Reviewerly/exHarmony huggingface dataset:
Exploration of observational documentation of early childhood teachers who practice play-centered curriculum: Focusing on collaborative action research with the director

This study is a collaborative action research conducted by the director, a researcher, with teachers to change the observation documentation for teachers' understanding of play-centered play based on the 4th standard childcare curriculum. The study was conducted from May 2021 to January 2022. Through a collaborative action research, we examined what teachers have changed as they solved the observation documentation that they considered difficult in the play-centered curriculum. As a result of the collaborative action research, first, the principal and the teacher decided to face each other, and the principal, the researcher, supported and resolved the concerns of the participating teachers. Second, by finding the meaning of the observation documentation , the change in form, the realization of the importance of the process of reviewing the observation documentation, and the release of the burden of the observation documentation were made. Third, to see early childhood a play again, it is to learn the competence of infants and to rediscover infants' play. In the process of exploring observational documentation, I came to understand the play-centered curriculum. Through this collaborative action research, as teachers and researchers explore the method of recording observations documentation, the perspective of looking at infants and young children changes, which is meaningful in understanding the play-centered curriculum.
Unleashing The Global Potential: The Impact of Quality of Life on Knowledge Spillover and Productivity Disparities

The purpose of this study is to look into the relationship between knowledge spillover, productivity differences, and the impact of quality of life in different countries. In order to accomplish this goal, the econometric technique known as Cross Sectional Augmented Autoregressive Distributive Lag (CS-ARDL) is utilized throughout this work. The findings point to the existence of complementarities between knowledge spillovers and the factors of quality of life in terms of their influence on total factor productivity. The findings imply that there is a contribution of knowledge spillovers to the national level of production. On the other hand, the relationship between knowledge spillovers and domestic productivity is contingent on a predetermined level of human capital as well as economic liberty. Hence, countries with more developed human capital and high economic freedom get more benefited from knowledge spillovers. This study has far-reaching ramifications. These findings can be used by policymakers to develop policies and initiatives that emphasize quality of life improvements, provide a conducive climate for knowledge spillover, and support productivity development across nations. Such approaches could help reduce economic inequities and propel global progress toward sustainable and inclusive growth.
AN OBSERVATIONAL STUDY OF IMMUNOPHENOTYPIC PATTERN IN MORPHOLOGICALLY DIAGNOSED CASES OF ACUTE MYELOID LEUKEMIA AT SMS MEDICAL COLLEGE, JAIPUR.

Background : The diagnosis and classication of AML were based on the French–American–British (FAB) system for nearly three decades. Morphological examinations of peripheral blood and bone marrow have consistently added value to this classication. Multiparameter ow cytometry is the preferred method of immunophenotypic analysis in AML due to the ability to analyze large numbers of cells in a relatively short period with simultaneous recording of information about several antigens for each cell. This observational type of cross Methods and Material : sectional study was carried out in the Pathology department, S.M.S. Medical College, Jaipur, during the period of 2021-2022. A total of 100 patients of all ages and both genders who were newly diagnosed with acute myeloid leukemia were included. Demographic data was noted. The samples were analysed for blast percentage and morphology on leishman stained peripheral blood and bone marrow smears. Immunophenotyping was done by ow cytometry. This included samples of peripheral blood or bone marrow aspirate from patients of morphologically diagnosed AML patients. Results : The most common subtype was AML- M2 and most commonly expressed antigen was CD33 followed by CD13.The mean positivity for CD33 among all AML subtypes was 98.94%. Aberrant expression of CD7 and CD19 were expressed in 5.32% and 4.26% of all cases respectively. There was concordance rate of 94% between morphology and FCM in our study. A combined eva Conclusion : luation of peripheral blood lm, bone marrow aspiration and ow cytometry were done. Flow cytometric analysis must always be performed in conjunction with cytomorphology for denitive diagnosis of acute myeloid leukemia.
Modeling 5G Threat Scenarios for Critical Infrastructure Protection

