Reviewerly/exHarmony huggingface dataset:
TEENAGE MOTHERS’ INVOLVEMENT IN THEIR CHILDREN’S ACQUISITION OF PRE-LITERACY SKILLS: A CASE OF KILIFI COUNTY, KENYA

Early literacy is an integral part in children’s learning. It is crucial in a child’s education cycle since it is the basic foundation on which all further learning is built. Many researchers have argued that since parents are the first teachers that a child encounter, it is important to find out how they can be involved in their children’s acquisition of pre-literacy skills. The two major ways of parental involvement that have been put forward are provision of resources that promote acquisition of pre-literacy skills and involving children in literary rich engagements. Unfortunately, studies on how teenage mothers can be actively involved in promoting their children’s acquisition of pre-literacy skills are scanty. Since teenage mothers are young and most of them depend on their parents for financial support and nurturance, it is important to find out how effective they can get involved in promoting their children’s acquisition of pre-literacy skills. Therefore, this article, highlights findings from a study that sought to establish whether teenage mothers are involved adequately in their children’s acquisition of pre-literacy skills. The study was based on Bronfenbrenner’s Ecological systems theory and employed descriptive survey design. It was carried out in Kilifi County, Kenya and involved a sample of 115 teenage mothers. A questionnaire and interview schedule were utilized to collect data from teenage mothers. Data was analyzed both quantitatively and qualitatively. Findings indicated that teenage mothers are not adequately involved in promoting their children’s acquisition of pre-literacy skills. The authors recommend that the government increases resources allocated to pre-primary schools and pre-primary school teachers should sensitize parents on their role in getting involved in their children’s education to promote pre-literacy skills. Article visualizations:
Gricean Maxims on Student-Teacher Talk in EFL Setting

&lt;p&gt;The student-teacher talk is the foundation that creates interaction in the classroom. It happens when the teacher gives instruction, explains the material or ask for the students’ opinion in the class. However, sometimes one of the participants might not always follow conversational maxims proposed by Grice. The problem is considered as the non-observance of the conversational maxims. This problem might create misunderstanding between students and teacher during the lecturing. This study aims to reveal what kind of the non-observance of the conversational maxims are found in the classroom and to reveal the occurrence of them. This is a pragmatics-discourse analysis with a narrative inquiry as the design. The researcher employs observation checklist and the audio recording as the research instruments. The data collection includes recording the data, listening to the data and then transcribing it into written form. The findings reveal that the only non-observance of the conversational maxims that isn’t found is suspending maxim. There are fourteen times in which the conversational maxims are not fulfilled. The non-observance of the conversational maxims are done by the students only. The lecturer does not take part in the failure of fulfilling conversational maxims. Hopefully, this study is significant to help the teachers and the students to minimize the non-observance of conversational maxims in order to avoid a misunderstanding during the lecturing.&lt;/p&gt;
Development DSL for data dictionary using MPS

Structural System Analysis (SSA) is a complete and accurate method for producing the information system specifications. The main artifacts of this method are a hierarchical set of data flow diagrams, data dictionary, and specification of primitive processes. Silab-MDD approach defines a process to integrate the SSA method in the whole approach by developing domain-specific languages (DSLs) for specifying Data Flow diagrams and Data Dictionaries (DataDDSL). This paper focuses on DataDDSL.
Using w-shingling to Group Similar Genes intoMetabuckets

The Pseudomonas aeruginosa AGI is an opportunistic superbug that affects patients with weakened immune systems due to infectious diseases in eyes, ears, burns or chronic diseases such as cystic flbrosis. This bacterium has genes without information and it is necessary to determine the function they fulfill in order to seek medical treatment that helps infected patients. Using w-shingling, metabuckets are generated that group genes by their similarity. Each metabucket contains genes with or without information, the latter can be associated with information about what they do possess. In addition, a factorial experiment is presented with the results of applying the algorithm to a set of genes obtained from public databases, where the average similarity of the genes grouped in metabuckets reaches 98%.
A study on improving EFL (English as a Foreign Language) students’ fluency by activating procedural memory: Focusing on repetition and memorization activities

This study’s aim is to identify and suggest a method for improving EFL learners’ fluency by activating their procedural memory through memorization and repetition activities. Improving L2 English learners’ fluency requires the frequent use in EFL classes of activities that depend on the procedural memory system, which underlies the implicit learning and processing of a wide range of perceptual-motor and cognitive skills (Ullman & Lovelett, 2016, p. 3). Four college students, all English majors with advanced reading skills, participated in this research. They selected three scenes from the movie 500 Days of Summer (Webb, 2009) and discussed them through the lens of their personal experiences with relationships. Subsequently, participants completed three rounds of memorization and repetition activities with one-week intervals between each round. The participants were required to express the characters’ feelings by memorizing and repeating the selected scenes. The study’s results indicate that all participants were initially reluctant to attempt memorization and repetition activities; however, after trying them, they became more willing to do so. Moreover, their attitudes towards memorizing and repeating became more positive. The participants’ mistakes in memorizing the movie scenes were reduced throughout the research process. Therefore, introducing more memorization and repetition activities, which have the capacity to activate students’ procedural memory, will improve the fluency of L2 learners. (Kookmin University)
Global Planning and Mapping

