Reviewerly/exHarmony huggingface dataset:
Implementasi Algoritma FP-Growth Untuk Menemukan Pola Frekuensi Pembelian Lauk Pada Rumah Makan Takana Juo

All dishes originating from West Sumatra are more popularly known as Padang cuisine. Padang cuisine is one of the most preferred dishes by Indonesians and foreign tourists. Restaurants that serve Padang cuisine, spread all over Indonesia. One of them is the Takana Juo Restaurant located in Singkawang, West Kalimantan. This restaurant serves a variety of side dishes with a distinctive taste from West Sumatra. The problem that occurs in this restaurant is that this restaurant often runs out of several types of side dishes, while there are still many other side dishes, so that many side dishes are often left out every day. So, a solution is needed to see the buying patterns of customers from restaurants in buying side dishes, so that the restaurant can arrange the side dishes that will be sold every day. The data mining approach is considered suitable for finding these customer buying patterns. This study uses data on side dishes sales in one day as a sample data of 12 transactions. Examples of the resulting buying patterns are 12 transactions that buy grilled chicken, 6 transactions buy beef rendang. The algorithm used to help restaurants find customer buying patterns is the FP-Growth Algorithm, so it can help restaurants regulate the number of side dishes to be sold each day. The result of this research is the decision in the procurement of side dishes at the restaurant which can be seen from the frequency pattern of side dishes purchases made by customers.
Сontent of pedagogical practice programs targeted to formation of professional mobility of the master’s degree student

The article deals with the issue of formation of professional mobility of a master's degree student during the pedagogical practice. The issue is regarded with reference to subject area 44.04.01 “Pedagogical Education”. The content of the pedagogical practice programs is represented by four components: motivational, cognitive, activity-centered, and reflexive.
PENINGKATAN PEMAHAMAN TEKNOLOGI INFORMASI DALAM MENDUKUNG PEMBELAJARAN DARING

Online learning is currently an inseparable part of the learning system since the COVID-19 pandemic. One of the obstacles in carrying out online learning is the limited skills in using information technology. This community service aims to improve students' understanding of information technology in supporting online learning. Based on the Pre and Post Test analysis, there was an increase in basic knowledge of information technology by 2.8% - 5.6% while an increase in understanding of the use of information technology was 8.3% - 13.9%. Overall there is an increase in knowledge and understanding of information technology after the training.
Law, Endowment and Inequality in Access to Finance

Theoretical work suggests an ambiguous relationship between the strength of institutions and the distribution of access to finance. Using a sample of listed firms from 70 countries, this study constructs country-level measures of inequality in access to external finance, and explores its relation to institutions. We show that inequality of access is positively related to financial development as well as inequality in the distribution of firm size, firm revenue, and residents’ incomes. Countries with stronger investor protection for equity and debt have higher inequality in equity and debt financing respectively, presumably as a result of higher efficiency in fund allocation. Finally, we find that the historical determinants of institutions, including the civil law tradition and the disease environment encountered by colonizers, are negatively related to inequality in access to external finance. The results support both law and endowment theories.
Technology Convergence and Cybersecurity: A Critical Analysis of Cybercrime Trends in India

Convergence has brought various opportunities in different aspects of life and led to the blurring of the boundaries between IT and telecommunications. It has various dimensions notably– technological, technical and content and has brought a digital revolution. Due to fast growth in e-commerce, e-governance, Social Networking and various other e-services cybersecurity has a growing challenge where database is easily collected and mis-used. Cyber attacks are more pervasive and threats to critical infrastructure and lack of security and underreporting makes Industrial systems and products more vulnerable. Internationally, the most affecting problems is mass-ransomware attacks and valuable data theft much like the last few years. Lack of electronic evidence, non-bailability of most of the Cybercrimes and proper convergence between private, and government institutions are making difficult to seize and stop criminal financial transactions. There are challenges in developing effective cross-national policing of cybercrime regarding cloud computing, social media, wireless/smart phone applications and other innovations in digital technology. There is a need to have one regulatory body to administer these convergent technologies it can be done through appropriate ICT policy and regulatory mechanisms, unified licensing as parts of a broader strategy to promote growth and better position in India and global information environments.
Multivariate Bühlmann-Straub credibility model for claim reserving

