Reviewerly/exHarmony huggingface dataset:
Exploring Automated Voice Casting for Content Localization Using Deep Learning

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Casting voice-actors to dub source language content into a target language—known as voice casting—consists largely of a manual workflow that could benefit immensely from increased levels of automation. Recent advancements in deep learning architectures for sequential data processing are providing the needed impetus to the realization of various AI-enabled audio-processing workflows. Specifically, applications such as speaker verification and speech synthesis have been gaining immense traction due to the advent and maturity of recurrent neural networks. We explore the viability of leveraging advancements in deep learning for text-independent speaker verification (TI-SV) for use in computer-aided voice casting. To this end, we propose and develop an automated voice-casting tool that uses similarity scores generated from neural network embeddings—from a robust autoencoder model trained for the task of TI-SV—to rank voiceover artists across different languages in voice-casting process. To evaluate the dexterity of the proposed approach, we conduct a subjective study emulating a simplified voice-casting process on actual voice-testing kits (dubbing auditions) from our content. We also use casting decisions from casting experts to further evaluate the tool as well as the subjectivity involved in the voice-casting process. We achieve promising results for the automated tool and prove that it could be a viable approach to automating the voice-casting process and warrants further exploration</i> .
Adaptive Mutual Supervision for Weakly-Supervised Temporal Action Localization

Weakly-supervised temporal action localization aims to localize actions from untrimmed long videos with only video-level category labels. Most previous methods ignore the incompleteness issue of Class Activation Sequences (CAS), suffering from trivial detection results. To tackle this issue, we propose a novel Adaptive Mutual Supervision (AMS) framework with two branches, where the base branch detects the most discriminative action regions, while the supplementary branch localizes the less discriminative action regions through an adaptive sampler. The sampler dynamically updates the inputs for the supplementary branch using a sampling weight sequence negatively correlated with the CAS from the base branch, thus encouraging the supplementary branch to localize the action regions underestimated by the base branch. To promote mutual enhancement between two branches, we further construct mutual location supervision. Each branch adopts the location pseudo-labels generated from the other branch as the localization supervision. By alternately optimizing two branches for multiple iterations, we progressively complete action regions. Extensive experiments on THUMOS14 and ActivityNet1.2 demonstrate that the proposed AMS method significantly outperforms state-of-the-art methods.
Media information technology games based on local cultural content

Abstract This study aims to determine the effect of information technology game media on cultural content on learning motivation of Yogyakarta PGRI University students. This research was conducted because there were still some students who had low motivation to learn, because some lecturers were inconsistent in the use of information technology game media. This study uses quantitative methods with a student population with a sample of 74 students. This study uses questionnaire instrument data collection techniques to obtain data media games and information and learning motivation. The analysis technique used in this study is simple regression analysis. The results of this study concluded that there was a positive and significant influence between technology and media games based on local cultural content on the learning motivation of Civics Education students class of 2019/2020. The results obtained with a value of rxy 0800&gt; rtable 0.227 which means that the information technology game media based on local cultural content can affect student motivation.
The preliminary of Design and Movement of Remotely Operated Vehicle (ROV)

Abstract The purpose of this study is to design a mini Remote Operated Vehicles (ROV) which based on remote control underwater robot, and test its movements, the longitudinal and vertical movements in calm water. This research is still a preliminary research to find the best design for the ROV and its movement. The Mini ROV design is integrated into several design processes, making designs by using software, planning the selection of each component used in the mini ROV, designing mechanical construction, designing electronic construction, and the final stage is the integration of the entire design process. The results of this research decided that the best design of mini ROV is named “AF-150114. The ROV robot is made with a total dimension of 65 cm in length, 50 cm in width, and 25 cm in height. In the ROV body is made using fiberglass base material that every gap is coated with a seal so that the ROV impermeability can be maintained properly. In movement system, ROV uses two leaves of propeller to move forward and backward while using a ballast pump system to submerge and lift the ROV.
Identifying Versions of Libraries used in Stack Overflow Code Snippets

