Reviewerly/exHarmony huggingface dataset:
Deep Learning for Wireless Communications: An Emerging Interdisciplinary Paradigm

Wireless communications are envisioned to bring about dramatic changes in the future, with a variety of emerging applications, such as virtual reality (VR), Internet of things (IoT), etc., becoming a reality. However, these compelling applications have imposed many new challenges, including unknown channel models, low-latency requirement in large-scale super-dense networks, etc. The amazing success of deep learning (DL) in various fields, particularly in computer science, has recently stimulated increasing interest in applying it to address those challenges. Hence, in this review, a pair of dominant methodologies of using DL for wireless communications are investigated. The first one is DL-based architecture design, which breaks the classical model-based block design rule of wireless communications in the past decades. The second one is DL-based algorithm design, which will be illustrated by several examples in a series of typical techniques conceived for 5G and beyond. Their principles, key features, and performance gains will be discussed. Furthermore, open problems and future research opportunities will also be pointed out, highlighting the interplay between DL and wireless communications. We expect that this review can stimulate more novel ideas and exciting contributions for intelligent wireless communications.
Development of student’s research activity in the field of music and culture study

The author concludes her 12 years’ experience of pedagogical support of research activity of university students (within a special course in Moscow State University (2007–2019)) and of high school students from Russia (during expeditions to Bashkiria, Khakassia and Altai (2012–2019)) and other countries (at XI International Research School in Yakutia (2018)). Through the case histories from practice the following aspects are examined: the specificity of culture study approach to traditional music of different peoples, thematic variety of regarded problematics, main steps of support of student’s research activity. In a teacher’s methodological accompaniment of students’ research work the following key elements are regarded: motivating students for research; creating conditions for discovering self-important meanings of music study; transformation of their cognition interests and hobbies into a research object; teacher’s independent study and problematization of research topic chosen by students; co-opperative development of the research algorithm; assistance in choosing methods for collecting and analyzing the material; orientation in work with theoretical sources after the student’s self-experience of an independent research activity; assistance in work with text of their research; correcting the work while a presentation preparation; psychological support of students on different stages of their research activity. Theoretical understanding of individual and group research work (also in a multinational audience) is also developed.
The Theory and Implementation of Bayesian Networks Structural Learning using Tabu Search Algorithm

This paper portrays the hypothesis and execution of Bayesian systems basic getting the hang of utilizing unthinkable pursuit calculation. Bayesian systems give an extremely broad but powerful graphical language for calculating joint likelihood disseminations. Finding the ideal structure of Bayesian systems from information has been demonstrated to be NP-hard. In this paper, unthinkable hunt has been created to give progressively proficient structure. We actualized auxiliary learning in Bayesian systems with regards to information characterization. With the end goal of correlation, we considered order task and applied general Bayesian systems alongside this classifier to certain databases. Our trial results show that the Tabu pursuit can locate the great structure with the less time multifaceted nature. The reenactment results affirmed that utilizing Tabu hunt so as to discover Bayesian systems structure improves the grouping exactness.
Increased interest and concentration of student learning with using the kotak pas

The learning model is one of the important parts in the world of education. Learning models with the involvement of play can help students understand mathematic and science. One method of play that can be used by elementary students is the Kotak Pas. Kotak Pas is a learning media that is easy to use, easy to carry or move to place. Kotak Pas is equipped with ten game books that contain mathematic and science. Each book has levels, from the easiest to the most difficult. Community service activities are carried out with several methods, namely conducting surveys and designing teaching materials, introducing Fitting Boxes, training teachers and students with Kotak Pas, and feedback.
Attribute-Induced Bias Eliminating for Transductive Zero-Shot Learning

Transductive Zero-shot learning (ZSL) targets to recognize the unseen categories by aligning the visual and semantic information in a joint embedding space. There exist four kinds of domain biases in Transductive ZSL, i.e., visual bias and semantic bias between two domains and two visual-semantic biases in respective seen and unseen domains, but existing work only focuses on the part of them, which leads to severe semantic ambiguity during the knowledge transfer. To solve the above problem, we propose a novel Attribute-Induced Bias Eliminating (AIBE) module for Transductive ZSL. Specifically, for the visual bias between two domains, the Mean-Teacher module is first leveraged to bridge the visual representation discrepancy between two domains with unsupervised learning and unlabelled images. Then, an attentional graph attribute embedding is proposed to reduce the semantic bias between seen and unseen categories, which utilizes the graph operation to capture the semantic relationship between categories. Besides, to reduce the semantic-visual bias in the seen domain, we align the visual center of each category, instead of the individual visual data point, with the corresponding semantic attributes, which further preserves the semantic relationship in the embedding space. Finally, for the semantic-visual bias in the unseen domain, an unseen semantic alignment constraint is designed to align visual and semantic space in an unsupervised manner. The evaluations on several benchmarks demonstrate the effectiveness of the proposed method, e.g., obtaining the 82.8%/75.5%, 97.1%/82.5%, and 73.2%/52.1% for Conventional/Generalized ZSL settings for CUB, AwA2, and SUN datasets, respectively.
A Literature Study on Digital Currency and Historical Developments of Money: Dynamic Pattern in Currency, Central Bank Digital Currency and Libra

