Reviewerly/exHarmony huggingface dataset:
GC-TTS: Few-shot Speaker Adaptation with Geometric Constraints

Few-shot speaker adaptation is a specific Text-to-Speech (TTS) system that aims to reproduce a novel speaker&#x2019;s voice with a few training data. While numerous attempts have been made to the few-shot speaker adaptation system, there is still a gap in terms of speaker similarity to the target speaker depending on the amount of data. To bridge the gap, we propose GC-TTS which achieves high-quality speaker adaptation with significantly improved speaker similarity. Specifically, we leverage two geometric constraints to learn discriminative speaker representations. Here, a TTS model is pre-trained for base speakers with a sufficient amount of data, and then fine-tuned for novel speakers on a few minutes of data with two geometric constraints. Two geometric constraints enable the model to extract discriminative speaker embeddings from limited data, which leads to the synthesis of intelligible speech. We discuss and verify the effectiveness of GC-TTS by comparing it with popular and essential methods. The experimental results demonstrate that GC-TTS generates high-quality speech from only a few minutes of training data, outperforming standard techniques in terms of speaker similarity to the target speaker.
GA-CSPN: generative adversarial monocular depth estimation with second-order convolutional spatial propagation network

Monocular depth estimation, which provides a critical method for understanding 3D scene geometry, is an ill-posed problem. Recent research studies have achieved significant progress by reliable network architecture and optimized constraints, such as spatial propagation network and depth metrics. We propose an effective generative adversarial network for fast and accurate monocular depth estimation. Our approach demonstrates the feasibility of applying a dense-connected UNet for reducing information transmission loss and then fine-tuning the blur depth by the high-order convolutional spatial propagation network (CSPN) that used a modified loss function of discriminator. Furthermore, we modify the loss function of discriminator by adding the correlation loss that is used to measure the similarity of real and fake labels. Compared with the original CSPN, the high-order CSPN reduces the computation complexity and accelerates the convergence of the generator network by increasing the order of kernel, which emphasizes the weight of kernels in the update formula. With these modifications, our generative adversarial second-order convolutional spatial propagation network (GA-CSPN) achieves more accurate results against state-of-the-art methods in both indoor and outdoor scenes on Make3D, KITTI 2015, and NYUv2 datasets.
Pengembangan Media Pembelajaran Berbasis Aplikasi Kinemaster Pada Pembelajaran Tematik Terpadu di Kelas III Sekolah Dasar

The development of the times demands that humans be more progressive, thus students are required to find information independently from the results of their interactions with the learning environment. Learning media are used to streamline messages so that a learning environment is created conducive. In fact, the use of learning media is still conventional, does not vary, and the use of digital media is still very minimal. This type of research is research and development using the 4D model. However, the steps taken are only define, design, and development. The resulting product declared very valid with the average feasibility of learning media is 95%, with details of the feasibility of material 93.3%, media 96.7%, and language 95%. The results of teacher and student responses were stated to be very practical, namely 92.5% and 95.25%. Thus, learning media based on the Kinemaster application in integrated thematic learning in class III SD Negeri 36 Koto Panjang are declared very valid and very practical.
Pairs trading in cryptocurrency market: A long-short story

Pairs trading that is built on ’Relative-Value Arbitrage Rule’ is a popular short-term speculation strategy enabling traders to make profits from temporary mispricing of close substitutes. This paper aims at investigating the profit potentials of pairs trading in a new finance area – on cryptocurrencies market. The empirical design builds upon four well-known approaches to implement pairs trading, namely: correlation analysis, distance approach, stochastic return differential approach, and cointegration analysis, that use monthly closing prices of leading cryptocoins over the period January 1, 2018, – December 31, 2019. Additionally, the paper executes a simulation exercise that compares long-short strategy with long-only portfolio strategy in terms of payoffs and risks. The study finds an inverse relationship between the correlation coefficient and distance between different pairs of cryptocurrencies, which is a prerequisite to determine the potentially market-neutral profits through pairs trading. In addition, pairs trading simulations produce quite substantive evidence on the continuing profitability of pairs trading. In other words, long-short portfolio strategies, producing positive cumulative returns in most subsample periods, consistently outperform conservative long-only portfolio strategies in the cryptocurrency market. The profitability of pairs trading thus adds empirical challenge to the market efficiency of the cryptocurrency market. However, other aspects like spectral correlations and implied volatility might also be significant in determining the profit potentials of pairs trading.
Simple Transfer Learning with TensorFlow Hub

