Reviewerly/exHarmony huggingface dataset:
Development Of Information Systems Orphanage (E-Orphanage) Web-Based

This study aims to develop a Web-Based Orphanage Information System (e-Panti) at the Silaturrahmi Orphanage in Makassar City. The information system for the Orphanage (e-orphanage) is used as a tool in conveying information on the Silaturrahmi Orphanage in Makassar City to people who want to know the official information of the Silaturrahmi Orphanage and can make it easier for the public to donate online. This research belongs to the type of Research and Development (R&D) research. The information system is designed using a prototyping development model. The research subjects in the development of this system are the caretaker of the orphanage, the orphanage and the community. The data analysis technique tested uses the ISO 25010 testing standard which includes aspects of Functionality Suitability, Performance Efficiency, Portability and Usability. Based on the results of the research, a Web-Based Orphanage Information System (e-orphanage) was obtained. In testing the Functionality Suitability aspect in the very feasible category, the Performance Efficiency aspect using the Gtmetrix class in the good category, the Portability aspect using the browserstack.com software, where the system is said to have fulfilled the portability aspect because this system can run well without errors and in good category, the aspect Usability in the very good category. So it can be concluded that the Web-Based Orphanage Information System (e-orphanage) is suitable for use at the Silaturrahmi Toddopuli Orphanage in Makassar City and is in accordance with the ISO 25010 feasibility standard.
Beyond Bitcoin: The Economics of Digital Currencies and Blockchain Technologies (Chapter 8: Conclusions)

Concluding thoughts and outlook for the future of the technologies affording digital currencies, cryptocurrencies, blockchain and smart contracts. For Chapter 1 (Introduction), see https://ssrn.com/abstract=3135021. For Chapter 2 (Means of Exchange: Ever-present Competition), see https://ssrn.com/abstract=3135028.For Chapter 3 (Platform-based Currencies), see https://ssrn.com/abstract=3135030. For Chapter 4 (Bitcoin and Emergence of Cryptocurrencies), see https://ssrn.com/abstract=3135043. For Chapter 5 (The Rich Land of Crypto), see https://ssrn.com/abstract=3135057. For Chapter 6 (Smart Contracts and Blockchain), see https://ssrn.com/abstract=3894110.For Chapter 7 (Enterprise Blockchains), see https://ssrn.com/abstract=3894117.For Chapter 8 (Conclusions: Future Full of Possibilities), see https://ssrn.com/abstract=3894118.
A complete change in attitude towards reading with the results to show for it

In 2013 St Thomas Aquinas Roman Catholic School, part of the Lumen Christi Multi-Academy Trust, invested in Accelerated Reader (AR) and Star Reading (SR) ...
Representations of Italian Populism and Immigration on Facebook: A Comparison of the Posts by Luigi Di Maio and Matteo Salvini (2014-2018)

The investigation is located within a research program which, using different triangulation models, explores the representations of Italian populism. In our case our interest is centered on social media. Although social media are the preferred vehicle of populist communication, their role has only begun to be studied in recent years. We are part of this new attention by focusing on both the communication and its impact on followers of two populist leaders, the politically transversal oriented Luigi Di Maio, and the far-right oriented Matteo Salvini. In line with what is stated by the theory of social representations, our starting point is that language is opaque and capable of promoting and directing inferences through specific linguistic markers. Our study focuses on how the immigration issue and populism are represented on Facebook by the two populist leaders. Thanks to data and methodologies triangulation models, their posts were analyzed for their language patterns and followers' approval. We noted a greater focus on immigration by Salvini; his immigration posts were also widely popular among his followers and more negative, compared to those of Di Maio. For Salvini the results are indicative of a radical-right-wing angry-populism characterized by a charismatic leadership strongly opposed to immigration. On the other hand, Di Maio's populism appears more nuanced on this subject and with less involvement of followers.
КІТАПХАНА ІСІ САЛАСЫНДАҒЫ ӘЛЕМДІК ТӘЖІРИБЕЛЕР

Innovations in library activities that meet modern requirements. The role of the library in the era of globalization. Review of the world experience in library and information services. Quantitative changes occurring in the world's libraries. Conducting a comparative analysis of foreign libraries. Problems of introduction of new technologies in the field of librarianship.
[Keynotes - 3 abstracts]

Memory state-preserving fault tolerance (MPFT) enables a running process to continue to run without disruption in spite of any failure of the underlying hardware machine or operating system, and is built on the idea of continuous virtual machine migration and epoch-based execution model. Although there has been a commercial implementation of MPFT for some time, its real-world use is not as wide-spread as expected, mainly because its serious performance penalty. In this talk, I will first go over the main limitations of existing MPFT implementations, and then describe an open-source implementation of MPFT that not only supports all basic MPFT functionalities, but also removes all of these limitations.
Sistem Transmisi Dan Pengoprasian Video Conference Kominfo Provinsi Jambi