Stack Overflow is a popular question and answer platform where developers share technical issues in the hope of receiving answers with potential solutions. The latter may include code snippets making use of library versions that have long since been succeeded by newer ones. Other developers finding such a snippet at a later point in time may be unaware of its outdatedness unless mentioned in a comment. Furthermore, it can be difficult to integrate the snippet without knowing the exact version of the library it is referencing. In this paper, we propose an automated approach to identifying ranges of Maven library versions that might have been used in a Java snippet on Stack Overflow. We use a prototype implementation of the approach to assess the overall outdatedness of Stack Overflow snippets with respect to the latest version of each referenced library available from Maven. We found a considerable number of snippets that use outdated library versions, which suggests that developers should be careful when adopting solutions from Stack Overflow.
A Transparently-Secure and Robust Stock Data Supply Framework for Financial-Technology Applications

Recently, program trading has become the mainstream of financial information technology. The current FinTech applications mainly encounter three technical issues, including scalability, long-time retrieval, and security. Many small-and-medium companies seek economical solutions, while the three above properties are still met. In this paper, we propose Send-and-Subscribe (SaS) framework, aiming at providing a secure and robust financial stock data retrieval repository. In our survey to the related industries, the proposed novel framework is the first work on addressing the above three issues for financial computing areas. Finally, we conduct a set of experiments to validate the proposed framework on the real-world Taiwan stock data. The test results show the proposed framework indeed satisfies the security, scalability, and long-query requirements, comparing to existing solutions.
Fuzzy Sets and Extensions: A Literature Review

Humanoid robots generated by inspiring by human appearances and abilities have become essential in human society to improve the quality of their life. All over the world, there have been many researchers who have focused on humanoid robots to develop their capabilities. Generally, humanoid robot systems include mechanisms of decision making and information processing. Because of the uncertainty behind decision making and information processes, fuzzy sets can be used in humanoid systems efficiently. This study presents a comprehensive literature review on the recent developments and theories associated with fuzzy set models.
Suitability study of Blockchain application in electric utility company business processes

Abstract Blockchain technology recently draws populous attention, both in academic research and industry area including energy industry. Blockchain is viewed as an innovative technology that can enhance conventional way of doing business, especially those that require intermediary involvement. Perusahaan Listrik Negara or PLN as the state owned electricity company in Indonesia also seek to improve its business process using current technology, including evaluating to implement Blockchain in PLN business processes. This study aims to develop a workable framework in identifying suitable business activity for Blockchain implementation in PLN. The framework is developed from existing frameworks with adjustment to better fit the application in PLN. The developed framework is then used in a focus group discussion involving business process stake holders to identify which business area Blockhcain can enhance the process and suitable for implementation. This study finds that certain existing PLN business process can be enriched by implementing Blockchain technology. Numbers of future innovative businesses that can help PLN to improve customer satisfaction and provide revenue to PLN also identified.
Performance analysis of privacy preserving distributed data mining based on cryptographic techniques

Privacy Preservation should be maintained while retrieving relevant information from multiple sources without any leakage. In order to make correct decision, Data mining plays a crucial role in any business environment. Performing distributed data mining for retrieving or extracting relevant information from multiple parties without any leakage is an important issue which has to be addressed. In order to overcome this problem, privacy preserving distributed data mining methods was used for secure and reliable information sharing from multiple parties. In this paper, different privacy preservation distributed data mining techniques commonly known as cryptographic approaches like Secure Multiparty Computation Homomorphic Encryption and Secret Sharing methods were discussed and methods like homomorphic encryption and secret sharing is implemented on medical and business data. Our experimental result shows that secret sharing method performs better than homomorphic encryption.
A Comparative Study on the Performance of Deep Learning Algorithms for Detecting the Sentiments Expressed in Modern Slangs

Sentiment analysis is a text investigation technique that distinguishes extremity inside the text, regardless of whether an entire document, sentence, etc. Understanding individuals’ feelings are fundamental for organizations since customers can communicate their considerations and emotions more transparently than any other time in recent memory. In this paper, the proposed model is the sentimental analysis on Twitter slangs, i.e., tweets that contain words that are not orthodox English words but are derived through the evolution of time. To do so, the proposed model will find the root words of the slangs using a snowball stemmer, vectorizing the root words, and then passing it through a neural network for building the model. Also, the tweets would pass through six levels of pre-processing to extract essential features. The tweets are then classified to be positive, neutral, or negative. Sentiment analysis of slangs used in 1,600,000 tweets is proposed using long short-term memory (LSTM) network, logistic regression (LR), and convolution neural network (CNN) algorithms for classification. Among these algorithms, the LSTM network gives the highest accuracy of 78.99%.
A Census of Swedish Government Administrative Authority Employee Communications on Cybersecurity during the COVID-19 Pandemic

