Reviewerly/exHarmony huggingface dataset:
Incorporating speaker embedding and post-filter network for improving speaker similarity of personalized speech synthesis system.

In recent years, speech synthesis system can generate speech with high speech quality. However, multi-speaker text-to-speech (TTS) system still require large amount of speech data for each target speaker. In this study, we would like to construct a multi-speaker TTS system by incorporating two sub modules into artificial neural network-based speech synthesis system to alleviate this problem. First module is to add speaker embedding into encoding module for generating speech while a large amount of the speech data from target speaker is not necessary. For speaker embedding method, in our study, two main speaker embedding methods, namely speaker verification embedding and voice conversion embedding, are compared to deciding which one is suitable for our personalized TTS system. Second, we substituted the conventional post-net module, which is adopted to enhance the output spectrum sequence, to further improving the speech quality of the generated speech utterance. Here, a post-filter network is used. Finally, experiment results showed that the speaker embedding is useful by adding it into encoding module and the resultant speech utterance indeed perceived as the target speaker. Also, the post-filter network not only improving the speech quality and also enhancing the speaker similarity of the generated speech utterances. The constructed TTS system can generate a speech utterance of the target speaker in fewer than 2 seconds. In the future, we would like to further investigate the controllability of the speaking rate or perceived emotion state of the generated speech.
Application of Augmented Reality Technology in Motion Systems in Humans to Support Biology Lessons

Technological progress is overgrowing, which facilitates various aspects of human life, including the world of education, where the application of technology is very supportive of the effectiveness of students in learning compared to only using books as a source of knowledge. Based on the results of interviews at Senior High School 2 Pringsewu, it can be concluded that the limited learning media needed to deliver lessons, especially the subject of Biology of Motion Systems in Humans, to students. And also, the lack of use technology such as smartphones has not been maximized, so Augmented Reality technology is needed. Therefore, this study aims to produce a learning media application based on Android Mobile using augmented reality technology. Furthermore, it focuses on Motion System Biology in Humans, namely the Framework expected with Augmented Reality technology to help introduce System Biology subjects interactively. Therefore, movement in Humans is the Framework and also innovations in the world of education today.
Chapter 4. Designing for Young Learners

Chapter 4 of Library Technology Reports  (vol. 55, no. 4), “Librarians as Online Course Designers and Instructors,” edited by Lucy Santos Green As K–12 students and teachers transition to online and blended learning environments, librarians will be a critical source of support and guidance. In chapter 4 of Library Technology Reports (vol. 55, no. 4), “Librarians as Online Course Designers and Instructors,” we discuss the transformative potential of online and blended learning to personalize K–12 students’ learning. We also share practical guidelines for creating and curating online resources for students and teachers.
An Experimental Model of the System of Children’s Musical Enlightenment: The Initial Level

Culture today is experiencing a state defined by most researchers as “critical”, “threshold”, “moral or spiritual apocalypse”. One of the main problems is the devaluation of spiritual values, which leads to the destruction of the connection between society and academic culture. The multi-channel and all-consuming system of mass communications (media) has begun to play an increasingly decisive role in modern society. As an alternative to spontaneous dissemination of information flows, the concepts of “media culture” and “media art” arise, which makes it possible to combine aesthetic searches with modern media, the best opportunities of which should be used in the field of academic culture dissemination. That is why the issues of artistic enlightenment, professional dissemination of academic culture in the new information space, the focus of spiritual, moral and aesthetic guidelines, as well as the training of potential musical educators within the initial professional stage are so relevant today. The proposed experimental model of children’s musical enlightenment is aimed at teaching the popularization of academic music, disseminating acquired knowledge about music in the context of related arts, on the basis of a unified three-stage educational music enlightening system. The purpose of the presented project is to develop and implement the model on the basis of the existing primary state music education (of preschool, school and post-school professionally-oriented levels) using the achievements of media technologies. The presented model will contribute to the most harmonious development of individual creative abilities of students, the formation of their active life position, and the provision of their primary professional and activity competencies in the field of musical enlightenment, which can help optimize primary art education.
Predictive Scaling for Elastic Compute Resources on Public Cloud Utilizing Deep Learning based Long Short-term Memory

