Reviewerly/exHarmony huggingface dataset:
Student Campus as a Competitive Advantage of a Private Practice-oriented University

The subject area of the article is the improvement of the competitive position of a private Russian university focused on training, including professional entrepreneurs, due to the presence of its own suburban campus. The article suggests a variant of its creation that is accessible to any large and market-successful non-state university. Unlike the most famous foreign universities that are in the top 100 [1] and have built their campuses at the expense of their own high incomes, or from Russian federal universities funded from the budget, even the largest domestic private universities do not have the resources to build full-fledged campuses according to existing standards. Therefore, at the first stage, it is advisable for them to focus on the purchase of small country boarding houses from among the departmental rest houses built in Soviet times. The requirements for the organization of the work of such a campus are formulated, including work and rest modes for both students and university teachers. The final part of the article clarifies the educational technologies that are most adapted for the considered variant of the organization of training. The purpose of the article is to argue the expediency of creating suburban campuses as a necessary element of the infrastructure of a large private university practicing entrepreneurship training. To achieve this goal, the following tasks are solved in the study: to formulate and briefly argue the main competitive advantages that the presence of a suburban student campus provides to a modern university, regardless of its organizational and legal status; to propose and justify a low-budget option for creating a suburban campus, available not only to state federal universities using budget allocations for this, but also to private institutions of higher professional education; to formulate the basic requirements for the organization of the campus, as well as for the work and rest regimes of direct participants in the educational process.
Dante e fumetti: Ricadute nella didattica, suggestioni divulgative

Dante’s Divina Commedia is one of the most influential literary works in Italy and not only. Reading, understanding and analysing it are a fundamental part of the skills every Italian student should reach, due to the national school programs, particularly in middle and high school. Many Italians and internationals comics’authors worked on Divina Commedia, feeding the collective imagination about Dante, giving new readings of popular suggestions – in this paper, we’ll partially analyse Marcello Toninelli’s and GōNagai’s works, as influences in didactics, but also as peculiar products, with a universal approach, inspired by Dante’s world. So comics can also be considered didactic media. In fact students can be involved in a process where they could build their own comics based on Divina Commedia. Considering both Divina Commedia and comics linguistic media, every student will be able to join a travel from literature to comics and back, creating a final original product, testifying comprehension and personalization of the original literary work.
Game Edukasi Untuk Menghafal Al-Quran Secara Sederhana Menggunakan Game Development Kit

The Covid-19 pandemic has greatly impacted various aspects around the world, the education aspect has not escaped attention, forcing students and workers to do activities at home. And most students (especially Islamic schools) who when they enter school are memorizing the Al-Quran forget to return when they return to school. This is very unfortunate considering the virtue of memorizing the Qur'an is very great reward and also the goodness in it. Various methods are also widely used by parents and teachers, including through learning media in the form of games. Education Game "Ngaji Yuk" is based on the problem of memorizing the Al-Quran using a Game Development Life Cycle (GDLC) game development kit based with AppGameKit Classic programming language. This game is made so that players can memorize the Al-Quran in a very simple way, namely by choosing true or false statements in the questions that appear in front of the screen. It is hoped that this game can become an interactive game for everyone, especially children who are learning to memorize the Al-Quran. It is also hoped that this game can become a new learning medium that can help raise children's enthusiasm to memorize the Al-Quran with the main emphasis on always repeating memorization (Muroja'ah), with this learning being carried out properly, because this game has gone through the testing phase using Cyclomatic Complexity. From the results of the questionnaire that the author conducted with respondents aged 18 to 34 years who are still active with computers, it can be concluded that the game "Ngaji Yuk" has been developed according to user needs.
Perencanaan Strategi SI/TI Untuk SMP Menggunakan Enterprise Architecture Planning

In the future development of data processing will be increased in line with the development of increasingly complex enterprise. Many Enterprise failure due to lack of careful planning that includes all elements of the organization. Enterprise will require data and information that accurate, timely and relevant for all parts that fit the needs of the business as well as the sharing of data between organizational units. To accommodate these needs can be done by increasing the utilization and strategy support SI/IT in the planning, implementation and control requires alignment with business strategy. Information system development aims to achieve the vision and mission of information system Junior high school in order to provide quality of data/information for the sake of business. The first step of achieving the vision and mission of this information system is to plan the information system to identify information needs and possible innovations in the use of technology to improve enterprise performance. Planning can use Enterprise Architecture Planning (EAP) methodology which result blueprint of the architecture data, application architecture, technology architecture and implementation plan for the enterprise In this research, a study case for planning of integrated information system development using EAP with applying value chain model and analisys approaches and tools is performed on Junior high school. The result in these cases study is the enterprise model, information resource catalog documentation, data architecture, application architecture, technology architecture and implementation plans
National Civil Applications Center