The modules that deal with high level navigation of the vehicle given the current vehicle location and the destination are grouped in the global planning and mapping section of the decision-making architecture as shown in Fig. 3.4. The proposed approach relies on OSM data to firstly use a global planner to find a route that reach a destination point set through the HMI. After that, it is computed a road corridor in which the vehicle will be able to drive.
Efficient Ciphertext Policy Attribute Based Encryption (ECP-ABE) for Data Deduplication in Cloud Storage

The cloud is the most suitable platform for storing and processing of data. Users are outsourcing data to the cloud to reduce the storage and maintenance cost locally. However, there are challenges to both cloud users as well as to the cloud service provider. The challenge for cloud service providers is to optimize the associated cost for the management and maintenance of uploaded data. The challenge for users is data privacy. To address these issues, encrypted data deduplication schemes proposed, but existing schemes suffer from security weakness and do not support data access control. In this paper, we propose an efficient ciphertext policy attribute-based encryption scheme for deduplication over encrypted data (ECP-ABE) in the cloud storage. Our scheme achieves privacy,deduplication along with access controls. The security and performance analysis demonstrates that our scheme is secure and efficient.
‘문법 지식’의 성격과 교육

This paper examines how grammar knowledge should be understood and interpreted in Korean language education. First, based on the Korean language curriculum, the presentation and understanding of knowledge as educational content is analyzed, while focusing on the type and nature of knowledge. Moreover, the understanding of grammar knowledge in the Korean language curriculum in analyzed, while focusing on the nature of knowledge. Finally, the nature of grammar knowledge and the direction of grammar education is determined.Korean language education highlights the importance of procedural knowledge and focuses on the application of knowledge based on pragmatism. Grammar knowledge has not only been perceived as propositional knowledge, but also the use and application of knowledge has been emphasized. This paper highlights the transformation and connection of propositional and procedural knowledge. This paper also notes that procedural knowledge must not only deal with processes, but also perspective and judgments.
Soil Analysis and Crop Fertility Prediction

Agriculture highly depends upon climate, topography, Soil and biology. In which soil plays an important role. In India farming is done traditionally, farmers do farm traditionally without knowing quality and content of the soil. The existing method of soil testing is done by sending the soil sample to laboratories for testing and these testing is time consuming and costly even many time farmers have to travel long distance for soil testing. There are chance of incorrect report due to human error. To overcome this all things, we proposed a system which gives accurate results for soil analysis by taking the pH value and converting it into Nitrogen (N), Phosphorus (P), Potassium (K) values from the soil. We are using Data mining technique which focuses on large data sets to extract information for prediction of crops and we will also provide suitable fertilizers prediction required for the land with predicted fertilizers price.
Pemetaan Kompetensi Guru Sekolah Dasar dalam Menyusun Artikel Hasil Penelitian yang layak terbit di Jurnal Ilmiah

Penelitian ini betujuan untuk melihat pemetaan kompetensi guru sekolah dasar dalam penyusunan karya tulis ilmiah pada 15 sekolah dengan jumlah 30 guru yang dilibatkan. Penilaian pemetaan kompetensi penyusunan karya tulis mencakup format naskah, lreativitas gagasan, topik yang dikemukakan, data dan sumber informasi, analisis, sintesis dan kesimpulan. Dari hasil penelitian menunjukkan bahwa kelima pemetaan kompetensi penyusunan karya tulis ilmiah masih kurang dari cukup dalam peningkatan kapasitas profesi. Adapun penyebabnya adalah tidak memiliki cukup waktu luang, referensi yang sangat terbatas, penguasaan metode penelitian yang kurang, temuan gagasan masih kurang, dan sumber data belum tulis dengan baik. This study aims to look at the mapping of elementary school teacher competencies in the preparation of scientific papers in 15 schools with a total of 30 teachers involved. The assessment of the competency mapping of writing papers includes the text format, creativity of ideas, the topics raised, data and sources of information, analysis, synthesis and conclusions. The results of the study indicate that the five-competency mapping compilation of scientific papers is still insufficient in increasing professional capacity. The cause is not having enough free time, very limited references, lack of mastery of research methods, findings of ideas are still lacking, and data sources have not been well written.
Research Guides: FSPP: Getting the Most Out of Your Library Resources: Sources for IGO and NGO information

Ford School of Public Policy Workshop: Learn about library resources and how to navigate them
Web Services Architecture Model to Support Distributed Systems