Cybersecurity is the backbone of a successful digitalization of society, and cyber situation awareness is an essential aspect of managing it.The COVID-19 pandemic has sped up an already ongoing digitalization of Swedish government agencies, but the cybersecurity maturity level varies across agencies.In this study, we conduct a census of Swedish government administrative authority communications on cybersecurity to employees at the beginning of the COVID-19 pandemic.The census shows that the employee communications in the beginning of the pandemic to a greater extent have focused on first-order risks, such as video meetings and telecommuting, rather than on second-order risks, such as invoice fraud or social engineering.We also find that almost two thirds of the administrative authorities have not yet implemented, but only initiated or documented, their cybersecurity policies.
Technological Solutions for Screening and Geolocation of COVID-19 Infected People: A Country Comparison Approach

Introduction: As the COVID-19 pandemic spreads around the world, governments are seeking solutions to mitigate contagion. These initiatives use technology to control the movement of infected people, particularly from mobile phone monitoring. This manuscript intends in the first stage to carry out a brief overview of these initiatives at the global level. After that, it aims to identify the main challenges posed by these apps in monitoring the individual's health data and explore good practices that may prove fundamental for the uptake of these solutions on a large-scale.Materials and Methods: This study employs a qualitative methodology to perform a review on technological solutions for screening and geolocation of COVID-19 infected people. Five countries have been selected considering the different approaches in the implementation of these technological solutions. Four fundamental principles for the evaluation of these solutions such as consent, proportionality, transparency, and security were considered. Through this approach, it has become feasible to identify and discuss the challenges and best practices in the implementation of these solutions. Results: Although these applications publicly assume that they guarantee people's fundamental rights this information becomes insufficient. It is necessary to evaluate these solutions specifically considering fundamental principles such as consent, proportionality, transparency, and security. The existence of an independent body authority that can audit these solutions is relevant, besides the voluntary adherence to these applications.Conclusion: The way these solutions are implemented and imposed in these countries is quite different. The absence of mechanisms to measure how data is stored and processed raise concerns among people. Accordingly, the large-scale adoption of these tools requires that people's fundamental rights be duly considered from a multidimensional perspective.
Distributed voltage regulation using permissioned blockchains and extended contract net protocol

• Extended contract net protocol (ECNP) used to resolve simultaneous voltage violations. • Permissioned blockchains used to provide decentralized decision making between agents. • Smart contracts developed on Hyperledger Fabric to automate agent negotiation. • Simulations on 69 bus system show that system resolves voltage violations in parallel. Distributed voltage regulation schemes within active distribution networks (ADNs) are receiving plenty of attention because they reduce latency bottlenecks. However, previous schemes often employ a trusted moderator to govern agent communication, thereby exposing potential trust issues that may affect agent performance. Blockchain technology can avoid the need for intermediaries, since it is a fully decentralized network that is governed by agents using distributed consensus mechanisms. Thus, this paper proposes a blockchain-based system for voltage regulation in ADNs that provides a competitive marketplace wherein agents can rationally bid for voltage regulation services. The proposed system utilizes a smart contract that i) enables asynchronous negotiation between agents to mitigate multiple violations simultaneously using the extended contract net protocol (ECNP), and ii) maintains a credit score that reflects the trustworthiness of an agent in resolving voltage violations. The proposed system also develops a bidding and pricing scheme for voltage regulation services that considers both active and reactive power contributions. A permissioned blockchain implementation is used to ensure that only known agents are permitted to access the system. The proposed system is implemented on the Hyperledger Fabric platform, where simulations executed on a 69 bus system demonstrate the system’s ability to mitigate multiple voltage violations simultaneously. As demonstrated by the simulation results, the highlight of the main contribution of the paper is the ability of the proposed system to mitigate multiple voltage violations simultaneously via the ECNP, thereby utilizing less bidding cycles to resolve voltage violations as compared to the original contract net protocol.
A Swarm Grammar-Based Approach to Virtual World Generation

