Reviewerly/exHarmony huggingface dataset:
Perancangan Sistem Informasi Akuntansi Pendapatan Retribusi Pemakaman Berbasis Microsoft Visual Studio 2010

This study aimed to design a retribution income funeral information system accounting using Microsoft Visual Studio 2010 at the Dinas Penataan Ruang Kota Bandung. Methods of data collection using direct observation methods and conducting literature reviews related to the subject matter and methods of software development using the waterfall model. From the research conducted, the authors found a problem, namely the ineffective process of recording and making savings and loan reports due to the absence of an adequate information system and still using a manual system so that errors often occur and require a long time in the recording process. Therefore, an information system is designed using Flowmap modeling and DFD (Data Flow Diagram) which is then implemented with the Microsoft Visual Studio 2010 programming language and Microsoft Office Access as a database. Suggestions are given by: 1) implement the results of the design by preparing hardware and brainware to meet system requirements and simplify the process of the system to be used 2) continuous system evaluation 3) development of funeral retribution income information systems must adapt to technology which will continue to grow.Keywords: Information Systems, Retribution Income, Microsoft Visual Studio 2010, Microsoft Office Access
Sentralisasi dan Desentralisasi dalam Manajemen Madrasah Ibtidaiyah

Centralization is the process of administering the state and government that takes place centrally. Decentralization is the surrender of government authority by the government  to autonomous regions to regulate and manage self-government in the system of the Republic of Indonesia Unitary State. Decentralization of education will provide full opportunity for education managers to regulate and develop their regions, so that the potential and opportunities of education, especially Elementary School, can develop optimally.
FILTRA: Rethinking Steerable CNN by Filter Transform

Steerable CNN imposes the prior knowledge of transformation invariance or equivariance in the network architecture to enhance the the network robustness on geometry transformation of data and reduce overfitting. Filter transform has been an intuitive and widely used technique to construct steerable CNN in the past decades. Recently, group representation theory is used to analyze steerable CNN and reveals the function space structure of a steerable kernel function. However, it is not yet clear on how this theory is related to the filter transform technique. In this paper, we show that kernel constructed by filter transform can also be interpreted in the group representation theory. Meanwhile, we show that filter transformed kernels can be used to convolve input/output features in different group representation. This interpretation help complete the puzzle of steerable CNN theory and provides a novel and simple approach to implement steerable convolution operators. Experiments are executed on multiple datasets to verify the feasibilty of the proposed approach.
Review on Modern Mathematics in the Analysis of Artificial Intelligence Data and Innovative Devices

The current review is concerned with the principle of modern mathematics in the analysis of artificial intelligence data and innovative devices, although the performance of both graphics processing units (GPUs) and programmable logic gates (FPGAs) greatly outperforms that of CPUs, a factor of 10 in Efficiency may be achieved using a more precise design using an application-specific integrated circuit (ASIC). These accelerators employ strategies such as improved memory usage and low precision arithmetic to speed up computational operations and computing throughput. Some low-resolution floating-point formats use AI acceleration and become half-accurate and bfloat16. Deep learning frameworks are still in the midst of development which has made it difficult to design custom computer hardware, while reconfigurable devices such as Programmable Logic Gate Arrays (FPGAs) have made it easier to develop hardware, frameworks, and software together. Microsoft used FPGAs to speed inference and its application to AI prompted Intel to acquire Altera to integrate FPGAs into the server. CPUs that would be able to accelerate AI as well as general-purpose tasks.
Criminal persuasion techniques versus sharing personal information

Human factor in cybersecurityThis unique event aims to:-Provide a near zero cost event to reduce barriers to international collaborations between researchers-Promote the exchange of cybersecurity findings and perspectives-Reach out to and engage new audiences curious about cybersecurity
HUBUNGAN ANTARA FREKUENSI BERMAIN GAME ONLINE DENGAN KEDISIPLINAN BELAJAR

This study aims to examine the relationship between the frequency of playing online games with student learning discipline. The hypothesis proposed is that there is a negative relationship between the frequency of playing online games with student learning discipline. The population of this study were students taken using the Purposive Random Sampling technique, namely students covering eight existing faculties, namely the Faculty of Psychology, Faculty of Teacher Training and Education, Faculty of Law, Faculty of Social and Political Sciences, Faculty of Engineering, Faculty of Islamic Religion, Faculty of Economics, Faculty of Agriculture, who are actively playing games. on line. The independent variable in this study is the Frequency of Playing Online Game Clash Of Clans (COC) which is expressed by a scale, while the dependent variable is learning discipline which is also expressed by a scale. Data analysis used Product Moment Correlation analysis. The results of the analysis showed that rx = 0.223 with p = 0.071, (p > 0.05), this means that there is no significant relationship between the frequency of playing the online game Clash Of Clans (COC) and Student Learning Discipline. These results indicate that the hypothesis proposed in this study is rejected.
Effects of Embedded Depth of Internal Printed Ferromagnetic Cell on Data Clarity of Rewritable 3D Objects

