research was conducted ADD ( Analyze, Design, and Develop). It was conducted through the following analyzing the needs, designing the framework, and Developing the material. The learning material for teaching writing recount text using STAD technique had been validated by the expert: the learning material is appropriate with some revisions. The researcher suggested that further development of learning material for teaching recount text using STAD, for the next developers who conduct the same product to make it more complete (ADDIE) until the Implementation and Evaluation section in the class to make the learning material better
Research on the Interaction of Environmental Factors in the Three Gorges Reservoir Area Based on the Two-layer Network System Dynamics Model

Abstract This paper takes the ecological security of the Chongqing Three Gorges Reservoir area as the research object, establishes five subsystems of policy, population, water environment, economy, and biology, and uses the analytic network process to calculate the impact weights of different indicators in each layer of the system. The results show that the weight of the water environment indicator layer is the largest, and other indicators have varying degrees of impact on ecological security. On this basis, construct a system dynamics model, select the main constraints of environmental factors in the Three Gorges Reservoir area, set three different development scenarios, and observe their impact on CODcr And determine the range of ecological safety thresholds.
THE NETWORK OF AGRICULTURAL LIBRARIES OF THE BSSR IN THE FIRST HALF OF THE 1970S

The article presents the analysis of state of agricultural libraries of the Byelorussian Soviet Socialist Republic (BSSR) in the first half of the 1970s. The main types of libraries of the presented network have been analysed; the data on library staff composition and education have been presented; the number of readers and total amount of books which were lent have been shown; the main activities of the agricultural libraries haven considered.
Discriminative Feature Adaptation via Conditional Mean Discrepancy for Cross-Domain Text Classification

This paper concerns the problem of Unsupervised Domain Adaptation (UDA) in text classification, aiming to transfer the knowledge from a source domain to a different but related target domain. Previous methods learn the discriminative feature of target domain in terms of noisy pseudo labels, which inevitably produces negative effects on training a robust model. In this paper, we propose a novel criterion Conditional Mean Discrepancy (CMD) to learn the discriminative features by matching the conditional distributions across domains. CMD embeds both the conditional distributions of source and target domains into tensor-product Hilbert space and computes Hilbert-Schmidt norm instead. We shed a new light on discriminative feature adaptation: the collective knowledge of discriminative features of different domains is naturally discovered by minimizing CMD. We propose Aligned Adaptation Networks (AAN) to learn the domain-invariant and discriminative features simultaneously based on Maximum Mean Discrepancy (MMD) and CMD. Meanwhile, to trade off between the marginal and conditional distributions, we further maximize both MMD and CMD criterions using adversarial strategy to make the features of AAN more discrepancy-invariant. To the best of our knowledge, this is the first work to definitely evaluate the shifts in the conditional distributions across domains. Experiments on cross-domain text classification demonstrate that AAN achieves better classification accuracy but less convergence time compared to the state-of-the-art deep methods.
Sustaining cultural tourism through higher female participation in Nigeria: the role of corporate social responsibility in oil host communities

This paper adds to the gender discourse in sustainable African tourism development from the corporate social responsibility (CSR) perspective. Specifically, we examine the impact of CSR on the development of rural women in cultural tourism. A total of 600 rural women were sampled across the Niger Delta. Results from the use of a logit model indicate a significant relationship between CSR and cultural tourism development in oil host communities in Nigeria. This implies that CSR of a multinational oil company (MOC) is a critical factor for sustaining cultural tourism. The findings suggest increased female participation in General Memorandum of Understanding (GMoU) interventions of MOC and the need to pay close attention to which extent the participation of rural women in the GMoU projects may be limited by cultural and traditional obstacles.
Statistical Mechanics of Quantum Error-Correcting Codes

Random quantum dynamics generates quantum error correcting codes that are robust against observers and errors. Here, the authors describe a simple physical picture of such codes, mapping characteristic code properties to the statistical mechanics of fluctuating ``entanglement domain walls''. The error correcting criterion is understood as a decoupling condition of domain walls, which leads to a universal scaling law of the ``code distance''.
Access to Handwashing during COVID-19: Need for a Human Right-Focused Approach to Ensure Right to Health

In the absence of the much-needed vaccine against the COVID-19 pandemic, the World Health Organization resorted to different non-pharmaceutical public health interventions. Handwashing that can be practised by washing hands using soap and water is one such intervention that forms a barrier against the spread of COVID-19; especially with the lack of affordability of an almost luxury item like sanitiser. However, the lack of access to handwashing has affected the right to health and has impacted the state measures to control the spread of COVID-19. Part I of the paper brings to light the following in the above context: (a) handwashing as a frontline defence against the spread of infectious disease; and (b) impact of lack of access to handwashing during COVID-19. Part II of the paper analyses in the context of COVID-19: (a) the IS approach taken by States to ensure handwashing; and proposes that (b) the States should have adopted the OUGHT approach to ensure handwashing.The paper attempts to criticize the State actions addressing the concerns and risks associated with infectious diseases and access to handwashing under an IS approach and proposes the importance to address them under an OUGHT approach to ensure the right to health for all.
The Theory of Social Relay by M.A. Rozov: Pathway for Understanding