Abstract One of the approaches that is used for claim reserving in insurance companies is credibility theory, which allows claim reserving by combining claim payment data with other information. In this paper, the Bühlmann-Straub credibility model is used. Furthermore, in general, claim reserving in a company is done by calculating the claim reserve in each line of business (LoB) in the company, then the total claim reserve for the company (aggregate reserve) is obtained by adding up the claim reserve in each LoB. Considering the possibility that there is correlation between the existing LoBs, the value of aggregate reserve can actually be less than the sum of the claim reserve in each of the existing LoB. Therefore, research on the claim reserving then evolves by considering claim payment data from various LoBs in a company, or also called claim reserving in multivariate context. In this paper, a research is conducted on the development of multivariate Bühlmann-Straub credibility model for claim reserving along with estimation for model’s parameters. The model is used to calculate claim reserve for three LoBs of insurance company in United State, based on the data of claim amount during the period of 2008-2017 that was published by Association of Insurance Commisioners of the United State. It appears that the error of multivariate Bühlmann-Straub credibility model is lower than the error of standard Bühlmann-Straub credibility model.
Efficient Clock Drift Corrections and Self-Surveys for Nimble Nodes

Summary Although a GNSS chip remains an essential component of the next generation of small land seismic recording nodes, their power consumption is high compared to other aspects of the electronics. To maximize the lifetime of the battery and minimize node size and weight, GNSS use should be optimized. We use two field trial examples from the development of the latest generation of land seismic receivers to show how GNSS usage can be optimized without compromising on timing accuracy or the ability of the nodes to self-survey to an accuracy sufficient for seismic processing. Without temperature calibration, we find that as few as four time-checks per day are sufficient to ensure that sample time errors are less than ±1ms. With temperature calibration, GNSS hiatuses of up to several days can be corrected. For short surveys, the lower limit on the number of fixes may be set by positional requirements, with at least 60 measurements being required to measure X, Y and Z to within ±2m.
"We have to reinvent us": teachers and physical education teaching during the COVID-19 pandemic

In the scenario of rapid transformation and high uncertainty caused by the Covid-19 pandemic, this research identified the challenges and learning of Physical Education teachers related to remote education For this, a focus group was held with five teachers from private schools The challenges faced by teachers were: feelings of fear and anxiety;adaptation to online classes and the use of technologies in teaching;inhibiting students to open the cameras;find suitable activities for remote education;the difficulty in evaluating students;the lack of support from some families Regarding learning, they indicated: learning how to use TDICs;intensifying collaboration with colleagues;the adaptation of methodological strategies and the use of creativity Emergency remote teaching created new challenges for Physical Education teachers, but it also provided learning opportunities, including the use of TDICs in teaching
Cognitive Science and Artificial Intelligence, The Intertwined Mesh

The natural human cognitive abilities led the way to artificial cognitive capacities through formalisms and tools which widened the web through computer programs. Now the natural human cognitive capacities which aid in acquiring knowledge and to derive benefits from this knowledge are being translated through artificial intelligence to machines. In this paper, there will be a conscious effort to discuss the various domains which contribute towards cognitive science and how this interface between cognitive psychology and Artificial Intelligence poses one of the biggest challenges in the process of attributing the cognitive abilities to the machines. An intelligent machine, though programmed well, may still go astray in the maze of disparate social or cultural norms.
RANCANG BANGUN SISTEM INFORMASI BERBASIS WEB PADA TEACHING FACTORY BAKERY SMK PUTRA ANDA BINJAI

Teaching Factory is an educational program implemented at SMK. Teaching factory is a learning method created for student learning media to face the real world of work.SMK Putra Anda Binjai has 1 teaching factory bakery in the culinary department.In this case, the teaching factory bakery does not yet have an information system that can promote the production of bread made by students majoring in culinary.Web-based information systems are one of the information technologies that facilitate the promotion process on social media.By using a web-based information system, the bakery teaching factory team will find it easier to promote the bread they make, thereby increasing sales production.From the results of the promotion using the website information system, many people know that SMK Putra Anda has a teaching factory bakery so many people start ordering bread made by students majoring in culinary.This is because the promotion is right on target, namely using social media which is a key to success in promotion.
Dining Philosophers, Byzantine Generals, and the Various Nodes, Users, and Citizens under Blockchain Rule