In the current atmosphere there are many methods that can be used in a learning process and in general, a learning process or in a room by involving participants in its implementation. One of the methods used which is often used is the field work practice (PKL) method. Namely learning by going directly to an agency in the world of corporate and government work. This field work practice (PKL) is one of the programs of the Telkom Institute of Technology which aims to provide students with work experience before going directly to work. And can take the knowledge gained in implementing field work practices (PKL) and can apply it in the world of work. In the implementation of field work practices (PKL) which was carried out on August 10, 2020 to September 25, 2020, the authors carried out this field work practice at the Department of Communication and Information, Jambi Province, as for the activities carried out in the implementation of the field work practice tasks, namely making preparations in the video converence process. carried out within the scope of the Jambi provincial government, and in the implementation process using the zoom metting application and can also use google meet. This field work practice report discusses the challenges of activities carried out during field work practice activities at Diskominfo Jambi province, especially installation activities and video converence monitors. Keywords: Conference, zoom, google meet
A Novel Evaluative Method of the Subject “Education and Society” of the Autonomous University of the Andes, Ecuador, based on Plithogenic Numbers

The subject “Education and Society” is part of the syllabus of the “Basic Education” program of the Autonomous University of the Andes, Ecuador. This subject has the complexity of linking different aspects of society, some of them not free from contradictions among themselves, and imprecision and uncertainties in assessing students’ performance. For this reason, the concepts and aspects to be evaluated on this subject are represented through the use of plithogenic numbers. Plithogenic sets were defined to model concepts arising from the dynamic interaction among other simpler ones, which may have contradictions with each other and include neutralities or indeterminacies. This research aims to propose a novel method for the evaluation of the subject “Education and Society” through the use of plithogenic numbers and their operators. The lecturers will be able to perform the evaluations with the use of natural language; in the same way, the results will be given using a linguistic scale, which will facilitate the understanding and representation of the evaluations. On the other hand, plithogenic numbers will allow capturing the complexity, imprecision, and uncertainty that lecturers may face in their evaluations.
Design of Projector Correction system based on RK3399

In real life, the projector projection picture is not flat, there is a certain deformation. Traditional method is to adjust the position of the projector manually, but it is not convenient in real scenes, and the two projectors are simply corrected by the traditional method, so the projection effect in the overlapping area is not ideal. In recent years, there is also a lot of research on how to do the work of the projector correction, and put forward a lot of solutions, but most of them are very tedious. This paper presents a correction method using embedded device RK3399 combined with OpenCV software, which can manually and automatically correct the projection picture of one or two projectors by software control, and deal with the projection overlap area, which makes the visual effect of projection better and the operation convenient. It can be widely used in the projector system of prisons and command centers.
Weapon and Object Detection Using Mobile-Net SSD Model in Deep Neural Network

Abstract: The plan is to establish an integrated system that can manage high-quality visual information and also detect weapons quickly and efficiently. It is obtained by integrating ARM-based computer vision and optimization algorithms with deep neural networks able to detect the presence of a threat. The whole system is connected to a Raspberry Pi module, which will capture live broadcasting and evaluate it using a deep convolutional neural network. Due to the intimate interaction between object identification and video and image analysis in real-time objects, By generating sophisticated ensembles that incorporate various low-level picture features with high-level information from object detection and scenario classifiers, their performance can quickly plateau. Deep learning models, which can learn semantic, high-level, deeper features, have been developed to overcome the issues that are present in optimization algorithms. It presents a review of deep learning based object detection frameworks that use Convolutional Neural Network layers for better understanding of object detection. The Mobile-Net SSD model behaves differently in network design, training methods, and optimization functions, among other things. The crime rate in suspicious areas has been reduced as a consequence of weapon detection. However, security is always a major concern in human life. The Raspberry Pi module, or computer vision, has been extensively used in the detection and monitoring of weapons. Due to the growing rate of human safety protection, privacy and the integration of live broadcasting systems which can detect and analyse images, suspicious areas are becoming indispensable in intelligence. This process uses a Mobile-Net SSD algorithm to achieve automatic weapons and object detection. Keywords: Computer Vision, Weapon and Object Detection, Raspberry Pi Camera, RTSP, SMTP, Mobile-Net SSD, CNN, Artificial Intelligence.
Nonlocal sensing of temporal delay in dispersive links using time-energy entangled photons