We proposed the method for non-destructively embedding information inside a 3D fabricated object very clearly by the process of re-magnetization. Our strong points are that the 3D object is finished (ready to use) after only a printing process, and is able to be reused by re-writing information many times. In this paper, we investigated the effects of the depth (positions inside the object) of the storage cell, which is printed as a ferromagnetic filament, on the clarity of the embedded information. Our purpose: we need to find the conditions that gave the most benefit in both obtaining high magnetic strength and protecting the embedded information. With this advantage, the method leads to the production of creating the high-quality household 4D object, the personal interactive 3D object, in the near future.
SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning

We describe a policy learning approach to map visual inputs to driving controls conditioned on turning command that leverages side tasks on semantics and object affordances via a learned representation trained for driving. To learn this representation, we train a squeeze network to drive using annotations for the side task as input. This representation encodes the driving-relevant information associated with the side task while ideally throwing out side task-relevant but driving-irrelevant nuisances. We then train a mimic network to drive using only images as input and use the squeeze network's latent representation to supervise the mimic network via a mimicking loss. Notably, we do not aim to achieve the side task nor to learn features for it; instead, we aim to learn, via the mimicking loss, a representation of the side task annotations directly useful for driving. We test our approach using the CARLA simulator. In addition, we introduce a more challenging but realistic evaluation protocol that considers a run that reaches the destination successful only if it does not violate common traffic rules. A video summarizing this work is available at this https URL , and code is available at this https URL .
Hidden Advertorial Detection on Social Media in Chinese

Nowadays, there are a lot of advertisements hiding as normal posts or experience sharing in social media. There is little research of advertorial detection on Mandarin Chinese texts. This paper thus aimed to focus on hidden advertorial detection of online posts in Taiwan Mandarin Chinese. We inspected seven contextual features based on linguistic theories in discourse level. These features can be further grouped into three schemas under the general advertorial writing structure. We further implemented these features to train a multi-task BERT model to detect advertorials. The results suggested that specific linguistic features would help extract advertorials.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN PERUMAHAN DI BANDAR LAMPUNG

Houses are one of the basic human needs besides clothing and food. There are many factors that influence everyone to choose a house to live in, including a house model, house prices, land area, building area, distance to location, environment and so on. In Bandar Lampung, there are many housing estates. But the absence of a system that can be used by the community as prospective buyers to choose housing to be purchased in accordance with the desired criteria.  This has resulted in consumers, especially buyers of houses or housing, still finding it difficult to find housing according to their desired and financial needs. In this study a housing decision support system was applied. And the results of decision support systems can help decision makers, namely buyers in choosing housing that fits the desired criteria and according to needs and finances.
Reflexiones sobre las clases presenciales y virtuales ante la contingencia sanitaria por COVID-19, en la clase de Filosofía para Niños y Jóvenes. Información del autor: Leslie Cázares Aponte.

Resumen:En este articulo se encuentran las reflexiones y el analisis de la experiencia de las clases en modalidad presencial, virtual y de comunidades de dialogo con diferentes estudiantes y profesores de otros colegios nacionales e internacionales a partir del confinamiento por Covid-19. Los alumnos de la clase de Filosofia para Ninos y Jovenes, han explorado en sus sesiones de dialogo filosofico, la incertidumbre, las certidumbres, el significado de las diferentes formas de particpacion virtuales y presenciales a partir del analisis de temas filosoficos de la vida, pero tambien de temas emergentes para comprender la realidad que les ha tocado vivir. Esta informacion es el resultado de la cristalizacion de las experiencias positivas, pero tambien de las necesidades que han detectado de sus clases, para poder continuar con sus clases en modalidad remota.Este articulo es una de tres partes de la investigacion doctoral sobre las manifestaciones del pensamiento critico de jovenes de preparatoria en su clase de Filosofia para Ninos y Jovenes a partir de un seguimiento cualitativo del desempeno de los alumnos. La informacion que se encuentra, esta vinculada a la mejora de las comunidades de dialogo tanto de manera presencial como virtual, asi como al uso de materiales didacticos y estrategias educativas pertinentes para el futuro de las clases del Proyecto de Filosofia para Ninos y Jovenes de la propuesta de Mathew Lipman y Ann Margaret Sharp. Key word: children and youngsters philosophy, critical thinking, confinement and pandemic, research communities.Abstract:In this article  the analysis and reflections about the experience in presencial, virtual clases, dialog communities with different students and profesors of Mexico are found, since the the confinement because of Covid-19. The philosophy for children and youngsters class students have explored in their philosophy dialog sessions, the uncertainty, certainty the meaning of the different ways of virtual and presencial participation from the life philosophical analysis, but also about emerging topics to understand the reality which they have to live. This information is the result of the solidification of the positive experiences, but also about the needs that they have identified in their classes, to be able to continue with their classes remotely.This article is one of three parts of the doctoral investigation about the critical thinking manifestation of high school youngsters in their children and youngsters’ philosophy class from the students qualitative development follow up. The information hereby presented is linked to dialog community improvement, not only on a presencial way and virtual but also the use of didactic materials and pertinent educative strategies for the future children and youngsters philosophy project classes from the Mathew Lipman and Ann Margaret Sharp proposal.
RRL: A Scalable Classifier for Interpretable Rule-Based Representation Learning

Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. However, rule-based models are hard to optimize, especially on large data sets, due to their discrete parameters and structures. Ensemble methods and fuzzy/soft rules are commonly used to tackle these issues, but they sacrifice the model interpretability. In this paper, we propose a new classifier, named Rule-based Representation Learner (RRL), that automatically learns interpretable non-fuzzy rules for data representation. To train the non-differentiable RRL effectively, we project it to a continuous space and propose a novel training method, called Gradient Grafting, that can directly optimize the discrete model using gradient descent. An improved design of logical activation functions is also devised to increase the scalability of RRL and enable it to discretize the continuous features end-to-end. Exhaustive experiments on 9 small and 4 large data sets show that RRL outperforms the competitive approaches, has low complexity close to the simple decision trees, and is rational for its main technical contributions.
Chapter 2. General Best Practices, Tools, and Resources

Chapter 2 of Library Technology Reports (vol. 55, no. 2), General Best Practices, Tools, and Resources Chapter 2 of Library Technology Reports (vol. 55, no. 2), “Mastering Mobile through Social Media: Creating Engaging Content on Instagram and Snapchat,” is devoted to general logistical information, tools, and best practices for both Instagram and Snapchat. Before diving into the specifics of each platform (which are covered in the following chapters), Alfonzo begins with some “housekeeping tips” you can employ to ensure your accounts are set up for success, including resources for policy creation, collaboration, and creating trackable links for later social media reporting. She then introduces a list of applications that you can use to boost your content, such as a variety of popular photo and video editors. She concludes this chapter with a list of suggested events you can add to your upcoming content calendar.
DESIGNING “LEARY” IN VOCABULARIES TEACHING LEARNING FOR BEGINNER

English is an international language. English is the language of communication in the globalized world, so that almost no country does not learn and use English as the language of international communication. The objective of the research is to designing LeaRy (Learn Vocabulary) for beginner level of English learners. In this research, the researcher used Research and Development (R&D) methods. This research procedure adapts the ADDIE development model which consists of five stages which include analysis, design, development, implementation and evaluation. The researcher used observation and questionnaire to collect the data. The researcher did research and field testing to the 21 students in Al-Lathifiyyah 1 females Islamic Boarding House. The result of material validation is 3.3 points and categorized as good quality. Then, the result of media validation is also categorized as good quality in 3.2 points. In addition, the result of user validation is 4.7 points and categorized as good quality. Therefore, LeaRy learning media is a good learning media to learn English vocabulary, especially in spell vocabulary and Elnglish pronunciation.
LibGuides: Online resources and support: Prepare for uni

A guide to help you with basic and more advanced digital skills for university learning Courses and resources to help you get ready for academic life
MMTL: The Meta Multi-Task Learning for Aspect Category Sentiment Analysis.

Aspect Category Sentiment Analysis (ACSA), which aims to identify fine-grained sentiment polarities of the aspect categories discussed in user reviews. ACSA is challenging and costly when conducting it into real-world applications, that mainly due to the following reasons: 1.) Labeling the fine-grained ACSA data is often labor-intensive. 2.) The aspect categories will be dynamically updated and adjusted with the development of application scenarios, which means that the data must be relabeled frequently. 3.) Due to the increase of aspect categories, the model must be retrained frequently to fast adapt to the newly added aspect category data. To overcome the above-mentioned problems, we introduce a novel Meta Multi-Task Learning (MMTL) approach, that frame ACSA tasks as a meta-learning problem (i.e., regarding aspect-category sentiment polarity classification problems as the different training tasks for meta-learning) to learn an ideal and shareable initialization for the multi-task learning model that can be adapted to new ACSA tasks efficiently and effectively. Experiment results show that the proposed approach significantly outperforms the strong pre-trained transformer-based baseline model, especially, in the case of less labeled fine-grained training data.
Research Guides: Introduction to eBooks: EBSCO eBook Tutorial