Recently, web services have presented a new and evolving model for constructing the distributed system. The meteoric growth of the Web over the last few years proves the efficacy of using simple protocols over the Internet as the basis for a large number of web services and applications. Web service is a modern technology of web, which can be defined as software applications with a programmatic interface based on Internet protocol. Web services became common in the applications of the web by the help of Universal, Description, Discovery and Integration; Web Service Description Language and Simple Object Access Protocol. The architecture of web services refers to a collection of conceptual components in which common sets of standard can be defined among interoperating components. Nevertheless, the existing Web service's architecture is not impervious to some challenges, such as security problems, and the quality of services. Against this backdrop, the present study will provide an overview of these issues. Therefore, it aims to propose web services architecture model to support distributed system in terms of application and issues.
Domain-Guided Task Decomposition with Self-Training for Detecting Personal Events in Social Media

Mining social media content for tasks such as detecting personal experiences or events, suffer from lexical sparsity, insufficient training data, and inventive lexicons. To reduce the burden of creating extensive labeled data and improve classification performance, we propose to perform these tasks in two steps: 1. Decomposing the task into domain-specific sub-tasks by identifying key concepts, thus utilizing human domain understanding; and 2. Combining the results of learners for each key concept using co-training to reduce the requirements for labeled training data. We empirically show the effectiveness and generality of our approach, Co-Decomp, using three representative social media mining tasks, namely Personal Health Mention detection, Crisis Report detection, and Adverse Drug Reaction monitoring. The experiments show that our model is able to outperform the state-of-the-art text classification models--including those using the recently introduced BERT model--when small amounts of training data are available.
Digital Forensics: 2020 (Seeds are Sown)

Digital Forensics is a scientifically derived and proved method of Identification, Collection , Analysis, Preservation and Presentation of evidence derived from digital exhibits such as  Computers. Storage Media, Mobiles, Network, Cloud, etc. The Investigation Officer on the basis on the analysis report should be able to reconstruct the sequence of events that took place in the digital domain.
Data from: Cross-category adaptation of reflexive social attention

State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Psychology, Chinese Academy of Sciences
A Study on the Continuous Usage Intention of Artificial Intelligence Speaker in Product Service System Perspective

기술 간 융합, 산업 간 융합, 비즈니스 모델 간 융합을 중심으로 하는 4차 산업혁명에서 인공지능 에이전트 서비스는 무형의 서비스와 유형의 제품을 거쳐 소비자들에게 전달되는 것으로 대표적 사례인 인공지능 스피커를 중심으로 소비자들이 어떠한 요인으로 인공지능 에이전트 서비스를 계속해서 사용하는지를 탐구하였다. 실증 분석을 수행하기 위하여 인공지능 에이전트 서비스가 탑재된 제품 중 보급률이 높고, 인공 지능 에이전트 서비스의 중요도가 큰 인공지능 스피커 사용자들을 설문 대상으로 설정하였다. 그리고 품질-만족-충성도 모형과 IS(Information System) 지속사용 모형을 근간으로 연구 모형을 설계하였다. 분석 결과 서비스 품질과 제품 품질은 실용적인 측면인 지각된 유용성과 유희적인 측면인 지각된 즐거움 모두에 긍정적인 영향을 미치는 것으로 나타났다. 또한 지각된 유용성과 지각된 즐거움은 모두 지속사용의도에 영향을 주었다. 반면, 제품 품질과 사용만족, 그리고 사용만족과 지속사용 의도 간의 연구 가설은 통계적으로 유의하지 않은 결과를 얻었다. 결과적으로 인공지능 스피커에 탑재되어 제공되는 인공지능 에이전트 서비스는 서비스, 제품의 품질 양 차원에서 모두 유용성과 즐거움을 결정짓고 이 두 요인은 궁극적으로 지속사용의도까지 결정지었으나, 아직은 사용자가 만족할만한 수준에 도달하지 못한 것으로 짐작할 수 있다. 품질, 기능 측면에서 보다 향상된 발전이 요구됨을 알 수 있다.In the 4th Industrial Revolution, which focuses on convergence between technology, convergence between industries, and convergence between business models, AI agent service is delivered to consumers through intangible services and tangible products. We explored why consumers continue to use AI agent services. In order to perform the empirical analysis, we surveyed AI speaker users with high penetration rate and high importance of AI agent service. The research model was designed based on the quality-satisfaction-loyalty model and the IS (Information System) continuous use model. The results showed that service quality and product quality positively affected both perceived usefulness as a practical aspect and perceived enjoyment as a playful aspect. Also, perceived usefulness and perceived enjoyment all affected the intention to continuous use. On the other hand, the research hypotheses between product quality, use satisfaction, use satisfaction and the intention to continuous use were not statistically significant. As a result, the AI agent service provided on the AI speaker determines the usefulness and enjoyment both in terms of quality of service and product quality. These two factors ultimately determine the intention of continuous use. However, it can be assumed that it has not reached the level. It is necessary to develop further in terms of quality and function.
The Proportion of Comets in the Card Game SET

The beautiful paper “Sets, Planets, and Comets” ([1], in the September 2013 issue of this Journal) poses the following question: In the card game SET, if you draw a collection of nine cards at rand...
Critical Overview of Privacy-Preserving Learning in Vector Autoregressive Models for Energy Forecasting.