Fifth-generation cellular networks (5G) are currently being deployed by mobile operators around the globe. 5G is an enabler for many use cases and improves security and privacy over 4G and previous network generations. However, as recent security research has revealed, the 5G standard still has technical security weaknesses for attackers to exploit. In addition, the migration from 4G to 5G systems takes place by first deploying 5G solutions in a non-standalone (NSA) manner, where the first step of the 5G deployment is restricted to the new radio aspects of 5G. At the same time, the control of user equipment is still based on 4G protocols; that is, the core network is still the legacy 4G evolved packet core (EPC) network. As a result, many security vulnerabilities of 4G networks are still present in current 5G deployments. To stimulate the discussion about the security risks in current 5G networks, particularly regarding critical infrastructures, we model possible threats according to the STRIDE threat classification model. We derive a risk matrix based on the likelihood and impact of eleven threat scenarios (TS) that affect the radio access and the network core. We estimate that malware or software vulnerabilities on the 5G base station constitute the most impactful threat scenario, though not the most probable. In contrast, a scenario where compromised cryptographic keys threaten communications between network functions is both highly probable and highly impactful. To improve the 5G security posture, we discuss possible mitigations and security controls. Our analysis is generalizable and does not depend on the specifics of any particular 5G network vendor or operator.
Local Navigation of a Wheeled Mobile Robot as a Carrier of Equipment for Aircraft Defects Monitoring at the Parking Lot

The focus of this work is to solve the high accuracy local navigation problem for heterogeneous group of robots in the process of monitoring the surface of an aircraft (on the basis of solving this problem for a single-axle wheeled module). High accuracy of navigation and orientation solution for the single-axle wheel module is achieved through the application of well-known cloud matching and scan matching algorithms, using the recovered surface of the aircraft and additional information about the model of this surface. Local navigation solution is based on information from the inertial sensors and encoders. Integrated data processing is carried out using the Kalman filter. Simulation results of the single-axle wheeled module motion along the aircraft have confirmed the efficiency of the proposed approach to local navigation.
Knowledge Enhanced Digital Objects: a Data Lake Approach

Open science is gaining traction as a means to bring greater value to the products of research beyond the published manuscript - that is, to make data and software more accessible and usable. The FAIR principles [1] are a significant step forward in defining the characteristics needed of data for it to be shared, used, and reused.
Monte Carlo Tree Search and Machine Learning Techniques on Block Go Programs

Black Go, a game similar to Go, uses Tetris-like pieces and can teach complex Go logic through a fun and easy game process. Monte Carlo tree search can be used to design Block Go programs, and machine learning techniques can train game records and obtain useful game patterns. The paper discusses how to develop a pattern database on Block Go and its performance on Monte Carlo tree search to create a good Block Go program.
Application of Information Technology in Business Strategy in Industry 4.0: Case Study from Indonesia

A rapid change or revolution in the business world is unavoidable. The big change is the shift from a work system that still uses human power to automation technology. The industrial revolution 4.0 is a change that combines cyber technology with automatic technology, which is characterized by combining technology and information into conventional industry. qualitative approach is one type of research that is used to understand something related to the research subject. This research also has a way to make it easier to understand holistically and describe the description in the form of sentence structure. As a result, this sentence structure will take up a lot of research methods that have been previously developed and will be used as a special context naturally. Information technology is a design, implementation, development and management of computer-based information systems in both software and hardware. The industrial revolution 4.0 can be seen that this revolution has a good impact on the company's performance running more effectively and efficiently. high-value technology revolution is further divided into two, namely to this revolution, in the end it does not only affect one sector. the drawback, if the company is blind to the latest technology, it is very unfortunate if the company passes information that should support the company's performance.
Multi-view Hypergraph Contrastive Policy Learning for Conversational Recommendation

Conversational recommendation systems (CRS) aim to interactively acquire user preferences and accordingly recommend items to users. Accurately learning the dynamic user preferences is of crucial importance for CRS. Previous works learn the user preferences with pairwise relations from the interactive conversation and item knowledge, while largely ignoring the fact that factors for a relationship in CRS are multiplex. Specifically, the user likes/dislikes the items that satisfy some attributes (Like/Dislike view). Moreover social influence is another important factor that affects user preference towards the item (Social view), while is largely ignored by previous works in CRS. The user preferences from these three views are inherently different but also correlated as a whole. The user preferences from the same views should be more similar than that from different views. The user preferences from Like View should be similar to Social View while different from Dislike View. To this end, we propose a novel model, namely Multi-view Hypergraph Contrastive Policy Learning (MHCPL). Specifically, MHCPL timely chooses useful social information according to the interactive history and builds a dynamic hypergraph with three types of multiplex relations from different views. The multiplex relations in each view are successively connected according to their generation order in the interactive conversation. A hierarchical hypergraph neural network is proposed to learn user preferences by integrating information of the graphical and sequential structure from the dynamic hypergraph. A cross-view contrastive learning module is proposed to maintain the inherent characteristics and the correlations of user preferences from different views. Extensive experiments conducted on benchmark datasets demonstrate that MHCPL outperforms the state-of-the-art methods.
Alleviating Matching Bias in Marketing Recommendations