We employ electro-optic frequency mixing to achieve precision measurement of temporal delay between dispersive fiber-optic links by taking advantage of high-dimensional frequency-bin entanglement in a nonlocal sensing geometry.
Location, Location: Enhancing the Evaluation of Text-to-Speech synthesis using the Rapid Prosody Transcription Paradigm

Text-to-Speech synthesis systems are generally evaluated using Mean Opinion Score (MOS) tests, where listeners score samples of synthetic speech on a Likert scale. A major drawback of MOS tests is that they only offer a general measure of overall quality-i.e., the naturalness of an utterance-and so cannot tell us where exactly synthesis errors occur. This can make evaluation of the appropriateness of prosodic variation within utterances inconclusive. To address this, we propose a novel evaluation method based on the Rapid Prosody Transcription paradigm. This allows listeners to mark the locations of errors in an utterance in real-time, providing a probabilistic representation of the perceptual errors that occur in the synthetic signal. We conduct experiments that confirm that the fine-grained evaluation can be mapped to system rankings of standard MOS tests, but the error marking gives a much more comprehensive assessment of synthesized prosody. In particular, for standard audiobook test set samples, we see that error marks consistently cluster around words at major prosodic boundaries indicated by punctuation. However, for question-answer based stimuli, where we control information structure, we see differences emerge in the ability of neural TTS systems to generate context-appropriate prosodic prominence.
One-Shot Learning with Memory-Augmented Neural Networks Using a 64-kbit, 118 GOPS/W RRAM-Based Non-Volatile Associative Memory

Learning from a few examples (one/few-shot learning) on the fly is a key challenge for on-device machine intelligence. We present the first chip-level demonstration of one-shot learning using a 2T-2R resistive RAM (RRAM) non-volatile associative memory (AM) as the backend of memory-augmented neural networks (MANNs). The 64-kbit fully integrated RRAM-CMOS AM core (0.2 mm2 at 40 nm node) enables long-term feature embedding and retrieval, demonstrated in a challenging 32-way one-shot learning task using Omniglot dataset. Using only one example per class for 32 unseen classes during on-chip learning, our AM chip achieves ~72% measured inference accuracy on Omniglot as the first chip accuracy report compared to software accuracy (~82%), while reaching 118 GOPS/W for in-memory L1 distance computation and prediction.
Sentiment Analysis of Public Opinion Related to Rapid Test Using LDA Method

In 2020 the world will be shocked by an outbreak of a disease that has developed tremendously. This disease is the Coronavirus. The Indonesian government, in overcoming conducted a Rapid early detection test in the spread of the Coronavirus. The steps of the Indonesian government have received rejection in several areas because people consume hoax news on social media. Indonesians widely use Twitter in conversations about the Coronavirus. Previous research was carried out using large-scale data, which affected the performance of the topic extraction method. The classification used resulted in poor accuracy using LDA to find the probability of topics in existing documents. LDA excels in large-scale data processing and is more consistent in generating the topic proportion value and word probability. Aspect-based sentiment analysis on public opinion regarding the rapid test on Twitter using LDA can determine aspects and public opinion on the rapid test. The test results of this study obtained 7000 tweets, four aspects of the results of topic using LDA, and getting the best accuracy using the RBF kernel by 95%. The sentiment of the Indonesian people towards the Rapid test is positive, with 4,305 sentiments.
Integrating Non-Functional Requirements for Marginalized Users in the Design of IoT Systems

A Journal article by Dr. Leah Mutanu, a Lecturer in Information Systems Technology at the School of Science and Technology in USIU- Africa
A generalization of cyclic code equivalence algorithm to constacyclic codes

Recently, a new algorithm to test equivalence of two cyclic codes has been introduced which is efficient and produced useful results. In this work, we generalize this algorithm to constacyclic codes. As an application of the algorithm we found many constacyclic codes with good parameters and properties. In particular, we found 22 new codes that improve the minimum distances of best known linear codes (BKLCs).
Data Mining-based Fault Detection Method for Distributed Power Generation Systems

Traditional fault detection methods for power generation systems use cen-tralized fault processing analysis, which leads to long accuracy and responsetime of fault detection. To address these problems, a data mining-baseddistributed power generation system fault artificial intelligence detectionmethod is studied. The depth-first search tree algorithm is used to divide thegrid of distributed generation system. The network structure is modified tolocate fault zones by processing anomaly mining of the system data aftergrid division. The combination of fuzzy logic and wavelet singular entropyis used to complete the detection and identification of system faults. Throughsimulation experiments, it is verified that the response time of the detectionmethod is only 0.016 s, and its detection error rate and false negative rate are1.23% and 1.25%, which are far lower than other methods.
A Multi-Level Adaptive Loot Box Recommendation System for Video Games

