Reviewerly/exHarmony huggingface dataset:
Environment awareness through Miniature landscape of Mud-Fort Art: An Observation

The majority of the States in India have their own rich culture and heritage.The richness of their identity is preserved in various cultures and art.In Maharashtra, the trend of replicating the miniature Forts is the one preserved in play activities by school children.Nowadays such activity is recognized as healthy practice on social media.Such informal art practices are only appreciated by the locals but its close similarities with the concept of Land Art sculpted in miniature forms are to be studied for the awareness of the ecosystem.Land Art and Earth art emerges as the new concept of the art movement in 1960 and 1970 to sensitize environmental study (Marcia and John,).The majority of the areas near the forts have an environmental setting along with its beauty that needs to be conserved and sensitized.There are 350 Forts in Maharashtra that symbolize the liberal society and are part of their celebration (https://memumbai.com/fort-making-workshop/6/10/2017).To restore the aesthetic beauty of the fort and its environment the build-up model of the same can in developing an exclusive action plan for its conservation.The Mud fort activities of the children have the potential to convey major issues in miniature forms related to the ecosystem.The process of Making Mud Forts too requires lots of preparation in terms of proportionate soil and water, consistency of the mixture, and supportive material borrowed from recycling waste materials (Ashwin Khan, 2017).In this search, the aim was to identify and conserve authentication of the derived information from the tendency to observe natural form and associate it with innovative concepts of Modern Art.Data collected from the various workshop records provides training in sculpting the basic structure of Mud Forts in different places in Maharashtra was analyzed.The observation made on the artwork of a trained and untrained population was studied based on their awareness of representing the features of the replicated monument.The alternate technology used in this process was examined.The study found that the trained population was more advance, focused on presenting built-up space, and also enjoy the desire to learn about such environmental issues derived from land art.This study encourages the use of traditional methods to introspect the major issues, by using the sustainable model to find the solution to the ecosystem.It formulates that informal education in art can help in collaborative work on a project with true environment art.
DATA MINING USING CRISP-DM PROCESS FRAMEWORK ON OFFICIAL STATISTICS: A CASE STUDY OF EAST JAVA PROVINCE

Data mining on official statistics becomes a study interest, as it offers an opportunity to reveal hidden patterns within the data. This study investigates the data mining process's appropriateness using the CRISP-DM method to a secondary-quantitative data analysis and to investigate hidden information revealed from data mining on official statistics. Data is collected from the East Java BPS website, and the unit of analysis is regency/municipality. Five macro development indicators (Human Development Index, Gross Regional Domestic Products, poverty rate, Gini Ratio, open unemployment rate) are selected as analysis variables. Workflows of data analysis are designed using Knime software. This study shows the usefulness of the CRISP-DM method for secondary research because it specifies standardized stages for analyzing secondary data and improves the secondary analysis rigor. Furthermore, the clustering technique classifies regencies/municipalities into three clusters. One of the clusters has desirable indicator levels: high Human Development Index - high Gross Regional Domestic Products - low poverty rate, together with undesirable ones: high Gini Ratio - high open unemployment rate. This result indicates that a regency/municipality might not achieve an ideal condition of the five macro development indicators. Some indicators such as the open unemployment rate might be an inevitable impact. This research adds to the literature on development economics studies, particularly on the application of data mining, the CRISP-DM method, and Knime software to official statistics.
The Influence of the Implementation of the Regional Financial Accounting System and the Role of the Internal Auditor on the Quality of Financial Reports with the Internal Control System as a Moderating Variable