Agreements, consensuses, protocols, resource-sharing, and fairness are all examples of social and political metaphors that define and shape new computational algorithms. The thought experiments and allegories about resource-sharing or agreement between nodes played a vital role in the development of concurrent programming (enabling processor power-sharing and process synchronization) and still later in the development of distributed computing (facilitating data access and synchronization). These paved the way for current concepts of consensus mechanisms, smart contracts, and other descriptions of cryptocurrencies, blockchain, distributed ledger, and hashgraph technologies, paradoxically reversing the relations between metaphor and artifact. New computing concepts and algorithmic processes, such as consensus mechanisms, trustless networks, and automated smart contracts or DAOs (Distributed Autonomous Organizations), aim to disrupt social contracts and political decision-making and replace economic, social, and political institutions (e.g., law, money, voting). Rather than something that needs a metaphor, algorithms are becoming the metaphor of good governance. Current fantasies of algorithmic governance exemplify this reversal of the role played by metaphors: they reduce all concepts of governance to automation and curtail opportunities for defining new computing challenges inspired by the original allegories, thought experiments, and metaphors. Especially now, when we are still learning how best to govern the transgressions and excesses of emerging distributed ledger technologies, productive relations between software and allegory, algorithms and metaphors, code and law are possible so long as they remain transitive. Against this tyranny of algorithms and technologies as metaphors and aspirational models of governance, we propose sandboxes and environments that allow stakeholders to combine prototyping with deliberation, algorithms with metaphors, codes with regulations.
Language Controls More Than Top-Down Attention: Modulating Bottom-Up Visual Processing with Referring Expressions

How to best integrate linguistic and perceptual processing in multimodal tasks is an important open problem. In this work we argue that the common technique of using language to direct visual attention over high-level visual features may not be optimal. Using language throughout the bottom-up visual pathway, going from pixels to high-level features, may be necessary. Our experiments on several English referring expression datasets show significant improvements when language is used to control the filters for bottom-up visual processing in addition to top-down attention.
Reasonable & Inexpensive Onsite Mobile Repairing Service in Gurgaon

At SPOLU, Our Mobile Repair Service center is a trusted and professionally managed smartphone repair clinic. Our repair team is Always Inclined to provide best
Impact of Knowledge Management on Employee Satisfaction in Nepalese Banking Sector

This research tries to examine the impact of knowledge management on employee satisfaction in the Nepalese banking sector. This research employs a descriptive and causal research design. Survey research design was adopted to carry out this research. Structured questionnaires were administered for collecting primary cross-sectional data. Two hundred and fifty-seven responses were collected. Judgmental sampling method was used for sample identification. Survey research was done to collect quantitative data. For data analysis, partial least square structural equation modeling was used to test the psychometric and econometric aspects of the causal model. SPSS Process Macros was also used for testing the moderating effect of gender on knowledge management to employee satisfaction. Knowledge sharing, knowledge creation, knowledge satisfaction, and knowledge retention had significant effect on employee satisfaction. No support was found for knowledge acquisition. Knowledge management dimensions are independent for gender that gender did not moderate knowledge management dimensions on employee satisfaction. In Nepal, banks and financial institutions are benefiting from implementing knowledge management activities for employee satisfaction with their jobs. It contributes to the knowledge management strategies for the Nepalese banking sector.
The threshold effect of political institutions on the finance-growth nexus: Evidence from Sub-Saharan Africa

This study explores the effect of political institutions on the finance-growth nexus for a sample of 24 Sub-Saharan African countries over the period 2002-2011. Using the recent dynamic panel threshold model proposed by Seo and Shin (2016), we found that financial development has a growth-enhancing effect only in countries with strong political institutions. The results are robust across different institutional indicators.
LibGuides: Moving Files to SharePoint: Next Steps

A temporary guide created to assist in the very easy process of moving files to SharePoint.
Comparison of deep convolutional neural networks for classification of breast ultrasound images

Breast ultrasound has been widely utilized for classifying tumors into benignancy and malignancy. The limitations of traditional breast ultrasound are the handcrafted features obtained by well-trained sonographers and subjective decision according to different individual experiences. Recently, CNN-based deep learning techniques have exhibited better performance in medical images. However, most research for deep learning in medical ultrasound adopts CNN models developed for natural images due to the lack of common standard and dataset. In this paper, we compare six DCNN models which exhibit good performance for natural images - VGGNet, ResNet, InceptionNet, DenseNet, and EfficientNet. Our classification results demonstrate that CNN models of relatively lower performance on natural images show better performance on gray-scale ultrasound images and further study of CNN models are needed focusing on the features of medical images.
Wanted: A Standard for Virtual Patent Marking

Patent marking is used by patentees to provide a form of notice to the public about the existence of a patent and can increase damages awarded in cases of infringement. Historically, marking is done by listing the associated patents on the product they cover. Virtual patent marking (VPM), or web marking, offers patentees a convenient alternative to this practice by allowing patentees to place a web address, linking to a list of the associated patents, on the product instead of the static patent list. However, due to uncertainty in the legal community about proper implementation, adoption has been slow, despite broad agreement on their many advantages over traditional marking. In this article, we lay out guidelines for the proper implementation of VPMs and call for a formal standard to encourage their adoption.
An Experimental Study of Spammer Detection on Chinese Microblogs

