Reviewerly/exHarmony huggingface dataset:
Rancang Bangun E-Commerce Berbasis Web untuk UMKM Batik

In the sales process carried out by batik craftsmen who are in Trusmi village, namely still selling their batik cloth to the sorum of batik sellers, so that the craftsmen take profits from selling their batik cloth only slightly, because the batik cloth will be resold by the sorum of batik sellers. In terms of application development, the author uses the Software Development Life Cycle (SLDC) model with an approach using the waterfall method, while the stages use the waterfall method, namely communication, planning, modeling, construction, and deployment. Therefore, the author makes an application for selling batik cloth that aims to break the chain of batik cloth sales, so that craftsmen can get greater profits because they directly sell their products to consumers without going through a batik seller sorum. The final result obtained in this study is an application for selling batik cloth which is expected to help batik craftsmen in Trusmi village in selling their batik cloth directly to consumers without having to go through the center of batik sellers.&#x0D; In the sales process carried out by batik craftsmen who are in Trusmi village, namely still selling their batik cloth to the sorum of batik sellers, so that the craftsmen take profits from selling their batik cloth only slightly, because the batik cloth will be resold by the sorum of batik sellers. In terms of application development, the author uses the Software Development Life Cycle (SLDC) model with an approach using the waterfall method, while the stages use the waterfall method, namely communication, planning, modeling, construction, and deployment. Therefore, the author makes an application for selling batik cloth that aims to break the chain of batik cloth sales, so that craftsmen can get greater profits because they directly sell their products to consumers without going through a batik seller sorum. The final result obtained in this study is an application for selling batik cloth which is expected to help batik craftsmen in Trusmi village in selling their batik cloth directly to consumers without having to go through the center of batik sellers.
Effectiveness of video-based teaching program to assess the knowledge of young mothers in identifying developmental milestones in Delhi NCR, India

In this study, a video-based teaching program was used to assess the knowledge of young mothers in identifying developmental milestones. The purpose of this study was (1) to assess the effectiveness of video-based program, (2) to assess the knowledge of young mothers in identifying developmental milestones. Methods. Self-structured tool on knowledge regarding developmental milestones was used in this study. Each mother participated in the questionnaire. In the final position, the questionnaire score was obtained and the results was evaluated using inferential and descriptive statistics. Results. A mean value of 4.94 pre-test and a mean value of 11.17 post-test were obtained for all the subjects. There was a strong correlation between the pre-test and post-test measurements (df=. 6.23). There was a difference between the pre/posttest St. Deviation of 2.37 and 1.45, with t value of 22.70. The results from the analysis shows that after the administration of the VBTP which was conducted online, there was a significant increase in the knowledge regarding DM among young mothers.
Efficiency Modeling and Optimization of Interactive Scenarios Cultivated by Students' Core Values Under the Background of Chain Data Mining Algorithm

The layered chain data mining algorithm based on the ES algorithm idea, the periodic pattern incremental mining algorithm, inherits the storage characteristics of the layered chain graph and the advantages of separating frequent patterns, but the layered chain graph also has limitations. In the simulation of incremental mining algorithm application of layered chain graph. The core values lead the evolution of interactive scene efficiency modeling and optimization; on the other hand, the evolution of interactive scene efficiency modeling and optimization is an intuitive reflection of the changes in core values. The algorithm can reduce the number of times of scanning the transaction database and the number of candidate itemsets generated, thereby reducing the time to generate the largest frequent itemsets. The experimental results show that the algorithm improves the computing efficiency by 7.5%.
Basic Parametric Models and a Statistical Perspective on Learning

This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.
An integrated approach to the preparation and conduct of a single online lecture

The purpose of the study is to present the methodology of organizing and conducting a single online lecture as part of educational work for students of a technical university. The scientific novelty consists in the description of an integrated approach to the methodology of developing a single online lecture within the framework of educational work and the presentation of material to an extensive remote audience. As a result, the problem of insufficient coverage of the issue of the implementation of educational work of the university in the format of distance learning was identified; the possibility of implementing the concept of education in the conditions of remote learning through working with a large remote audience, which requires huge training from the teacher as the organizer of the event; the need for an integrated approach in understanding and presenting the material, as well as the choice of forms and methods of interaction with a remote audience when organizing and conducting an educational event in a remote format was noted.
Research on Key Technologies of Street View Video Privacy Information Processing