The cloud resource usage has been increased exponentially because of adaptation of digitalization in government and corporate organization. This might increase the usage of cloud compute instances, resulting in massive consumption of energy from High performance Public Cloud Data Center servers. In cloud, there are some web applications which may experience diverse workloads at different timestamps that are essential for workload efficiency as well as feasibility of all extent. In cloud application, one of the major features is scalability in which most Cloud Service Providers (CSP) offer Infrastructure as a Service (IaaS) and have implemented auto-scaling on the Virtual Machine (VM) levels. Auto-scaling is a cloud computing feature which has the ability in scaling the resources based on demand and it assists in providing better results for other features like high availability, fault tolerance, energy efficiency, cost management, etc. In the existing approach, the reactive scaling with fixed or smart static threshold do not fulfill the requirement of application to run without hurdles during peak workloads, however this paper focuses on increasing the green tracing over cloud computing through proposed approach using predictive auto-scaling technique for reducing over-provisioning or under-provisioning of instances with history of traces. On the other hand, it offers right sized instances that fit the application to execute in satisfying the users through on-demand with elasticity. This can be done using Deep Learning based Time-Series LSTM Networks, wherein the virtual CPU core instances can be accurately scaled using cool visualization insights after the model has been trained. Moreover, the LSTM accuracy result of prediction is also compared with Gated Recurrent Unit (GRU) to bring business intelligence through analytics with reduced energy, cost and environmental sustainability.
UMA ANÁLISE INTEGRATIVA DE FRAMEWORKS DE IMPLEMENTAÇÃO DA GESTÃO DO CONHECIMENTO: PROPOSTA DE UMA AGENDA DE PESQUISA

Objective: This article analyzes the characteristics of the Knowledge Management (KM) implementation frameworks published in high impact magazines, and seeks to answer the following research question: How are Knowledge Management implementation frameworks characterized?Design/methodology/approach: Through a literature review, ten Knowledge Management implementation frameworks were identified. These were individually analyzed and structured as to their dimensions and variables, as well as the pillars and processes of KM.Results: Individual analysis of each framework led to the generation of an integrative framework that guides the direction of research in the field. The frameworks are also classified as descriptive and prescriptive. Analysis of the frameworks also revealed gaps in measuring processes, measuring results, and solutions that envision IT as a fundamental structure for KM. In addition to the integrative framework, this article also proposes a research agenda for empirical studies and the application of Knowledge Management frameworks. Limitations/implications of the research: This study was limited to KM implementation frameworks. Therefore, articles with other structures were not included in the analysis. Future studies will be able to carry out an overview with different perspectives, more generic or more specific, analyzing KM processes and their mechanisms individually.Practical implications: For managers, this study promotes discussion on the applicability of implementation frameworks, invoking more pragmatic studies that assist in the measurement, application, and conducting of KM processes.Theoretical implications: This article contributes to research in the area of KM by bringing together frameworks from different sectors, under the classification of KM processes and pillars, allowing the identification of gaps that have not yet been filled and reflections on why this topic of research has not been taken up by professionals in the field.Originality/value: There is a lack of options adapted to certain sectors, such as the banking segment. There is also a need to incorporate into the frameworks the use of KM maturity models and indicators for process metrics, which will be qualitative and quantitative, as well as measurements of financial and non-financial results. The article manages gives an overview of these gaps.
FEATURES OF THE IMPLEMENTATION OF A SOCIAL PROJECT IN THE FIELD OF ECOLOGY

The features of the implementation of a social project in the field of ecology are studied, which are associated with the selection of the target group, with their socialization in the system of measures aimed at the development of the cognitive, emotional and volitional sphere in the conditions of the ecological path.
Child-directed Listening: How Caregiver Inference Enables Children's Early Verbal Communication.

Author(s): Meylan, Stephan C.; Foushee, Ruthe; Bergelson, Elika; Levy, Roger | Abstract: How do adults understand children's speech? Children's productions over the course of language development often bear little resemblance to typical adult pronunciations, yet caregivers nonetheless reliably recover meaning from them. Here, we employ a suite of Bayesian models of spoken word recognition to understand how adults overcome the noisiness of child language, showing that communicative success between children and adults relies heavily on adult inferential processes. By evaluating competing models on phonetically-annotated child language from the Providence corpus, we show that adults' recovered meanings are best predicted by prior expectations fitted specifically to the child language environment, rather than to typical adult-adult language. After quantifying the contribution of this child-directed listening over developmental time, we discuss the consequences for theories of language acquisition, as well as the implications for commonly-used methods for assessing children's linguistic proficiency.
PERJALANAN SENIMAN DI DUNIA VIRTUAL MELALUI E-GOVERNMENT