With the development of Web 2.0, social media such as Twitter and Sina Weibo have become an essential platform for disseminating hot events. Simultaneously, due to the free policy of microblogging services, users can post user-generated content freely on microblogging platforms. Accordingly, more and more hot events on microblogging platforms have been labeled as spammers. Spammers will not only hurt the healthy development of social media but also introduce many economic and social problems. Therefore, the government and enterprises must distinguish whether a hot event on microblogging platforms is a spammer or is a naturally-developing event. In this paper, we focus on the hot event list on Sina Weibo and collect the relevant microblogs of each hot event to study the detecting methods of spammers. Notably, we develop an integral feature set consisting of user profile, user behavior, and user relationships to reflect various factors affecting the detection of spammers. Then, we employ typical machine learning methods to conduct extensive experiments on detecting spammers. We use a real data set crawled from the most prominent Chinese microblogging platform, Sina Weibo, and evaluate the performance of 10 machine learning models with five sampling methods. The results in terms of various metrics show that the Random Forest model and the over-sampling method achieve the best accuracy in detecting spammers and non-spammers.
Holmes, Humility, and How Not to Kill Each Other

Oliver Wendell Holmes’s dissent in Abrams v. United States is one of the intellectual anchors of modern First Amendment doctrine. In the century since Abrams, we have witnessed changes in society, technology, and politics that have shaped and reshaped the contours of our First Amendment landscape. But not everything has changed — some aspects of our human experience remain remarkably similar to the context in which Holmes wrote. One unchanged aspect of the human condition is our inability to know with certainty. Confronted with this reality in his own day, Holmes at times gestured toward a foundationless relativism. But even if his larger corpus tends in that direction, his Abrams dissent can be read to sketch a less skeptical approach rooted in a kind of epistemic humility. This interpretation enlists Holmes as an advocate for more charitable discourse across deep differences. In today’s pluralistic society, acknowledging our lack of certainty can help us move toward better dialogue with one another. Part I offers a reading of Holmes’s Abrams dissent that focuses on the humility underlying Holmes’s epistemic claims and explains the implications of this humility for discourse norms. Part II distinguishes epistemic humility from more skeptical views. Part III then applies a lens of epistemic humility to three kinds of truth claims in contemporary discourse: claims whose certainty is not provable (focusing on the example of religious claims), claims whose practical certainty is not yet proven (focusing on the example of medical treatments of transgender children), and claims that are certain to be false (focusing on the example of demonstrable lies).
Ask Question with Double Hints: Visual Question Generation with Answer-awareness and Region-reference

The task of visual question generation~(VQG) aims to generate human-like questions from an image and potentially other side information (e.g. answer type or the answer itself). Despite promising results have been achieved, previous works on VQG either i) suffer from one image to many questions mapping problem rendering the failure of generating referential and meaningful questions from an image, or ii) ignore rich correlations among the visual objects in an image and potential interactions between the side information and image. To address these limitations, we first propose a novel learning paradigm to generate visual questions with answer-awareness and region-reference. In particular, we aim to ask the right visual questions with \emph{Double Hints - textual answers and visual regions of interests}, effectively mitigating the existing one-to-many mapping issue. To this end, we develop a simple methodology to self-learn the visual hints without introducing any additional human annotations. Furthermore, to capture these sophisticated relationships, we propose a new double-hints guided Graph-to-Sequence learning framework that first models them as a dynamic graph and learns the implicit topology end-to-end, and then utilize a graph-to-sequence model to generate the questions with double hints. Our experiments on VQA2.0 and COCO-QA datasets demonstrate that our proposed model on this new setting can significantly outperform existing state-of-the-art baselines by a large margin.
Making Use of Reviews for Good Explainable Recommendation

Reviews are used in generating explainable recommendation. However, the use of reviews has so far not been adequately addressed. In this paper, we examine methods that make use of reviews effectively. There is a trade-off between the number and quality of reviews to use, that is, we should like to use reviews as many as possible to generate explainable recommendation, however in a large number of reviews there can be low quality ones, which can cause low quality explainable recommendation generation. We discuss new methods that use not only reviews written by a user but also those utilized by the user to generate good explainable recommendation. Our methods can be applied to different explainable recommender approaches, which is shown by adopting two state-of-the-art explainable recommender approaches in this paper. Experimental results demonstrate that our methods can be of benefit to existing explainable recommender approaches as regards both recommendation and its explanation qualities.