This article aims to examine the effect of applying the regional financial accounting system and the role of internal auditors on the quality of financial reports.In this case, the internal control system is used as a moderating variable.This article also aims to determine the effect of the independent variable on the dependent variable to determine the effect on the dependent variable.In the moderating variable, there are several factors that will strengthen or weaken the influence of the independent on the dependent variable.In this study, the test was carried out using moderated regression analysis using 75 respondents in 25 Regional Office Organizations and Jambi Provincial Agencies as samples.This article uses differential semantics as a measurement scale, with an interval scale score of 1 to 7 points.The type of information used in this examination is essential information, including quantitative data.From this study, it was found that there was a lack of risk assessment carried out, such as leaders who did not carry out risk evaluations that were completed, for example, leaders who did not check bookkeeping records, cash and products.There was no adequate separation of duties from the control environment, as evidenced by getting a low score.The interaction variable between the regional financial accounting accounting system and the control system is calculated to be -1.224 and a significance value of 0.226 > 0.05 (α = 5%) H4a is rejected.The interaction variable between the role of internal auditors and the internal control system has a t count of 1.461 and a significance value of 0.149 > 0.05 (α = 5%) H4b is rejected.
Automated Zebrafish Phenotype Pattern Recognition: 6 Years Ago, and Now

The article assesses the developments in automated phenotype pattern recognition: Potential spikes in classification performance, even when facing the common small-scale biomedical data set, and as a reader, you will find out about changes in the development effort and complexity for researchers and practitioners. After reading, you will be aware of the benefits and unreasonable effectiveness and ease of use of an automated end-to-end deep learning pipeline for classification tasks of biomedical perception systems.
A Safety Assurable Human-Inspired Perception Architecture

Although artificial intelligence-based perception (AIP) using deep neural networks (DNN) has achieved near human level performance, its well-known limitations are obstacles to the safety assurance needed in autonomous applications. These include vulnerability to adversarial inputs, inability to handle novel inputs and non-interpretability. While research in addressing these limitations is active, in this paper, we argue that a fundamentally different approach is needed to address them. Inspired by dual process models of human cognition, where Type 1 thinking is fast and non-conscious while Type 2 thinking is slow and based on conscious reasoning, we propose a dual process architecture for safe AIP. We review research on how humans address the simplest non-trivial perception problem, image classification, and sketch a corresponding AIP architecture for this task. We argue that this architecture can provide a systematic way of addressing the limitations of AIP using DNNs and an approach to assurance of human-level performance and beyond. We conclude by discussing what components of the architecture may already be addressed by existing work and what remains future work.
Research and Application of Intelligent Inspection System

In view of the wide distribution and large number of unattended stations in the gas field, the problems of large workload, low work efficiency and high driving safety troubles the inspection personals when they inspect the unattended stations by driving a car every day. Under this background, a intelligent inspection system which uses the technology of “video tour + image recognition” is researched and applied in gas field. Based on the neural network algorithm, A set of image recognition algorithm which is suitable for on-site production monitoring meters is formed, and high-definition industrial video is used to inspect and identify on-site parameters automatically. Combined with these tow technologies, the intelligent inspection system is researched successfully, which can replace workers for executing inspection tasks, finding abnormal parameters and generating inspection reports The application results show that the recognition accuracy of intelligent inspection system is more than 95%, the inspection workload of staff can be reduced by 85%, and the inspection efficiency can be increased by 80%. At the same time, the intelligent inspection system effectively reduces the staff's stay time in high-risk areas and releases the internal human resources, which provides a reliable and effective technical means for the safety control of unattended stations.
Urban Design Initiatives in Drought-prone Areas dealing increasing Water Demand as Pandemic Covid-19 Impact

The Covid-19 pandemic becomes a primary issue in urban development that requires several supporting facilities and infrastructures to handle. The adequate water supply to ensure the washing hand and bathing as part of health protocol implementation is crucial for drought-prone areas. Meanwhile, the government strategies on drought handling by assisting the water trucks are considered a temporary solution. Hence, this study examines urban design initiatives to deal with Covid-19 impact on the water sector, particularly in Semarang drought-prone areas. The research used a quantitative method by overlaying physical variables such as the soil type, slope, and water supply coverage to determine the drought level map. Afterward, the drought level compared with the population growth and community's economic capacity to describe the area's vulnerability. Besides, the existing drought handling strategies were analyzed to examine the impact. This study utilized secondary data such as the government institution websites as well as Google maps. The analysis results point out that most of drought-prone areas did not serve by government water service and relied on the community-based water supply system. In addition, 25% of the inhabitants are categorized as a low-income community, so that they prefer to take water from the river or spring than have to expend more to buy the water. Meanwhile, the existing initiatives on drought handling did not have significant impacts because of a lack of knowledge and unclear regulation. The Water Sensitive Urban Design (WSUD) concept has already been adopted, yet the implementation was still small-scale and unsustained. Therefore, it is necessary to consider the physical, socio-economic, and political aspects in implementing the drought handling initiatives.
Mutil-level Local Alignment and Semantic Matching Network for Image-Text Retrieval