First posted January 26, 2023 For additional information, contact: Director, National Civil Applications CenterU.S. Geological Survey12201 Sunrise Valley Drive, MS 562Reston, VA 20192Email: cac@usgs.gov The U.S. Geological Survey (USGS) National Civil Applications Center (NCAC) analyzes remote-sensing data from the Intelligence Community (IC) and the U.S. Department of Defense (DOD) to support public safety missions and to study land-surface and environmental changes. The NCAC provides remotely sensed images to USGS scientists and other civilian Federal agencies; the images come from intelligence and military sensors, referred to as U.S. National Imagery Systems (USNIS), and unclassified commercial satellite data purchased by the DOD. Often these data are referred to as Geospatial Intelligence (GEOINT), which is defined in U.S. Code, title 10, section 467 as “the exploitation and analysis of imagery and geospatial information to describe, assess, and visually depict physical features and geographically referenced activities on or about the earth. Geospatial intelligence consists of imagery, imagery intelligence, and geospatial information.” The NCAC also provides the secretariat with staff and manages the U.S. interagency Civil Applications Committee (CAC), which oversees and facilitates the appropriate civilian uses of overhead remote-sensing technology and data collected by military and intelligence systems and commercial sources. Funded by the USGS National Land Imaging Program, the NCAC operates facilities in Reston, Virginia, and Lakewood, Colorado.
Virtual Art Exhibition System: An Implementation Method for Creating an Experiential Museum System in a Virtual Space

In order to provide art exhibitions in a virtual space which integrates various data of an art museum and gives an emotional experience, the Virtual Art Exhibition System is proposed. This research uses a multi-database called Artizon Cloud to display museum data, combinate with technologies called Data Sensorium and Torus Treadmill to project images and enable visitors to walk around the virtual museum. Moreover, the virtual museum will exploit the users intentions and be personalized, automatically generating further art exhibitions.
Rules for Converting Natural Language Text with Motion Verbs into TIL-Script

The paper deals with the rules for converting natural language text with motion verbs into TIL-Script, the computational variant of Transparent Intensional Logic (TIL). This function is part of the TILUS tool, which is now being worked on, and which will be used for the needs of appropriate textual information sources retrieval and natural language processing. Our work is currently starting on a module that allows the transformation of a particular subset of natural language texts describing journey descriptions into logical constructions. Hence, in this paper, we focused on the transformation rules for sentences containing motion verbs describing the agent’s movement on the infrastructure. These rules are based on the utilization of Stanford typed dependencies representation and verb valency frames of motion verbs.
Steps to Emotion Corpus Creation in Thai: An Exploration of Thai Emotion Wordlists, Depression Corpus and Facial Expression in Speech Situation

In this paper, we proposed a two-phase project on emotion corpus creation based on multi-knowledge of cognitive semantics, discourse analysis, paralinguistics, and computer science. Data were gathered from Thai lexicon of five main Thai dictionaries and thesaurus, in addition to written and spoken texts of people with depression in Thai and facial expression with speech situation. We found that semantic primes and features of each emotion were needed to serve as a guideline of emotion categorization in Thai context. We introduced the step-by-step methods of the first phase to create Thai emotion corpus entailing both verbal and nonverbal corpora. The way to classify emotion corpus by focusing on the specific text of depression as well as to find the guidelines of labelling facial expression in the situation of specific emotions was explored. Lastly, the step of creating emotion corpus in the second phase was introduced with some suggestions and discussion.
A Literature Review of Blockchain Applications in Healthcare

Abstract: Health data is usually in a scattered manner as they are usually gathered by various entities. The data’s scatteredness develops a burden in designing adequate health bills which ensure the well-being of the patients. Moreover, the concerns in achieving synchronization and instantaneous access to such data worsens the problem all the more. So, designing a system that can ensure the integrity, security, preciseness, and accessibility of the patient’s medical data is of paramount importance. Health systems based on blockchain can guarantee the above features and protect data from being tampered or from any kind of attacks. Blockchain also helps in management of data and its flawless integration. It makes most of its smart contracts feature to execute the several requests and queries related to the patients (medicine, appointments, medical procedures, lab tests or previous medical information). All the stakeholders in the system should be able to obtain the medical data preserved in a database not having to compromise on its credibility. In this survey, we examine the previous research work various authors have carried out on implementing blockchain in several aspects of healthcare.
Deep Learning Based Adaptive Multimodal System for Bitcoin Price Prediction