This study attempts to find out the characteristics of digital currency and currency transformation through the analytical descriptions of the literature. In the early days of the emergence of new currency, market-oriented autonomous monetary adjustment was made along with various attempts by the private sector, and then government-centered central currency management and coordination were made for the national monopoly of profits and power. Digital currency can be seen as the emergence of a new form of money that will bring about paradigm changes. CBDC can be divided into direct and indirect types. CBDC is expected to require a strategic approach by the government or firm as it will bring about changes in the ecosystem of related industries. Libra is a stablecoin designed to minimize price fluctuations, and if it succeeds in commercializing it, it is expected to bring about revolutionary changes in the financial industry around the world.
Analisis Kesalahan Penggunaan Afiks dalam Makalah Mahasiswa Semester 1 Program Studi Pendidikan Bahasa Indonesia Universitas Al Asyariah Mandar

This research is based on the problem of the many errors in the use of affixes in students' papers or scientific papers. This affects the various changes in meaning caused by affixes. This study aims to describe the errors in the use of affixes contained in the first semester student papers in the Indonesian language study program at Univeristas Al Asyariah Mandar. This type of research is descriptive qualitative. The instrument used in this study was the human instrument. The data collection technique used is the note taking technique. The object under study is a paper written by a student. The results showed that from 14 research sources 32 data were produced and there were three types of errors in the use of affixes, namely prefixes, confixes, and simulfices. As for the prefix error, which is 1 error or 3.13%, there are 18 errors or 56.25% and 1 error or 3.13%. Errors in the use of confixes, namely as many as 1 error or 3.13%, and as many as 8 errors or 25.00% and in-i as much as 1 error or 13.13%. Error in using simulfix, which is 2 errors or 6.25%. Based on the results of data analysis that has been done, it can be concluded that students make a lot of mistakes in the use of pre-prefixes. In addition, various spelling errors were also found. Therefore, mastering affixes and spelling needs to find a solution.
How to Delete Your MobiKwik Account

If you want to delete your MobiKwik account, you can learn how to do that here. This is the step by step guide about how to delete your MobiKwik account ...
형태초점 영어수업의 암시적 및 명시적 기법의 효과 비교를 위한 메타분석

This study is to investigate the effects of English classes taught in explicit vs. implicit FnF (Focus-on-Form) instruction via a meta-analytic approach. To this end, the study extensively searched and collected papers meeting the criteria being a quantitative approach and done in a domestic EFL (English as a foreign language) setting. 59 studies were analyzed according to their FnF instructional technique and their results in vocabulary, function and/or grammar. The moderating factors were classified into learners’ ages and instructional period, which in turn the study investigated any differential effects moderated by these different factors. The findings are: Both explicit and implicit FnF result in about medium effects on vocabulary, function and grammar. The moderating effects did not contribute any major statistical differences between explicit and implicit FnF. It was noted, though, that explicit FnF caused a big effect while implicit FnF a medium effect, which follows a conclusion made by a collection of papers mentioned in the references. This study cautions that explicit FnF effects on vocabulary are limited by not having sufficient number of papers to cover the statistical necessity.
On the Development of Expert Systems

This report shows a way to deal with building specific Personal Computer helped programming designing  devices for the improvement of master frameworks. These apparatuses structure an incorporated improvement condition permitting the Personal Computer  supported advancement of various applications in the suitable field. The coordinated condition speaks to the mixture  of Secure Windows Initiative programming language and Data Base Management System  PostgreSQL apparatuses. Secure Windows Initiative- programming language do providing the most fitting apparatuses to the arrangement of intelligent assignments in master frameworks. Consequently, we have to apply a suitable information base administration framework to expand the ability of the information base. For this reason we utilized the most developed open source Postgre-Sstructured QueryLanguage apparatuses. Because of our exploration we have made apparatuses empowering the similarity of this programming language.
Pop Up Book Kubus dan Balok Untuk Siswa SMP