Abstract As image and video data are recorded, stored or applied worldwide, data privacy protection is receiving increasing attention. Before data application, it is necessary to hide the information that can be personally identified in the video, for example, the human face that can be directly located to the personal identity, the license plate number that can be indirectly located to the personal identity and other data, while retaining the appropriate degree of data availability. Taking the actually collected European road street view video as an example, and taking the face and license plate as the privacy information, this paper proposes a method to automatically detect the face and license plate area in the street view video based on the YOLO5s model, and hide the detected object area with the Gaussian blur in the image processing technology. The experiment shows that the method is scientific, efficient and feasible.
Cybercrime in Africa

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 59, Issue 4 p. 23845A-23845B Policy and Practice Cybercrime in Africa First published: 02 June 2022 https://doi.org/10.1111/j.1467-6346.2022.10576.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume59, Issue4June 2022Pages 23845A-23845B RelatedInformation
Memaksimalkan peran perpustakaan sebagai sumber belajar di Sekolah Menengah Atas Muhammadiyah 1 Ponorogo

Introduction. This research analyses efforts made by the library of Muhammadiyah Senior High School 1 Ponorogo in maximising the role of library as a source of learning as issued by the International Federation of Library Associations and Institution. &#x0D; Data Collection Methods. This paper employed a qualitative descriptive approach through participant observations and structured interviews. &#x0D; Data analysis. Data from interview transcripts, observation sheets and documentations were analysed by conducting the process of data display, data reduction and conclusion drawing. The analysis process also considered several supporting literature in the form of books, scientific journals, conference proceeding.&#x0D; Results and discussion. The roles of the library as a learning source are demonstrated by several activities to improve users’ skills such as providing resource capabilities and supporting critical thinking, literacy, and personal and interpersonal development.&#x0D; Conclusion and suggestions: In serving its responsibility as a learning resource, the library at SMA Muhammadiyah 1 Ponorogo did not fully comply with the International Federation of Library Associations and Institutions' standard requirements. Libraries are limited in their ability to fulfill information needs, but they have make little effort to explain that information to users.
Optimization of Library Decision-Making Mechanism under Big Data Environment: Based on Environmental Sensing Technology

This paper designs a scheme based on the extraction pro designing and improving the library decision support system based on the big data and environmental sensing technology perspective can effectively improve the scientific nature of library decision-making and promote the modernization of library governance. This article summarizes the development process of the library decision support system through the analysis of the library decision-making environment. The library decision-making mechanism based on big data emphasizes the realization of sharing, openness and innovation value, which will inevitably lead to the establishment of a flat decision-making structure. Promote the integration of collective wisdom and data rationality in the decision-making process, thereby breaking the limitations of traditional decision-making and establishing a democratic library decision-making mechanism.
Quantized neural networks for modulation recognition

The electromagnetic environment is becoming increasing cluttered and complex, therefore the need to perform operations such as modulation recognition on wide bandwidth signals in real time is becoming crucial. Deep learning solutions have shown state-of-the-art performance for modulation recognition, however they are computationally expensive. In this paper we address this demand on resources by applying Quantized Neural Networks (QNNs) to the problem of modulation recognition. We demonstrate that QNN implementations of state-of-the-art Neural Networks (NNs) with different bit widths achieve varying levels of performance, on both our own 10 class dataset (up to 80% classification accuracy) and the open source 24 class DeepSig dataset (up to 75% classification accuracy at 10dB signal-to-noise ratio (SNR)). We then explore the accuracy-hardware trade-off of scaling NN size and bits when using QNNs. It was found that scaling the NN size had similar accuracy-hardware trade-offs as scaling the NN bits. However, 1-bit NNs had an unfavorable trade-off, therefore suggestions are made on how to improve the 1-bit NN architecture and training procedure. To demonstrate the potential benefits of low-precision quantized NNs and encourage further work we propose novel optimizations for 1bit and 2bit QNNs in FPGAs and ASICs that eliminate the need for weights memory and simplify the hardware required to implement a QNN. This provides further savings in hardware, allowing QNNs to provide favorable accuracy-hardware trade-offs, and supports the implementation of significantly larger NNs with a throughput of 100s of millions of inferences per second.
Asymmetric kernels for dead pixel replacement and cluster masking