Loot Box is one of the most important economies in video Games especially in massively multiplayer online (MMO) games. With Free to Play games growing in popularity, loot boxes became a primary monetisation technique. Loot boxes consist of virtual items that are consumed by players. The items vary from a new weapon to a skin. Loot boxes are one of the reward elements that create continuous interest among players to play games. On the other hand, publishers can generate revenues even after the game is released. These advantages have made loot boxes quite popular in the video game industry. Loot boxes have become one of the monetizing methods for video games. An online multiplayer accommodates different types of players and also the design incorporates opportunities for different types of fun. Considering this design philosophy, defining loot boxes with predefined items in it may cause disappointments to players as the item does not fit for them. In addition to causing negative engagement, such scenarios will not favor purchases that affect the game economy. Recommending loot items adaptive to the traits of the players and their play style could be a desired solution. We recommend a novel approach based on machine learning algorithms to solve this problem. The approach couples to Machine learning solutions together to generate loot item suggestions. The system uses Random Multimodal Deep Learning based classification approach to identify player profile and classification. The system identifies the player trait and the play style using sentiment analysis. The model employs Bartle player classification to evaluate the results. The multi-level approach helps the system to deliver results with minimal resource utilization.
An Empirical Evaluation of Artificial Intelligence Algorithm for Hand Posture Classification

During the past decade, an intensive growth of Human–Computer Interaction (HCI) has been evolved. It includes, but is not limited to, Virtual Reality, Augmented Reality, Voice Control Systems, EEG-based systems, etc. Primarily, HCI is the hybridization of Information & Communication Technology and Bio-metric. Besides, the biometric inputs like Human Face, Voice, Physical actions, EEG signals, etc. are cascaded with the computing module for robust and intelligent decision making. The hand postures are one of the most common biometric for system automation & feedback. In this study, exhaustive empirical research of the machine learning algorithm for hand posture classification has been established. In this connection, a recent dataset, ‘Mocap Hand Postures Data Set,’ has been opted to employ the different variants of the machine learning algorithm. To the best of the knowledge, the exhaustive comparative study on the said dataset is found to be deficient in the literature. Primarily the principal focus is to identify the best candidate for real-time hand posture classification. Moreover, the performance of each method has been rigorously measuring as a function of training accuracy, testing accuracy, prediction speed, and training time. Besides, the percentage recognition of each corresponding class is illustrated using the respective confusion matrix.
Framework of Mobile Game Design as an Assistive Technology for Children with Motor Disabilities

Computer games have emerged in the past decade as potential media beyond entertainment. Despite its popularity, game accessibility remains a major concern of various researchers. Children population with motor disabilities is a potential target for developing entertainment or therapeutic support games due to their interest to play. This paper presents: (1) a framework for mobile games for children with motor disability using simple hand postures and (2) Xgboost decision tree as a hand posture recognizer (98.48 percent training accuracy and 96.76 percent testing accuracy) as a prototype of hand posture-based commands as assistive technology to interact with games.
Alert System for Fall Detection

Abstract: Healthcare is one of the most important industries, yet new ideas must travel a long way before being fully adopted due to its complexity, scope of duty, and stringent laws. The Internet of Things (IoT) may be the key to resolving healthcare challenges. The Internet of Things (IoT) has a lot of potential in healthcare, but it's still in its early stages. With the advancement of medical IoT, new possibilities for telemedicine, remote monitoring of a patient's status, and much more will emerge. Falling is a significant health danger for the elderly. If the problem is not detected in a timely manner, it can result in the death or impairment of the elderly, lowering their quality of life. Falls are a major public health concern for the elderly around the world. When it comes to old age, we must keep an eye on our loved ones to ensure their health and safety. It is therefore critical to determine if an elderly person has fallen so that help can be provided promptly. Proposing a person fall detection system based on a wearable device for detecting the falls of people in every situation, which takes advantage of lowpower wireless sensor networks, smart devices, and analyses human body motions. The system detects movement using an accelerometer and a gyro sensor. The sensor is wired to a microprocessor, which transmits the acceleration data continuously. Fall detection and sudden movement changes in the person would be monitored by the system. The sensors are getting values from a quick movement shift with shock in the system. When a person falls and becomes unconscious, the system determines whether the person has indeed fallen. If the person has truly fallen, the system will send an alert to the caregivers and sound an alarm to alert anyone nearby. When the system detects that a person has fallen, it immediately sends an alert to the individual's care takers. It is an IoT-based fall detection system that assists people by telling their caregivers about their fall so that quick attention may be drawn to the situation and essential actions can be taken to save the person who has fallen. Keywords: Threshold Based Fall Detection, Arduino, Bi-Axial, Accelerometer, Gyroscope,