Image-text retrieval is a challenging task in the field of vision and language. The existing methods mainly compute the similarity of image-text pairs by the alignment between image regions and text words. Although these methods based on fine-grained local features achieve good results, these methods only explore the correspondence between salient objects and ignore the deep semantic information expressed by the whole image and text. Thus, we propose a novel multi-level local alignment and semantic matching network (MLASM) that introduces a multi-level semantic matching module after local alignment. This module supplies our model with more sufficient semantic information to understand the complex correlations between images and texts. Experiment results on two benchmark datasets Flickr30K and MS-COCO show that our MLASM achieves state-of-the-art performance.
Training 1-Bit Networks on a Sphere: A Geometric Approach

Weight binarization offers a promising alternative towards building highly efficient Deep Neural Networks (DNNs) that can be deployed in low-power, constrained devices. However, given their discrete nature, training 1-bit DNNs is not a straightforward or uniquely defined process and several strategies have been proposed to address this issue yielding every time closer performance to their full-precision counterparts. In this paper we analyze 1-bit DNNs from a differential geometry perspective. We part from noticing that for a given model with d binary weights, all possible weight configurations lie on a sphere of radius $$\sqrt{d}$$ . Along with the traditional training procedure based on the Straight Through Estimator (STE), we leverage concepts from the fields of Riemannian optimization to constrain the search space to spherical manifolds, a subset of Riemannian manifolds. Our approach offers a principled solution; nevertheless, in practice we found that simply constraining the norm of the underlying auxiliary network works just as effectively. Additionally, we observe that by enforcing a unit norm on the network parameters, our network explores a space of well-conditioned matrices. Complementary to our approach, we additionally define an angle based regularization that guides the auxiliary space exploration. We binarize a ResNet architecture in order to demonstrate the effectiveness of our approach in the tasks of image classification on the CIFAR-100 and ImageNet datasets.
A Highly Optimized GPU Batched Elasticnet Solver (BENS) with Application to Real- Time Keypoint Detection for Image Retrieval

In this paper, we present a highly optimized GPU batched elastic-net solver (BENS) with application to real-time key-point detection for image retrieval. BENS was optimized to perform hundreds of thousands of small elastic-net fits by batching each fit from specific steps in the elastic-net computation into a large matrix multiplication which can be computed efficiently using the CUBLAS library. The main motivation for BENS was a real-time implementation of the Sparse-Coding Key-point detector (SCK) algorithm which has reaching applications in science, engineering, social science and medicine. When BENS was applied to accelerate SCK, we have achieved a 232x speed up compared to the original CPU implementation of SCK. To demonstrate the newly accelerated SCK algorithm, we conducted an Bo Vw based image retrieval experiment using SCK as the key-point detector.
Nonprofits now have new choices for web URL domains

Public Interest Registry, the nonprofit organization that administers the .org website domain, has announced an expansion of domains available for philanthropic organizations, enabling them to choose from top‐level domains such as .charity, .foundation and .gives, depending on their organization's focus.
Speaking Performance and Grammatical Competence Across Cognitive Learning Styles and Strategy Inventory for Language Learning