This guide includes links to the Carl B.'s eBook collections and answers to eBook frequently asked questions.
An Intelligent System to Assist Piano Composition and Chords Generation using AI and Machine Learning

As a musician and producer, I’ve always struggled with finding chords when I first started writing music [5]. It sometimes goes to the extent of me forgetting my melody because I take so long trying to figure out the chords. So I came up with an idea for this app, that will help amateur and beginner musicians save time and provide chord suggestions to them as a booster to start writing songs [6]. It features a recording or a midi input feature, then the app will carefully analyze the given melody and give a selection of the best chord progressions using intelligent AI. As an output, it is able to present it as guitar chords, piano chords, and ukulele chords, enabling more different musicians to use this app.
Dark Web in the Dark: Investigating when Transactions Take Place on Cryptomarkets

Online illicit marketplaces known as cryptomarkets have gained considerable attention from the media, government authorities, law enforcement agencies, and researchers. An increasing number of studies have investigated various aspects of these cryptomarkets' characteristics, such as product categories, sale volumes, and the number of listings and vendors. However, there is a gap in the literature regarding whether illegal transactions (of illicit drugs) take place during the day or week. This study fills this gap by tracing Bitcoin addresses associated with the six previously leading and most active cryptomarkets—Silk Road, Silk Road 2.0, Agora, Evolution, Nucleus, and Abraxas—to identify the specific timings of these transactions. This study reveals clear patterns of activity on the marketplaces. First, transactions more often take place at night in European countries (Germany, Netherlands, the UK), the US, and Canada, where the cryptomarket drug trade is most active. Second, there are more transactions on Mondays, Tuesdays, and Wednesdays, and fewer on Saturdays and Sundays. This indicates that the retail drug trade accounts for a large part of the cryptomarkets. Further, this study examines the impact of a cryptomarket policing effort known as Operation Onymous, and indicates that this policing effort only displaced users among these marketplaces and did not deter their activity, even in the short-term. It also suggests that Operation Onymous did not alter users’ transaction patterns.
Knowledge Map Reasoning Technology Based on Complex Network

In recent years, with the rapid development of artificial intelligence technology, knowledge mapping research based on data mining, machine learning and knowledge engineering has attracted the attention of a large number of researchers. Knowledge mapping has important theoretical value and practical significance for the development of artificial intelligence and machine cognitive intelligence. In recent years, researchers have achieved a lot of excellent research results in the field of knowledge mapping technology, but there are still some problems to be solved. This paper studies the construction technology of knowledge map based on social network, mainly discusses the problems of entity acquisition, entity disambiguation, relationship completion, relationship reasoning and knowledge conflict resolution. The research results of this paper can provide reference for intelligent applications such as intelligent information retrieval, dialogue system and intelligent recommendation.
Anomaly detection in dosing system

In the process of sewage treatment and dosing, due to the physical and chemical factors of the medicament or thinking factors, the market will cause the dosing abnormal. To monitor the abnormal situation in real-time, this paper uses the Long Short-Term Memory algorithm to learn the time law of dosing and uses real dosing data to predict the value of the next time point by point, which can determine whether an exception has occurred in the dosing system reliably.
IMPLEMENTATION OF ONLINE LEARNING METHOD AND ITS IMPACT ON STUDENTS

The world is frightened by the Coronavirus or COVID-19 outbreak, Indonesia is no exception. Therefore, the Indonesian government has taken several policies to break the chain of transmission of COVID-19. Its main policy is to prioritize health and safety. Work, worship, and Study From Home (SFH). Recently, we revealed that the Coronavirus has had a profound impact on the world of education. This study aimed to obtain the impact of online learning methods on students’ behavior during pandemic COVID-19. COVID-19 affects to education fields, especially the effect on children which requires them to learned from home and using virtual face-to-face school. The effect of this virtual encounter is causing children to feel bored and spend more time playing. Many students feel overwhelmed by the tasks given from school. This virtual learning requires technology such as laptops and smartphones to access scheduled learning videos. However, this also has an impact due to economic hardship in pandemic conditions caused by many parents who lose their jobs and of course cause stress. Parents are required to be able to educate their children at home in accordance with the direction of the teaching staff and of course the burden on parents is increasing. Keywords : COVID-19, Learning, Method, Students, School