In this work we formulate and propose an extended version of the multi-agent Swarm Grammar (SG) model for the generation of virtual worlds. It unfolds a comparatively small database into a complex world featuring terrain, vegetation and bodies of water. This approach allows for adaptivity of generated assets to their environment, unbounded worlds and interactivity in their generation. In order to evaluate the model, we conducted sensitivity analyses at a local interaction scale. In addition, at a global scale, we investigated two virtual environments, discussing notable interactions, recurring configuration patterns, and obstacles in working with SGs. These analyses showed that SGs can create visually interesting virtual worlds, but require further work in ease of use. Lastly we identified which future extensions might shrink required database sizes.
Mixed reality updatable content for learning supportive tools

Abstract Mixed reality technology is a combination of Augmented Reality (AR) and Virtual Reality (VR) technologies. Augmented reality and virtual reality are often used for various fields in terms of education, media campaigns, even used as a game to be more interesting, creative and innovative. The success of Mixed Reality implementation as learning supportive tools are laid on the content richness. As similar to web content, the use of mixed reality as a learning resource will become less efficient when it doesn’t have dynamic content. This paper presents the early-stage development of the web-based application as the data source for mixed reality to enable dynamic content when using mixed reality as learning supportive tools. As the results, 112 feedback has been gathered along with acceptance rate more than 86 % stated that the dynamic content is more interesting than having mixed reality with only static content.
A Robust user Authentication Framework for Bigdata

Authentication is a concern in any security system, as it is considered as one of the most important mechanisms to ensure security and privacy because it determines the identity of the user if this user is authorized for access or unauthorized. This research work suggests an authentication system based on a single tier authentication using' `Inverse Hyperbolic Sine Functions”. This function is calculated and stored on the user side according to the mechanism used in the system. This value is not sent to big data server, while on the other side at the big data server, the calculation is done using logarithms Function, and then the values are compared to complete the authentication process. The importance of the proposed technique is that it combats many attacks such as: Replay attack Phishing attack, denial of service attack, and man in attack.
Lightspeech: Lightweight Non-Autoregressive Multi-Speaker Text-To-Speech

With the development of deep learning, end-to-end neural text-to-speech systems have achieved significant improvements on high-quality speech synthesis. However, most of these systems are attention-based autoregressive models, resulting in slow synthesis speed and large model parameters. In this paper, we propose a new lightweight non-autoregressive multi-speaker speech synthesis system, named LightSpeech, which utilizes the lightweight feedforward neural networks to accelerate synthesis and reduce the amount of parameters. With the speaker embedding, LightSpeech achieves multi-speaker speech synthesis extremely quickly. Experiments on the LibriTTS dataset show that, compared with FastSpeech, our smallest LightSpeech model achieves a 9.27x Mel-spectrogram generation acceleration on CPU, and the model size and parameters are compressed by 37.06x and 37.36x, respectively.
Geographic Information Systems for web-based maternity centers

Abstract One of the benefits of the geographic information system (GIS) is that it is a tool for capturing, storing, and displaying data related to the approximate position of the earth. The delivery place is one of the critical areas that considered urgent to deal with the delivery of pregnant women. With this information system, it expected that the public could quickly obtain information on the distribution of the nearest birthplaces, profiles of medical personnel such as doctors, midwives, and nurses as well as available facilities. The methodology used in this study is the Rational Unified Process, with stages: inception, elaboration, construction, and transition. The results of this study are web-based GIS applications to search for distribution of maternity places to facilitate the community in finding anywhere the distribution of midwives, their location, and facilities in the midwife’s area.
MISS: A Multi-user Identification Network for Shared-Account Session-Aware Recommendation

The user's interactions with the system within a given time frame are organized into a session. The task of session-aware recommendation aims to predict the next interaction based on user's historical sessions and current session. Though existing methods have achieved promising results, they still have drawbacks in some aspects. First, most existing deep learning methods model a session as a sequence, but neglect the complex transition relationships between items. Second, a single account is usually regarded as a single user by default, where the scenario of multiple users sharing the same account is ignored. To this end, we propose a Multi-user Identification network named MISS for the Shared-account Session-aware recommendation problem. MISS consists of two core components: one is the Dwell Graph Neural Network (DGNN), which incorporates item dwell time into the gated graph neural network to capture user interest drift across sessions. The other is a Multi-user Identification (MI) module, which draws on the attention mechanism to distinguish behaviors of different users under the same account. To verify the effectiveness of MISS, we construct two data sets with shared account characteristics from real-world smart TV watching logs. Extensive experiments conducted on the two data sets demonstrate that MISS evidently outperforms the state-of-the-art recommendation methods.