MWIR FPAs often contain a non-negligible number of dead pixels, and even clusters of pixels. In addition to the undesirable cosmetic effect that these flaws have on the output image, it can also be detrimental to downstream image exploitation efforts, including target detection and tracking algorithms. It is therefore necessary to mask such defects as early as possible, before it is exacerbated by the imaging pipeline processes, especially sharpening filters and contrast stretching, which are commonly used. This paper presents the results of an investigation into several dead-pixel replacement schemes of varying complexity, starting with simple replacement by the previous neighbor to interpolation using kernels of varying shapes and domain sizes. These are evaluated for accuracy, but also for their cluster-handling performance, latency impact, and suitability for real-time implementation on resource-constrained FPGA matrices. It is shown that asymmetric predictive kernels, optimized using neural nets or genetic algorithms, can offer significant improvements over naïve last-good-neighbor replacement, while affording improved cluster-handling capability, low FPGA resource requirements, and incurring minimal and even zero latency.
The Violining Body in AnthÈmes II by Pierre Boulez

This chapter presents a concept of the violining body as an enacted, multifaceted, embodied violin- specific musicking act (Small 1998) which incorporates the complexity of idiosyncratic challenges and affordances at play in the context of mixed music performance. Despite a wide scrutiny of mixed music discourse in recent years, the body in the context of mixed music performance has been largely relegated to the periphery of discussion. By looking at the example of Anthèmes II (1994) by Pierre Boulez, this chapter explores the enacted violin-specific body-instrument-environment ecosystemic cohesion as a knowledge-producing phenomenon, where in the process of mediatised transformation of the acoustic sound, the violinist is enacted through a continuous process of adaptation to the environment and centuries-old performance traditions, where innovative electronic technologies are wedded together. Specifically, the idiosyncrasies of sound projection and dynamic differentiation, haptic feedback, and accents are problematised in the context of ecosystemic electrified-violining-body approach to the practice.
Role of Knowledge Graphs in Analyzing Epidemics and Health Disasters

Medical healthcare and epidemic outbreak data are heavily interconnected and unstructured type of dataset. It is very difficult to represent and manage the large number of relationships between data items using traditional database management systems. Therefore, it is required to find a new framework to handle these datasets. A knowledge graph is a data analytics tool which can be used to handle a connection-rich type of dataset. It is an arrangement of entities (real-world objects or concepts) in the form of a network where these entities are linked to each other; therefore, knowledge graph is an efficient choice for the representation and analysis of linked data. This chapter provides the benefits that can be derived from a graph-based approach for analyzing dynamic, heterogeneous, and unstructured data applications in epidemiology.
Nurture Your Leadership Skills

The Major Gifts ReportVolume 24, Issue 7 p. 7-7 Leadership Roles and Responsibilities Nurture Your Leadership Skills Daniel Lindley, Daniel LindleySearch for more papers by this author Daniel Lindley, Daniel LindleySearch for more papers by this author First published: 30 May 2022 https://doi.org/10.1002/mgr.31952Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume24, Issue7July 2022Pages 7-7 RelatedInformation
Perbandingan Algoritma Regresi Linier dan Regresi Random Forest Dalam Memprediksi Kasus Positif Covid-19

The world is in an uproar by a virus that was first reported in Wuhan, China. This virus is reported to have a very wide spread in a short time, so that it spread throughout the world. The World Health Organization (WHO) has designated Corona Virus Disease 2019 as a global pandemic. In a short time the Covid 19 virus immediately spread to all corners of the world, including Indonesia. As a result of the spread of COVID-19 which soared, there was a stir in the community. This situation affects many sectors of the nation's life. Therefore, the researchers conducted this study to find out how much data were affected, died, and recovered from Covid 19 and how to analyze linear regression and forest random regression. The results of the author's research resulted in an RMSE value of 4310,952 MAPE 19.9 and an accuracy rate of 91.4% in the Linear Regression algorithm while the Random Forest algorithm produced an RMSE value of 4342,481, MAPE 16.2 and an accuracy rate of 91.3%. So it can be said that in this research linear regression is slightly better than random forest.
COVID-19: Digital Transformation as a Force for Resilience