In marketing recommendations, the campaign organizers will distribute coupons to users to encourage consumption. In general, a series of strategies are employed to interfere with the coupon distribution process, leading to a growing imbalance between user-coupon interactions, resulting in a bias in the estimation of conversion probabilities. We refer to the estimation bias as the matching bias. In this paper, we explore how to alleviate the matching bias from the causal-effect perspective. We regard the historical distributions of users and coupons over each other as confounders and characterize the matching bias as a confounding effect to reveal and eliminate the spurious correlations between user-coupon representations and conversion probabilities. Then we propose a new training paradigm named De-Matching Bias Recommendation (DMBR) to remove the confounding effects during model training via the backdoor adjustment. We instantiate DMBR on two representative models: DNN and MMOE, and conduct extensive offline and online experiments to demonstrate the effectiveness of our proposed paradigm.
Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation

Two-tower models are a prevalent matching framework for recommendation, which have been widely deployed in industrial applications. The success of two-tower matching attributes to its efficiency in retrieval among a large number of items, since the item tower can be precomputed and used for fast Approximate Nearest Neighbor (ANN) search. However, it suffers two main challenges, including limited feature interaction capability and reduced accuracy in online serving. Existing approaches attempt to design novel late interactions instead of dot products, but they still fail to support complex feature interactions or lose retrieval efficiency. To address these challenges, we propose a new matching paradigm named SparCode, which supports not only sophisticated feature interactions but also efficient retrieval. Specifically, SparCode introduces an all-to-all interaction module to model fine-grained query-item interactions. Besides, we design a discrete code-based sparse inverted index jointly trained with the model to achieve effective and efficient model inference. Extensive experiments have been conducted on open benchmark datasets to demonstrate the superiority of our framework. The results show that SparCode significantly improves the accuracy of candidate item matching while retaining the same level of retrieval efficiency with two-tower models.
The Study of the Language Nature of Dawei (Tavoyan) and Myanmar Language (Burmese) using Statistical Machine Translation

This paper presents to the first evaluation of the nature of machine translation system between Myanmar Language (Burmese) and Dawei (Tavoyan) Language. Now, there are collected 3K sentences based on the Myanmar Language of Face-book data and Myanmar dictionary corpus. Currently 3K pieces of data to test are broken down by hand into phrases. Myanmar Language and Dawei Language are two languages using different basic sentences structure. Dawei Language is low-source languages. So, they have more problems in communication. To solve the language problems of communication, for future work, this paper intends to test a machine translation system between Myanmar Language and Dawei Language. It also presents overview of the state-of-the-art machine translation approaches, structure and classification of machine translation based on the background theory of each approach.
Noun-Noun Metaphor Identification in Myanmar Language

Figurative languages can be found in all areas of human activities, literary, discourse and conversation. Metaphor, which is one of the figurative languages, becomes a problem in natural language processing (NLP). In Myanmar language, there is a gap for specific work of metaphor in NLP research field. This paper presents about the identification of noun-noun metaphor by using Myanmar WordNet (MMWN) and additional resources, such as wordnet2sql, bilingual dictionary and compound noun corpus. In this work, step by step processing of noun-noun metaphor identification are explained in details. Sentences are used for the experiments and categorized in five domains. Semantic relations in the WordNets are used for metaphor identification (MI) and compound noun corpus is used to identified the literal usage. The experimental results of noun pairs extraction and metaphor identification are described in this paper. The precisions are 57% in News, 76% in Novels, 79% in Articles, 81% in Formal and 76% in Conversational sentences. The issues found in metaphor identification and overall discussion about these issues are also presented in this paper.
A Conceptual Framework for ICT Policy Development in Myanmar Education Sector

The development of ICT for Education in Myanmar is crucial for digital education transformation and sustainable development to be in line with the targets of the National Education Policy and Global Sustainable Development Goal (SDG) goal-4 commitment. The paper argues for the compliance of ICT Policy in Myanmar Education sector by measuring ICT access, ICT use and skills, and ICT impact for Myanmar Digital Education Transformation and reaching the national sustainable development education goal. In recognition of Myanmar education reform, this has challenges to reach the objective of National Education Law and SDG-4 to apply the modern technology in education and quality education because ICT for Education policy development and digital technology investments are not ready in Myanmar Education. This paper draws on the international practice of ITU-ICT Development Index (IDI) and SABER ICT policy framework for developing the conceptual framework on National ICT policy development in Myanmar Education sector.
Impact of password management strategies on the trust enhancement in the digital era