The research aims to construct learning media for pop up books on cube and block materials that are suitable for use in junior high school students. This research is a development study using a modified 4-D model that is Define, Design, Develop and Deseminate from Thiagarajan. The subject of the material developed in this study was cube and beam class VIII material with a class VIII trial subject of SMPN 1 Sumbang. The results showed that the learning media pop up book was very valid (3.57) according to the validator, the teacher's response was very good (3.31) and student responses were good (3.29)
RDAP "Ask Me Anything" Townhall webinar “Using APIs for Non-Programmers”

This is a video recording of the RDAP webinar “Using APIs for Non-Programmers” held on April 22, 2020 @ 1:00 pm EST as part of the RDAP Town Hall “Ask Me Anything” series webinar. Webinar Desc...
Privacy-Preserving Payment Splitting

Abstract Widely used payment splitting apps allow members of a group to keep track of debts between members by sending charges for expenses paid by one member on behalf of others. While offering a great deal of convenience, these apps gain access to sensitive data on users’ financial transactions. In this paper, we present a payment splitting app that hides all transaction data within a group from the service provider, provides privacy protections between users in a group, and provides integrity against malicious users or even a malicious server. The core protocol proceeds in a series of rounds in which users either submit real data or cover traffic, and the server blindly updates balances, informs users of charges, and computes integrity checks on user-submitted data. Our protocol requires no cryptographic operations on the server, and after a group’s initial setup, the only cryptographic tool users need is AES. We implement the payment splitting protocol as an Android app and the accompanying server. We find that, for realistic group sizes, it requires fewer than 50 milliseconds per round of computation on a user’s phone and the server requires fewer than 300 micro seconds per round for each group, meaning that our protocol enjoys excellent performance and scalability properties.
GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Pixel Labeling

Existing CNN-based methods for pixel labeling heavily depend on multi-scale features to meet the requirements of both semantic comprehension and detail preservation. State-of-the-art pixel labeling neural networks widely exploit conventional scale-transfer operations, i.e., up-sampling and down-sampling to learn multi-scale features. In this work, we find that these operations lead to scale-confused features and suboptimal performance because they are spatial-invariant and directly transit all feature information cross scales without spatial selection. To address this issue, we propose the Gated Scale-Transfer Operation (GSTO) to properly transit spatial-filtered features to another scale. Specifically, GSTO can work either with or without extra supervision. Unsupervised GSTO is learned from the feature itself while the supervised one is guided by the supervised probability matrix. Both forms of GSTO are lightweight and plug-and-play, which can be flexibly integrated into networks or modules for learning better multi-scale features. In particular, by plugging GSTO into HRNet, we get a more powerful backbone (namely GSTO-HRNet) for pixel labeling, and it achieves new state-of-the-art results on the COCO benchmark for human pose estimation and other benchmarks for semantic segmentation including Cityscapes, LIP and Pascal Context, with negligible extra computational cost. Moreover, experiment results demonstrate that GSTO can also significantly boost the performance of multi-scale feature aggregation modules like PPM and ASPP. Code will be made available at https://github.com/VDIGPKU/GSTO.
Correction of wavefront rotation between interferometer and shack-hartmann sensor using bending modes

Abstract In some active optics system, the influence function, which is the surface deformation ability of an actuator, is measured by directly detecting the mirror surface using an interferometer and the surface correction of the primary mirror is done using the Shack-Hartmann (S-H) sensor. However, if the wavefronts of the interferometer and the S-H sensor have an obvious rotation, a large error will be introduced to the correction force calculations. In this paper, bending modes are used to detect this wavefront rotation. Bending modes are a series of orthonormal stiffness-increasing modes calculated using the influence functions. Two methods, optimum search and top-line (a line passes through the surface center and the top of surface deformation peaks) detection, are developed and tested in simulation using the experimental data of a 620-mm active optics system. The detection errors of the two methods are 0.286° and 0.085° in the simulation, respectively. The rotation detection method is then tested on this 620-mm system. The simulation and experimental results show that top-line detection is a suitable method for the detection of wavefront rotation.
Accreditation System of Distance Higher Education in the U.S. and Its Implications for China

Distance higher education is an important component of the Chinese higher education. How to enhance the quality of distance higher education is one of the key issues to be addressed in the research areas of distance education and higher education. As a crucial step to quality improvement, the constitution of accreditation system in distance higher education balances the benefits of all parties involved. This paper explores the American accreditation system of higher education and distance education and concludes with suggestions for the constitution of the Chinese quality assurance system: 1) establishing third-party institutions to share part of the government roles; 2) constituting the accreditation system; 3) setting up appropriate standards; 4) publicizing results of quality assurance work; and 5) building internal quality assurance mechanism. This paper is aimed to provide some reference to the constitution of accreditation system of distance higher education in China, facilitate the solution of quality problems in distance education, and promote the continuous improvement and development of distance education in China.
The effect of mind mapping training on neurosurgical nursing trainees