This study highlighted digital transformation, the transformation of accounting in the context of the COVID 19 pandemic, in light of the experiences of Brazilian multinational corporations from different sectors. Specifically, this study highlighted the relevance of digital transformation, through dynamic capabilities, for accounting / multinational corporations to become resilient in the context of the COVID 19 pandemic. Thus, using a scalar-type questionnaire, a survey was carried out with accounting professionals (accountants, controllers, analysts, coordinators, supervisors, etc.) of said corporations. The findings signaled that resilience was enhanced through dynamic capabilities, with emphasis on adaptability. This study is original and has implications for the practice of accounting, signaling the priorities of capabilities to achieve resilience in times of crisis or turmoil. This study represents a guide for managers in the accounting area, showing which skills need to focus their efforts. Furthermore, this research advances the body of knowledge for accounting theory.
Pandemic Opportunity to Reinvent Education

Due to the recent pandemic due to COVID-19, the world has changed, and resilient humanity has handled various sectors excellently, including education with technology and education content aligned to the industry's needs. Education has been changing, referred to as a journey of Education 1.0 to Education 4.0, adjusting to industry requirements of meeting basic needs from reading and writing to computing and then the internet of things with varying quality levels across the globe. All these changes brought the focus on education to the front. These technology changes brought forth by businesses are changing the education requirements and standards and the conditions of skills, foundation literacies, competencies, and human character qualities. The education platforms brought in a concept of nano-degrees, with online learning supported by customised data that corrects the mismatch between occupational supply and demand without any completion with the universities. Now with the COVID pandemic in operation, the survival of the universities is challenged. The educators need to update their expertise and knowledge level, and technologies are available for that.
Visualization of Needs Differences Between Commercial Areas Based on Topic Graph Centrality

Targeting that accurately captures customer needs is expected to become even more important in the future in order to overcome competitors. Some beauty salon customers also come from local areas to urban stores. Even in downtowns, customer demographics and customer preferences vary greatly depending on the region. Therefore, it can be inferred that there is some relationship behind the location of the store and the needs of customers. The purpose of this research is to understand the characteristics of customer needs and the relationship between regions. In this paper, we analyze the difference in the customer segment between regional areas in the beauty salon industry, through analysis of customer review data posted on the beauty salon reservation site. By applying non-negative matrix factorization method to the customer reviews for the stores in the commercial area to be analyzed, we extracted the topics related to the needs in each region. Furthermore, we attempted to visualize the needs caused by the commercial area by associating the response magnitude of this topic with the location information of the store where the review text was posted.
Deep independent component network for thermal-to-visible face recognition

Cross-domain face matching between the thermal and visible spectrum is a desired technology to recognize the visible face image with probe images captured in the thermal spectrum for the night-time surveillance and security applications. However, the large modality gap between faces captured in different spectrum makes thermal-to-visible face recognition quite a challenging problem. Classical Independent Components Analysis (ICA) and its variants have been successfully used for feature learning, and the projection matrix can be used for forming the basis of images. In this paper, we present a deep joint independent component analysis network (DJICAN), a deep architecture based on multilayer independent component analysis, which learns the mutual mappings between visible and thermal face images. Joint ICA assumes the sources are the same for a visible image and its corresponding thermal image. The goal of DJICAN is to obtain the basis matrices of visible and thermal face images, which represent the individual imaging systems for the two domains. First, a forward multilayer ICA is performed. Then, we use a novel backpropagation algorithm based on a reconstruction loss function to optimize the ICA basis and the sources. Extensive experiments are performed on the ARL polarimetric thermal facial datasets that contain face images that have been taken at three different ranges and with different face expressions. The results show that the proposed method performs better than the state-of-the-art methods in terms of both synthetic image quality and thermal-to-visible face recognition accuracy.