The article analyzes the problems of modern epistemology in the context of pre­senting the views and philosophical heritage of the famous Russian philosopher Mikhail Alexandrovich Rozov. The relevance of the theory of social relay devel­oped by him and the corresponding “wave” ontology, especially in the period of thematic, terminological and substantial transformations of modern epistemo­logy, is shown. The author carry out the idea that without solving the ontological and methodological problems of the empirical analysis of scientific knowledge, it is impossible to correctly investigate knowledge. The article describes in detail the logic of Rozov’s reasoning, and also demonstrates the scale of M.A. Rozov on the reforming of epistemology and philosophy of science, on the formulation of an urgent agenda, problems, goals and objectives of the study of knowledge. The bottom line is that understanding semiotic objects (scientific knowledge) as phenomena of social memory, which are reproduced according to direct or indi­rect (verbalized) patterns, opens a new world of social relay races. The broad ap­plicability of Rozov’s theory in various empirical contexts is demonstrated, which allows discussing both traditional and modern philosophical and method­ological problems of the natural and socio-humanitarian sciences, as well as in epistemology and philosophy of science.
Investigation and robust synthesis of polynomials under perturbations based on the root locus parameter distribution diagram

Investigation of the 4 th order dynamic systems characteristic polynomials behavior in conditions of the interval parametric uncertainties is carried out on the basis of root locus portraits. The roots behavior regularities and corresponding diagrams for the root locus parameter distribution along the asymptotic stability bound are specified for the root locus portraits of the systems. On this basis the stability conditions are derived, graphic-analytical method is worked out for calculating intervals of variation for the polynomial family parameters ensuring its robust stability. The discovered regularities of the system root locus portrait behavior allow to extract hurwitz sub-families from the non-hurwitz families of interval polynomials and to determine whether there exists at least one stable polynomial in the unstable polynomial family.
Delineation of Alteration Zones Based on Wavelet Neural Network (WNN) and Concentration–Volume (C-V) Fractal Methods in the Hypogene Zone of Porphyry Copper Deposit, Shahr-e-Babak District, SE Iran

In this paper, we aim to achieve two specific objectives. The first one is to examine the applicability of wavelet neural network (WNN) technique in ore grade estimation, which is based on integration between wavelet theory and Artificial Neural Network (ANN). Different wavelets are applied as activation functions to estimate Cu grade of borehole data in the hypogene zone of porphyry ore deposit, Shahr-e-Babak district, SE Iran. WNN parameters such as dilation and translation are fixed and only the weights of the network are optimized during its learning process. The efficacy of this type of network in function learning and estimation is compared with Ordinary Kriging (OK). Secondly, we aim to delineate the potassic and phyllic alteration regions in the hypogene zone of Cu porphyry deposit based on the estimation obtained of WNN and OK methods, and utilize Concentration–Volume (C–V) fractal model. In this regard, at first C–V log–log plots are generated based on the results of OK and WNN. The plots then are used to determine the Cu threshold values of the alteration zones. To investigate the correlation between geological model and C-V fractal results, the log ratio matrix is applied. The results showed that, Cu values less than 1.1% from WNN have more overlapped voxels with phyllic alteration zone by overall accuracy (OA) of 0.74. Spatial correlation between the potassic alteration zones resulted from 3D geological modeling and high concentration zones in C-V fractal model showed that the alteration zone has Cu values between 1.1% and 2.2% with OA of 0.72 and finally have an appropriate overlap with Cu values greater than 2.2% with OA of 0.7. Generally, the results showed that the WNN (Morlet activation function) with OA greater than OK can be can be a suitable and robust tool for quantitative modeling of alteration zones, instead of qualitative methods.
Digitizing Humanities in South Africa: Computational linguistic resources, training, and community building

South Africa has eleven official languages. However, not all have received similar amounts of attention. In particular, for many of the languages, only a limited number of digital language resources (data sets and computational tools) exist. This scarcity hinders (computational) research in the fields of humanities and social sciences for these languages. Additionally, using existing computational linguistics tools in a practical setting requires expert knowledge on the usage of these tools. In South Africa, only a small number of people currently have this expertise, further limiting the type of research that relies on computational linguistic tools. The South African Centre for Digital Language Resources (SADiLaR) aims to enable and enhance research in the area of language technology by focusing on the development, management, and distribution of digital language resources for all South African languages. Additionally, it aims to build research capacity, specifically in the field of digital humanities. This requires several challenges to be resolved that we cluster under resources, training, and community building. SADiLaR hosts a repository of existing digital language resources and supports the development of new resources. Additionally, it provides training on the use of these resources, specifically for (but not limited to) researchers in the fields of humanities and social sciences. Through this training, SADiLaR tries to build a community of practice to boost information sharing in the area of digital humanities.