Password management is a buzzword in digital services. Information security is the main motive of the companies offering digital services. Adoption of digital services is based on the trust factor to its users. The objective of this paper is 'to analyse the impact of password management strategies on trust enhancement in the digital era'. This study has analysed the primary data, collected through five points Likert scale from 416 respondents from the National Capital Region (NCR) of India using the stepwise method (forward selection) of multiple linear regressions in SPSS version 23. The results revealed that people have the intention to adopt digital services, but lack of trust is a crucial component that deters the users from adopting digital services. The study also revealed that password management strategies have a significant positive impact on trust enhancement, which directly leads to the adoption of digital services.
Measuring Performance Metrics for Blockchain-Based Land Registration Systems: A Mathematical Model Approach

Land registration is a crucial process that regulates the ownership, transfer, and management of real estate properties. The traditional land registration systems have been prone to errors, fraud, and inefficiencies, leading to the emergence of blockchain technology as a potential solution. A blockchain-based land registration system can transform the way real estate properties are managed and traded, providing numerous benefits to stakeholders. The advantages of a blockchain-based land registration system include improved efficiency, transparency, security, inclusivity, and social justice. Performance evaluation metrics are essential tools that can be used to determine the efficiency and effectiveness of a system. The various performance evaluation metrics that can be used to evaluate the performance of a blockchain-based land registration system include user adoption, transaction processing time, security, accuracy, cost reduction, scalability, user satisfaction, and error rate. These metrics are critical in evaluating the performance of a blockchain-based land registration system and ensuring that the system is gaining traction, functioning optimally, and meeting the needs of the users. The implementation of a blockchain-based land registration system can improve the land registration process's speed, efficiency, transparency, and security, providing numerous benefits to all stakeholders involved in the process.
Automated Suspicious Activity Detection from Surveillance Videos

Surveillance has become an integral part of security. For a human to manually watch every event captured on the CCTV camera is unfeasible, especially after the incident has already occurred. Hence, looking manually through the recorded footage for the same incident is time-consuming. An emerging area in the field of autonomous video monitoring is the detection of abnormal activities from video. This paper presents a suspicious activity detection system based on the LRCN model which combines CNN and LSTM as it allows the system to efficiently detect patterns in input data. The major goal of the system is to speed up the investigation process by reducing the amount of time needed for the examination of surveillance footage. According to the findings, the suggested approach can identify suspicious activity in surveillance recordings with an accuracy of up to 80%.
Architecting Autonomous Underwater Vehicles by Adapting Software Product Lines

Software reuse has been one of the most profitable practices in the Software Industry. Different software engineering approaches aim to systematically include reuse as part of their goals. In general, software-product lines as driven principles, component-based software engineering as a methodological approach, and feature models (FMs) for modeling requirements and composing the system’s architecture constitute strong trends for reuse in the development of Information Technology solutions. Technological development has enabled the inclusion of software in electromechanics devices as well as distributed and interoperable cyber-physical systems. Autonomous Underwater Vehicles (AUV) are a particular kind of Cyber-physical system including software, electronic and mechanical components. In this paper, we analyze software product lines for architecting AUV by using FMs. The aim of this paper is to extend FM for handling physical features in the construction of a family of AUVs for accomplishing different AUV missions, and by using a common set of assets.
Imagelytics: A Deep Learning-Based Image Classification Tool to Support Bioassessment

Bioassessment is the process of using living organisms to assess the ecological health of a particular ecosystem. It typically relies on identifying specific organisms that are sensitive to changes in environmental conditions. Benthic macroinvertebrates are mostly used as bioindicators of the ecological status of freshwaters. However, a time-consuming process of species identification that requires high expertise represents one of the key obstacles to reliable bioassessment of aquatic ecosystems. Partial automation of this process using deep learning-based image classification is the goal of an ongoing project AIAQUAMI we are participating in. One of the project deliverables is a standalone desktop application for image classification with visualization and reporting that we developed and open-sourced as Imagelytics. The application relies on a convolutional neural network (CNN) to classify images and the Grad-CAM algorithm to produce heatmaps of the image areas that mostly influenced the network decision. Along with the application code, we also open-sourced scripts that can be used to train CNN on an arbitrary dataset and produce required metadata, so it can be used with Imagelytics. In this paper, we presented technical details about the application and training method that will enable its general use for image classification tasks. As a part of the evaluation, we will show a use case related to species identification of non-biting midges (Diptera: Chironomidae).