&lt;p&gt;&lt;strong&gt;Abstract: &lt;/strong&gt;This study examines the correlation between speaking performance and grammatical competence, particularly in the EFL context, by including cognitive learning styles and the Strategy Inventory for Language Learning (SILL). The correlational study is employed to analyze the scores of argumentative speaking and written grammar tests of 30 Economics students at STIE Malangkuceswara Malang. It is found that there is no significant correlation between speaking performance and grammatical competence. However, FI students outperformed in speaking, and FD students surpassed in grammar. In fact, those who scored higher on both tests used more SILLs than those who scored lower.&lt;/p&gt;&lt;strong&gt;Abstrak:&lt;/strong&gt;&lt;em&gt; &lt;/em&gt;Penelitian ini merupakan upaya untuk melihat hubungan antara kemampuan berbicara dan kompetensi gramatikal, khususnya dalam konteks EFL dengan memasukkan gaya belajar kognitif dan Strategy Inventory for Language Learning (SILL). Studi korelasional digunakan untuk menganalisis nilai tes berbicara argumentatif dan tata bahasa tertulis pada 30 mahasiswa Ilmu Ekonomi STIE Malangkuceswara Malang. Diketahui bahwa tidak ada korelasi yang signifikan antara kinerja berbicara dan kompetensi tata bahasa. Namun, siswa FI unggul dalam berbicara dan siswa FD melampaui dalam tata bahasa. Faktanya, mereka yang mendapat skor lebih tinggi pada kedua tes menggunakan lebih banyak SILL dibandingkan mereka yang mendapat skor lebih rendah.
Pengembangan Media Blog Berbantu Buku Tugas terhadap Kegitan Literasi Dasar Kelas IV Sekolah Dasar

&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; This study aims to produce a book-based blog as a support for basic literacy in grade IV of elementary school that meets feasibility in terms of validity, practicality, attractiveness, and effectiveness. This research is a research &amp;amp; development research with a 4D model, namely define, design, development, disseminate. The results showed the validity level was 90.86%, the practicality level was 81.90%, the attractiveness level was 90.15%, the effectiveness of the product t&amp;gt;t table was 3.91&amp;gt;1.734 and there was an increase in the average pretest 23 to the average posttest 41,77. Comments and suggestions from experts and teachers and students are considered in improving the product so that the product is suitable for use.&lt;/p&gt;&lt;strong&gt;Abstrak:&lt;/strong&gt;&lt;em&gt; &lt;/em&gt;Penelitian ini bertujuan untuk menghasilkan media blog berbasis buku tugas sebagai penunjang literasi dasar pada kelas IV Sekolah Dasar yang memenuhi kelayakan dari segi kevalidan, kepraktisan, kemenarikan, dan keefektifan. Penelitian ini merupakan penelitian &lt;em&gt;research &amp;amp; development &lt;/em&gt;dengan model 4D, yaitu &lt;em&gt;define, design, development, disemminate&lt;/em&gt;. Hasil penelitian menunjukkan tingkat kevalidan 90,86%,tingkat kepraktisan 81,90%, tingkat kemenarikan 90,15%, keefektifan &lt;em&gt;t &lt;/em&gt;produk &amp;gt; &lt;em&gt;t &lt;/em&gt;tabel yaitu 3,91&amp;gt;1,734 dan terjadi peningkatan nilai rata-rata &lt;em&gt;pretest &lt;/em&gt;23 menjadi nilai rata-rata &lt;em&gt;posttest&lt;/em&gt; 41,77. Komentar dan saran dari para ahli dan guru serta siswa menjadi pertimbangan dalam perbaikan produk sehingga produk menjadi layak digunakan.
Modul Pencemaran Lingkungan Berbasis Biology Environment Technology Society untuk Matakuliah Dasar Ilmu Lingkungan