Abstract: Cryptocurrencies are the best example of Blockchain. And the Blockchain is a decentralized network where they are multiple nodes that control the network. Bitcoin established itself as the first decentralised cryptocurrency in 2009. Bitcoin revolutionized the crypto world and all other cryptocurrencies other than bitcoin such as Ethereum, Ripple, etc. are called altcoins. In the crypto market, the price of bitcoin reflects the other cryptocurrencies. Therefore, our intention is to create prediction models which uses deep learning concepts for bitcoin and to forecast its future price.
Research on the Problem of the Efficiency of Bitcoins: The Energy Costs for the Generation of This Cryptocurrency on a Global Scale

The work is devoted to the problems of efficiency of bitcoins, especially power inputs due to generating of this cryptocurrency. Nowadays the problems of mining power input efficiency seem to have passed from a comparative problem to the problem of existence of this blockchain technology, requiring a different engineering policy or different managerial decisions. The comparative analysis of bitcoins generating power input and the world’s power input has shown that there are almost no trustworthy methods of evaluating input power for blockchain technologies. This problem is solved by application of contemporary methods by setting up big mining companies, located in areas with cheap electric energy and free power balance, possessing their own technological resources and thinking of creating alternative power capacities for mining objectives. At that the state systems start to introduce various limitations for power capacities used for cryptocurrencies including quotas and price privileges. Represented are the results of the analyses of the dynamics of alternation of the main parameters, influencing power consumption at generation of bitcoins on the basis of available literary data and own investigation. Given are the results of calculations regarding bitcoins generation, the costs of consumed electric power per mining of one bitcoin. A more objective index of bitcoin power consumption is suggested, it being correlated with its unit and the unit of heshrate eh showing that Nbtc·LgH duplex is constantly growing despite a decrease in general mass of generated bitcoins, while the relative bitcoin energy consumption decreases with time, still such decrease happens slower than the growth of its market value.
Interactive Application Development Using Augmented Reality Concept for Catalog Tire in Planet Ban Store Pasar Kemis

Currently, the company competes to maximize the role of technology in innovating and developing its business. One of them is PT Planet Ban Pasar Kemis in terms of promoting the latest tire products. In this case, researchers want to change the way Planet Ban promotes which has so far only used a 2D tire catalog by utilizing Android-based AR techniques. This makes it difﬁcult for customers to understand the tire products of PT Planet Ban Pasar Kemis. But with AR, customers only need to scan 2D catalog images with Android to see the tire model into 3D form. The system development method used by researchers is the waterfall method where the sequence and steps of research have been structured by the condition of PT Planet Ban. As for System Analysis and Design, researchers use Uniﬁed Modelling Language or UML methods, while product design uses blenders and unity to apply 2D catalog images into 3D models. To measure the success rate of the application, researchers conducted a testing phase using the black box testing method with results that match the test scenario so that the application can be used according to the needs of the user.
Prediction of PrivyID Application Comments Use as an Electronic Document (e-doc) using the Ensemble Vote method

Indonesia is developing one of the more efficient and effective Financial Technology (Fintek) support services innovations by using electronic documents. The Electronic Document provider business that is used as a reference and utilized by fintech companies is PrivyID. In this study, how is the commentary aspect of using the PrivyID application for digital signature services to become a legal electronic document. Web-based application platforms and mobile applications in the community are indispensable for the use of Electronic Documents developed by PrivyID as a service provider in business and personal transactions that are needed by the community. More in-depth research regarding the Prediction of PrivyID Application Comments in Its Use as an Electronic Document (e-doc) taken from 818 data of PrivyID application users. The research was conducted by combining 3 (three) algorithms (k-Nearest Neighbor, Na¨ıve Bayes, and C4.5) in the Ensembles Vote method which resulted in the best Prediction Comment value with an accuracy of 86.80.
Federated Recommendation with Additive Personalization

Building recommendation systems via federated learning (FL) is a new emerging challenge for advancing next-generation Internet service and privacy protection. Existing approaches train shared item embedding by FL while keeping the user embedding private on client side. However, item embedding identical for all clients cannot capture users' individual differences on perceiving the same item and thus leads to poor personalization. Moreover, dense item embedding in FL results in expensive communication cost and latency. To address these challenges, we propose Federated Recommendation with Additive Personalization (FedRAP), which learns a global view of items via FL and a personalized view locally on each user. FedRAP enforces sparsity of the global view to save FL's communication cost and encourages difference between the two views through regularization. We propose an effective curriculum to learn the local and global views progressively with increasing regularization weights. To produce recommendations for an user, FedRAP adds the two views together to obtain a personalized item embedding. FedRAP achieves the best performance in FL setting on multiple benchmarks. It outperforms recent federated recommendation methods and several ablation study baselines.
Autoencoder-based Joint Communication and Sensing of Multiple Targets