Cooperation between different data owners may lead to an improvement in forecast quality - for instance by benefiting from spatial-temporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection questions, said data owners might be unwilling to share their data, which increases the interest in collaborative privacy-preserving forecasting. This paper analyses the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing Vector Autoregressive (VAR) models. The paper also provides mathematical proofs and numerical analysis to evaluate existing privacy-preserving methods, dividing them into three groups: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as a trade-off between privacy and forecasting accuracy, while the original data in iterative model fitting processes, in which intermediate results are shared, can be inferred after some iterations.
한국 국가위기관리체계의 효율성 제고방안 고찰 - 통합방위체계와의 연계를 중심으로 -

Before the 2000s, the concept of national security had been recognized as traditional measures against threats suppressing and defending invasions from the outside. And yet, as shown in the war on terror or the civil war in Syria, terrorism, civil wars, cyber/psychological warfare, natural/human disasters have developed complexly, rather than military aggression since September 11 Attacks. With a conceptual change from traditional security to comprehensive one, security crisis and disaster/calamity are mixed in the concept of Korea’s national crisis management, and terms are used in confusion. This study would pay attention to the insufficient establishment of the areas and levels of the national crisis management.Related laws have been enacted but as individual laws having to survive themselves while there is no standards act, so it is hard to expect efficiency. Thus, There should be the entire nation’s empathy for the needs of the resolution of the vulnerability of the legal system that Korea’s crisis-related laws should receive the approval of the National Assembly at the time of war, the unification of the concept of national emergency/crisis management and the reestablishment of the reorganization of related groups and organizations. Therefore, this study would intensively present measures for connecting and sharing the integrated defense system based on the United Defense Act with the National Crisis Management System.This study set ‘crisis management system’ as an independent variable; ‘efficiency’ as a dependent variable; and ‘basic theme of concept’ and ‘statute system’, ‘organization & structure’ as parameters. In spite of many related studies, very regretfully, developmental changes are delayed. And yet, realistic limitations should not be ignored. However, in enacting national crisis-related laws or organizing a system (organization and group), it would be necessary to proceed in a direction, helpful for the nation’s existence and profit, national stability and economic prosperity.
Improving Deep Unconstrained Facial Recognition by Data Augmentation

Facial recognition technology has emerged as an attractive solution for many of today’s needs in identification and identity verification. In recent years, the use of deep learning techniques and convolutional neural networks in particular has led to high-performance systems with near-human recognition capabilities. In general, these models are trained and evaluated on image datasets that do not sufficiently consider the lighting conditions of a real environment. However, in many practical applications the lighting is uncontrolled, which may seriously affect the performance of these systems. In this chapter, we propose a data augmentation method to achieve a model that is robust to variations in brightness. The training dataset is augmented by generating 3D faces from 2D images in the original dataset, followed by a Lambertian reflectance lighting variation that simulates the lighting variations that occur in real environments. The approach is evaluated on the YaleB and ORL datasets, with respective accuracy gains of 17.77% and 9%, compared to the model trained without data augmentation.
Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction

Classical monocular Simultaneous Localization And Mapping (SLAM) and the recently emerging convolutional neural networks (CNNs) for monocular depth prediction represent two largely disjoint approaches towards building a 3D map of the surrounding environment. In this paper, we demonstrate that the coupling of these two by leveraging the strengths of each mitigates the other's shortcomings. Specifically, we propose a joint narrow and wide baseline based self-improving framework, where on the one hand the CNN-predicted depth is leveraged to perform pseudo RGB-D feature-based SLAM, leading to better accuracy and robustness than the monocular RGB SLAM baseline. On the other hand, the bundle-adjusted 3D scene structures and camera poses from the more principled geometric SLAM are injected back into the depth network through novel wide baseline losses proposed for improving the depth prediction network, which then continues to contribute towards better pose and 3D structure estimation in the next iteration. We emphasize that our framework only requires unlabeled monocular videos in both training and inference stages, and yet is able to outperform state-of-the-art self-supervised monocular and stereo depth prediction networks (e.g, Monodepth2) and feature-based monocular SLAM system (i.e, ORB-SLAM). Extensive experiments on KITTI and TUM RGB-D datasets verify the superiority of our self-improving geometry-CNN framework.