&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; The purpose of this study was to produce Environment Pollution valid and practice modul based on Biology Environment Technology Society (BETS) of the material, the validity of teaching materials, and practicality. This developmental research adopted the ADDIE development model. The result of material expert validation was 100% with a very valid category. The results of the validation of teaching material experts amounted to 99.50% with a very valid category. The results of the assessment by field practitioners amounted to 90.98% with a very practical category. The results of the assessment of usage by students amounted to 90.35% with a very practical category. Based on these results it can be concluded that the BETS-based environmental pollution module that has been produced has been appropriate to be used as teaching material for the Environmental Science Basic course.&lt;/p&gt;&lt;strong&gt;Abstrak:&lt;/strong&gt;&lt;em&gt; &lt;/em&gt;Penelitian ini bertujuan untuk menghasilkan modul pencemaran lingkungan berbasis BETS (&lt;em&gt;Biology Environment Technology Society&lt;/em&gt;) yang valid dan praktis. Model pengembangan yang digunakan yaitu model pengembangan ADDIE. Hasil validasi ahli materi sebesar 100% dengan kategori sangat valid. Hasil validasi ahli bahan ajar sebesar 99,50% dengan kategori sangat valid. Hasil validasi oleh praktisi lapangan sebesar 90,98% dengan kategori sangat praktis. Hasil penilaian penggunaan oleh mahasiswa sebesar 90,35% dengan kategori sangat praktis. Kesimpulan penelitian yaitu modul pencemaran lingkungan berbasis BETS (&lt;em&gt;Biology Environment Technology Society&lt;/em&gt;) yang dihasilkan telah layak digunakan sebagai bahan ajar untuk matakuliah Dasar Ilmu Lingkungan.
Investigating EFL Teachers’ Difficulties in Connected Speech

&lt;p class="Abstrak"&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; English teachers must have adequtae knowledge of pronunciation like phonetics and phonology. They also must be able to perform a good speaking performance as they must be linguistic models for their students in the class. This study investiagted several phonological aspects of connected speech such as linking, elision, and assimilation by EFL English teachers. The result revealed that assimilation was the most difficult phonological aspects of connected speech rather than linking or elision. The major difference between English and native language of the teachers could be the main reason of their contrains related to connected speech aspects.&lt;/p&gt;&lt;strong&gt;Abstrak:&lt;/strong&gt; Guru bahasa Inggris harus memiliki pengetahuan yang memadai tentang pengucapan seperti fonetik dan fonologi. Mereka juga harus mampu berbicara dengan baik karena mereka harus menjadi panutan bagi siswa mereka di kelas. Penelitian ini menyelidiki beberapa aspek fonologis dari &lt;em&gt;connected speech&lt;/em&gt;, seperti &lt;em&gt;linking, elision&lt;/em&gt;, dan &lt;em&gt;assimilation &lt;/em&gt;oleh guru bahasa Inggris. Hasil penelitian menunjukkan bahwa asimilasi merupakan aspek fonologis yang paling sulit daripada linking atau elision. Perbedaan utama antara bahasa Inggris dan bahasa pertama guru bisa menjadi alasan utama kendala yang dialami mereka terkait dengan aspek tersebut.
A Joint Model for Detecting Causal Sentences and Cause-Effect Relations from Text

Text documents are rich repositories of causal knowledge. While journal publications typically contain analytical explanations of observations on the basis of scientific experiments conducted by researchers, analyst reports, News articles or even consumer generated text contain not only viewpoints of authors, but often contain causal explanations for those viewpoints. As interest in data science shifts towards understanding causality rather than mere correlations, there is also a surging interest in extracting causal constructs from text to provide augmented information for better decision making. Causality extraction from text is viewed as a relation extraction problem which requires identification of causal sentences as well as detection of cause and effect clauses separately. In this paper, we present a joint model for causal sentence classification and extraction of cause and effect clauses, using a sequence-labeling architecture cascaded with fine-tuned Bidirectional Encoder Representations from Transformers (BERT) language model. The cause and effect clauses are further processed to identify named entities and build a causal graph using domain constraints. We have done multiple experiments to assess the generalizability of the model. It is observed that when fine-tuned with sentences from a mixed corpus, and further trained to solve both the tasks correctly, the model learns the nuances of expressing causality independent of the domain. The proposed model has been evaluated against multiple state-of-the-art models proposed in literature and found to outperform them all.