We investigate the potential of autoencoders (AEs) for building a joint communication and sensing (JCAS) system that enables communication with one user while detecting multiple radar targets and estimating their positions. Foremost, we develop a suitable encoding scheme for the training of the AE and for targeting a fixed false alarm rate of the target detection during training. We compare this encoding with the classification approach using one-hot encoding for radar target detection. Furthermore, we propose a new training method that complies with possible ambiguities in the target locations. We consider different options for training the detection of multiple targets. We can show that our proposed approach based on permuting and sorting can enhance the angle estimation performance so that single snapshot estimations with a low standard deviation become possible. We outperform an ESPRIT benchmark for small numbers of measurement samples.
Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation

Self-supervised learning is an increasingly popular approach to unsupervised learning, achieving state-of-the-art results. A prevalent approach consists in contrasting data points and noise points within a classification task: this requires a good noise distribution which is notoriously hard to specify. While a comprehensive theory is missing, it is widely assumed that the optimal noise distribution should in practice be made equal to the data distribution, as in Generative Adversarial Networks (GANs). We here empirically and theoretically challenge this assumption. We turn to Noise-Contrastive Estimation (NCE) which grounds this self-supervised task as an estimation problem of an energy-based model of the data. This ties the optimality of the noise distribution to the sample efficiency of the estimator, which is rigorously defined as its asymptotic variance, or mean-squared error. In the special case where the normalization constant only is unknown, we show that NCE recovers a family of Importance Sampling estimators for which the optimal noise is indeed equal to the data distribution. However, in the general case where the energy is also unknown, we prove that the optimal noise density is the data density multiplied by a correction term based on the Fisher score. In particular, the optimal noise distribution is different from the data distribution, and is even from a different family. Nevertheless, we soberly conclude that the optimal noise may be hard to sample from, and the gain in efficiency can be modest compared to choosing the noise distribution equal to the data's.
FairShare: Blockchain Enabled Fair, Accountable and Secure Data Sharing for Industrial IoT

Industrial Internet of Things (IIoT) opens up a challenging research area towards improving secure data sharing which currently has several limitations. Primarily, the lack of inbuilt guarantees of honest behavior of participating, such as end-users or cloud behaving maliciously may result in disputes. Given such challenges, we propose a fair, accountable, and secure data sharing scheme, $\textit{FairShare}$ for IIoT. In this scheme, data collected from IoT devices are processed and stored in cloud servers with intermediate fog nodes facilitating computation. Authorized clients can access this data against some fee to make strategic decisions for improving the operational services of the IIoT system. By enabling blockchain, $\textit{FairShare}$ prevents fraudulent activities and thereby achieves fairness such that each party gets their rightful outcome in terms of data or penalty/rewards while simultaneously ensuring accountability of the services provided by the parties. Additionally, smart contracts are designed to act as a mediator during any dispute by enforcing payment settlement. Further, security and privacy of data are ensured by suitably applying cryptographic techniques like proxy re-encryption. We prove $\textit{FairShare}$ to be secure as long as at least one of the parties is honest. We validate $\textit{FairShare}$ with a theoretical overhead analysis. We also build a prototype in Ethereum to estimate performance and justify comparable results with a state-of-the-art scheme both via simulation and a realistic testbed setup. We observe an additional communication overhead of 256 bytes and a cost of deployment of 1.01 USD in Ethereum which are constant irrespective of file size.
Modeling Preferences: A Bayesian Mixture of Finite Mixtures for Rankings and Ratings

Rankings and ratings are commonly used to express preferences but provide distinct and complementary information. Rankings give ordinal and scale-free comparisons but lack granularity; ratings provide cardinal and granular assessments but may be highly subjective or inconsistent. Collecting and analyzing rankings and ratings jointly has not been performed until recently due to a lack of principled methods. In this work, we propose a flexible, joint statistical model for rankings and ratings under heterogeneous preferences: the Bradley-Terry-Luce-Binomial (BTL-Binomial). We employ a Bayesian mixture of finite mixtures (MFM) approach to estimate heterogeneous preferences, understand their inherent uncertainty, and make accurate decisions based on ranking and ratings jointly. We demonstrate the efficiency and practicality of the BTL-Binomial MFM approach on real and simulated datasets of ranking and rating preferences in peer review and survey data contexts.