ObjectiveTo explore the effect of mind mapping training for neurosurgery nursing interns.MethodsA total of 60 nursing interns from December 2017 to December 2018 were selected as study objects, and were randomly divided into the routine group and the management group, with 30 cases of each group.The routine group was trained by the routine training method, while the management group was trained by the mind mapping training method.The effects of different training methods for nursing interns in two groups were compared.ResultsThe scores of the clinical nursing theoretical knowledge, clinical thinking ability, basic medical technology nursing operation, clinical nursing safety management, and clinical emergency management of nursing interns in the management group were higher than those of the routine group, with statistically significant differences(P<0.05). The satisfaction of nursing interns in the management group was significantly higher than that in the routine group, with statistically significant difference(P<0.05).ConclusionsThe effect of mind mapping training on neurosurgical nursing interns is significant, which can obviously improve the clinical teaching effect and the satisfaction of nursing interns.Key words: Neurosurgery; Nursing interns; Mind mapping; Training effect
Il «diritto umano alla scienza» e l’emergenza da CoViD-19

This paper analyses some very interesting aspects of the draft General Comment on the «human right to science», adopted by the UN CESCR, in connection to the CoViD-19 pandemic to underline the role that science can have in emergency situations. These aspects are the right of access to science, the non-discrimination principle, the free dissemination of scientific data and publications.
Learning a Reinforced Agent for Flexible Exposure Bracketing Selection

Automatically selecting exposure bracketing (images exposed differently) is important to obtain a high dynamic range image by using multi-exposure fusion. Unlike previous methods that have many restrictions such as requiring camera response function, sensor noise model, and a stream of preview images with different exposures (not accessible in some scenarios e.g. some mobile applications), we propose a novel deep neural network to automatically select exposure bracketing, named EBSNet, which is sufficiently flexible without having the above restrictions. EBSNet is formulated as a reinforced agent that is trained by maximizing rewards provided by a multi-exposure fusion network (MEFNet). By utilizing the illumination and semantic information extracted from just a single auto-exposure preview image, EBSNet can select an optimal exposure bracketing for multi-exposure fusion. EBSNet and MEFNet can be jointly trained to produce favorable results against recent state-of-the-art approaches. To facilitate future research, we provide a new benchmark dataset for multi-exposure selection and fusion.
Levy Library Guides: ISMMS Shift to Remote Instruction: Home

In response to the 2020 COVID-19 outbreak, ISMMS has made a temporary shift to online instruction. This is your guide to current info and updates.
Tool for Simulating Branch and Bound Computations

Abstract The paper describes a simulator of parallel Branch and Bound (BnB) method. Several subdomain trees for benchmark functions are analyzed, a characteristic Gaussian-like distribution is discovered. An algorithm of artificial tree generation is formulated according to this criterion. The process of simulator modeling is described, several computational experiments are conducted. Their results show a hyperbolic decrease trend for modeled time as the number of computational units grows, which is concluded to be similar to real systems.
USING SYSTEM MODELS TO RESOLVE CO-REFERENCE IN TRANSLATING NATURAL LANGUAGE REQUIREMENTS INTO CODE

One important quest in software development is to be able to translate natural language requirements into executable code.In this paper we address the issue of co-reference resolution, extending our previous work on a controlled natural language translation to live sequence charts.While domain-specific co-reference resolvers perform adequately, and state-of-the-art deep learning-based generic resolvers are emerging, in software development, especially for critical systems, a higher level of performance is required.We present an approach that incorporates system-model-based anaphora resolution.Using an annotated corpus for software requirements with system models, we show that while our approach reaches high accuracy, it is synergistic to generic resolvers and when combined with them it reaches higher accuracy.Our method shows that incorporating system models into the pipeline of requirements to code is a promising approach.
지능형 관광 서비스를 위한 관광 사진 분류체계 개발

In recent years technology of Convolutional Neural Network (CNN) among the technologies of deep learning has evolved dramatically and has shown an outstanding performance in the analysis of image data. First of all, the training of deep learning model is prerequisite to classify the photos posted by the tourists on Web by applying CNN technology. In this study we aim to develop the photo classification system in view of travel purpose in order to classify the photos posted by tourists on Flickr. We developed the category for photo classification by reviewing around 38,000 photos posted by tourists as well as by analysing literatures and web sites, and then verified the category by classifying 8,400 photos one by one manually according to the category developed. The category we developed has 3 hierarchical levels such as 13 major classification, 64 medium classification and 164 minor classification. We expect that our study can applied in base material when one tries to classify the photos for travel purpose by using the CNN deep learning model.