The virtual era requires artists to be more developed in their work and also be able to apply their work to virtual media. UPT Taman Budaya as a place where artists show their existence which will later be uploaded through the YouTube account of UPT Taman Budaya, namely Cak Durationm, in this case UPT Taman Budaya helps artists to continue to develop through virtual event performances in the Cak Duration building. By using direct observation techniques in the field, the author is assisted by employees. The author as an internship student at UPT Taman Budaya participates in helping virtual event activities during the internship, and the author also provides suggestions and solutions to promote the virtual event through promotional advertisements on social media belonging to UPT Taman Budaya
Ensemble Federated Adversarial Training with Non-IID data

Despite federated learning endows distributed clients with a cooperative training mode under the premise of protecting data privacy and security, the clients are still vulnerable when encountering adversarial samples due to the lack of robustness. The adversarial samples can confuse and cheat the client models to achieve malicious purposes via injecting elaborate noise into normal input. In this paper, we introduce a novel Ensemble Federated Adversarial Training Method, termed as EFAT, that enables an efficacious and robust coupled training mechanism. Our core idea is to enhance the diversity of adversarial examples through expanding training data with different disturbances generated from other participated clients, which helps adversarial training perform well in Non-IID settings. Experimental results on different Non-IID situations, including feature distribution skew and label distribution skew, show that our proposed method achieves promising results compared with solely combining federated learning with adversarial approaches.
Pelatihan penggunaan podcast dan audiobook untuk meningkatkan kemampuan berbicara peserta kursus di LKP-MEC Namlea

Speaking skills are vital skills for the LKP MEC course participants to meet the ir needs in facing the world of the job. The training on using podcasts and audiobooks to improve speaking skills took place for eight meetings. In the training process, the participants were first guided on the procedures for accessing and using podcasts or audiobooks through projector media to provide knowledge about information and communication technology to improve speaking skills. Furthermore, podcasts and audiobooks can also be accessed through each trainee's device/mobile phone so that they can listen to English conversations independently. Conversational materials on podcasts and audiobooks included in authentic materials that help participants gain knowledge of the language. To hone speaking skills in the training process, each participant imitates the speaker's way of speaking, accent, dialect, and pronunciation. In addition, they record vocabulary and sentences and practice speaking in pairs using each language knowledge they acquire consistently. As many as 15 people or 75 per cent of the participants who had attended the training were declared competent in mastering speaking skills and fluency in having dialogue using a variety of spoken English properly and correctly until they were finally accepted for an internship at the Grand Sarah Hotel of Namlea.
Web-based Application Forecasting of Components and Tools for Practicum in Telecommunication Engineering Laboratory/ Workshop of State Polytechnic of Sriwijaya

State Polytechnic of Sriwijaya is an Indonesian vocational institution that incorporates practical learning into all of its existing study programs. In each laboratory of the study program, students will find the supporting components and resources they need for practicum. Telecommunication engineering is one of them. Currently, the component retrieval and tool borrowing system is still manual, both in the retrieval and return stages, by filling out the given hard-file form sheet. Therefore, a web-based inventory monitoring application is required for laboratory technicians. The creation of this application implements the Prototype Method. The implementation of this method aims to make the application that is created to fit the needs of the user. This system helps laboratory technicians and Telecommunication Engineering students in the process of component retrieval and tool borrowing to be more organized and scheduled and can store reports in the form of soft-files. This information system can also be used by students to monitor the number of components and tools available in real-time. This application are also equipped with forecasting the number of practicum components to kwowing the needs of student practicum in the next year. Forecasting was created using the Weighted Moving Average method. Black box testing is carried out in each menu on the application and gives valid results so that the application can be used.
Research on multi-product artificial intelligence recommendation

With the rise of diversified stores, the relationship between consumer characteristics and commodities affects consumer orientation. Stores are in urgent need of the application of artificial intelligence, the ultimate purpose of which is to help diversified products make accurate consumption recommendations. 565 questionnaire survey data of a cultural and creative store in Fuzhou, China, this paper uses the Apriori association rule algorithm to find out the consumption relevance between goods, and then uses the C5.0 classification algorithm to set consumers' purchase decision-making behavior as the target variable, and three characteristic factors affecting consumers' basic attributes, behavior characteristics and purchase preferences as independent variables. The results showed that coffee beans (10%) were a boost to drinks (85%), tea (10%) to art tea sets (84%), wood carvers (32%) to home furnishings (84%), plants (10%) to incense burrs (80%), and toys (26%) to stationery (78%).
Adaptations of students to on-line format in of higher learning

The article considers new requirements for higher education, the improvement of modern educational technologies such as training courses and distance learning technologies, and the study and identification of difficulties of students during the crisis caused by the pandemic. To date, a certain amount of knowledge important for formulating and solving the problem of adaptation of students during online education has been accumulated. Stating the active development of various aspects of the problem that we are investigating, we can conclude that the problem of adaptation of students to online learning using distance learning technologies and the difficulties that arise in this case have not yet become the subject of a special study. Questions about the effectiveness of distance or blended learning and the role of personal qualities of teachers and curators in the educational process are determined by primary importance of the problems of education and training at this stage of development of modern Ukraine. The article analyzes and identifies the socio-psychological state of the students and the problems they encounter during distance learning through questionnaires using Google Forms, and draws conclusions on how to solve these problems.
NeRV: Neural Representations for Videos

We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. Given a frame index, NeRV outputs the corresponding RGB image. Video encoding in NeRV is simply fitting a neural network to video frames and decoding process is a simple feedforward operation. As an image-wise implicit representation, NeRV output the whole image and shows great efficiency compared to pixel-wise implicit representation, improving the encoding speed by 25x to 70x, the decoding speed by 38x to 132x, while achieving better video quality. With such a representation, we can treat videos as neural networks, simplifying several video-related tasks. For example, conventional video compression methods are restricted by a long and complex pipeline, specifically designed for the task. In contrast, with NeRV, we can use any neural network compression method as a proxy for video compression, and achieve comparable performance to traditional frame-based video compression approaches (H.264, HEVC \etc). Besides compression, we demonstrate the generalization of NeRV for video denoising. The source code and pre-trained model can be found at https://github.com/haochen-rye/NeRV.git.
Book writers and article contributors celebrated

PEKAN, 13 November 2020 - Universiti Malaysia Pahang (UMP) Press published four new books entitled Harimau Yang Mengaum, Individual Field Project Guideline – Bachelor of Business Engineering with Honours, Module Strength of Materials 1, and Sensor and Instrumentation Systems.
IMPLEMENTASI SISTEM INFORMASI MANAJEMEN KEPEGAWAIAAN (SIMPEG) DI KANTOR KEMENTERIAN AGAMA KABUPATEN MALUKU TENGAH

This study aims to determine the implementation of the Personnel Management Information System (SIMPEG) at the Office of the Ministry of Religion of Central Maluku Regency and to find out what obstacles arise in implementing the SIMPEG. This research is descriptive using a qualitative approach so that to support this research activity, data collection from informants is carried out. Data analysis is preceded by collecting and collecting data or information in the field, after that it is classified according to the type and group, then it is analyzed or interpreted qualitatively. Based on the results of the analysis, it is known that the implementation of SIMPEG has made a major contribution to the implementation of the functions of the Ministry of Religion Office of Central Maluku Regency, but in implementing SIMPEG it can be said that it is not optimal, because it is still experiencing several obstacles and problems caused by several shortcomings in its implementation.
Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphs

We present a generalization of Transformers to any-order permutation invariant data (sets, graphs, and hypergraphs). We begin by observing that Transformers generalize DeepSets, or first-order (set-input) permutation invariant MLPs. Then, based on recently characterized higher-order invariant MLPs, we extend the concept of self-attention to higher orders and propose higher-order Transformers for order-$k$ data ($k=2$ for graphs and $k>2$ for hypergraphs). Unfortunately, higher-order Transformers turn out to have prohibitive complexity $\mathcal{O}(n^{2k})$ to the number of input nodes $n$. To address this problem, we present sparse higher-order Transformers that have quadratic complexity to the number of input hyperedges, and further adopt the kernel attention approach to reduce the complexity to linear. In particular, we show that the sparse second-order Transformers with kernel attention are theoretically more expressive than message passing operations while having an asymptotically identical complexity. Our models achieve significant performance improvement over invariant MLPs and message-passing graph neural networks in large-scale graph regression and set-to-(hyper)graph prediction tasks. Our implementation is available at https://github.com/jw9730/hot.