Transfer learning is the process of creating new learning models by fine-tuning previously trained neural networks. Instead of training a network from scratch, we download a pre-trained open source learning model and fine-tune it for our own purpose. A pre-trained model is one that is created by someone else to solve a similar problem. We can use one of these instead of building our own model. A big advantage is that a pre-trained model has been crafted by experts, so we can be confident that it performs at a high level (in most cases). Another advantage is that we don’t have to have a lot of data to use a pre-trained model.
Advancing Risk Management Capability Using the OCTAVE FORTE Process

OCTAVE FORTE (Operationally Critical Threat, Asset, and Vulnerability Evaluation FOR The Enterprise) is a process model that helps executives understand and prioritize the complex risks affecting their organization. It also helps organizations identify, analyze, prioritize, and mitigate risks that could impact them. The Software Engineering Institute (SEI) developed the OCTAVE FORTE process model to help organizations evaluate their security risks and use ERM principles to bridge the gap between executives and practitioners. The process model guides organizations that are new to risk management in building an ERM program, and it helps mature organizations fortify their existing ERM program, making it more reliable, measurable, consistent, and repeatable. Besides describing the OCTAVE FORTE process, this report recommends methods and provides a sample risk management policy that organizations can refer to or adapt when writing their own policy. Supplemental materials contain templates that organizations can use when conducting many of the OCTAVE FORTE activities.
Digitally-Signed Video/Audio Streams as Prevention of AI-Based Attacks

The main purpose of the research is to provide the solution that allows digitally signing video and audio records, which is extremely important not only in the frame of copyright protection but also when it comes to understanding whether a video is fake. In the world where neural networks are used more and more often, it becomes easy to make a fake video using the face of some famous politician, maybe even the president of the US, and broadcast such a video, causing different kinds of negative events in the situation of the political crises.
Drones as Futuristic Crime Prevention Strategy: Situational Review during COVID-19 Lockdown

This paper explores the potential for virtual tourism in Africa as a way to contribute towards economic growth and sustainability under the impact of the COVID-19 pandemic.The study engaged in an archival method or document review, hence, thematic and content analysis.Virtual tourism has the potential to reach millions of people.The paper contributes towards innovative strategies for integrated and regional virtual or online tours of resorts such as game parks, reserves and other tourist attractions in Africa given travel restrictions.African governments must seriously consider partnership with the private sector in building virtual technology for virtual interaction and business resilience.
Model-Agnostic Defense for Lane Detection against Adversarial Attack

Susceptibility of neural networks to adversarial attack prompts serious safety concerns for lane detection efforts, a domain where such models have been widely applied. Recent work on adversarial road patches have successfully induced perception of lane lines with arbitrary form, presenting an avenue for rogue control of vehicle behavior. In this paper, we propose a modular lane verification system that can catch such threats before the autonomous driving system is misled while remaining agnostic to the particular lane detection model. Our experiments show that implementing the system with a simple convolutional neural network (CNN) can defend against a wide gamut of attacks on lane detection models. With a 10% impact to inference time, we can detect 96% of bounded non-adaptive attacks, 90% of bounded adaptive attacks, and 98% of patch attacks while preserving accurate identification at least 95% of true lanes, indicating that our proposed verification system is effective at mitigating lane detection security risks with minimal overhead.
Efficient diagnostics for quantum error correction

Fault-tolerant quantum computing will require accurate estimates of the resource overhead under different error correction strategies, but standard metrics such as gate fidelity and diamond distance have been shown to be poor predictors of logical performance. We present a scalable experimental approach based on Pauli error reconstruction to predict the performance of concatenated codes. Numerical evidence demonstrates that our method significantly outperforms predictions based on standard error metrics for various error models, even with limited data. We illustrate how this method assists in the selection of error correction schemes.
Analysis on the Development Path of Music Education in Rural Areas under the Influence of COVID-19 Outbreak

In 2020, the sudden COVID-19 outbreak brought huge economic and life losses to the whole world, affecting all walks of life in China, including the education industry which suffered from many problems, such as students' learning needs for medical health knowledge, publicity needs for public health safety, psychological and mental health protection needs, and online learning needs based on internet accelerated speed .In rural China, some rural students have physical and mental health problems due to the problems of rural population structure, infrastructure construction, talent mobility, etc. In this period, schools should pay more attention to the psychological counseling and education of rural students, on which music education has a positive impact. During the epidemic period, rural education construction around cultural revitalization was gradually implemented under the background of China's full implementation of rural revitalization strategy. In the construction of rural education, music education is an important cornerstone to promote the all-round development of students, build a rural cultural atmosphere and inherit the excellent national culture. With the change of social environment, the country music education welcomes new opportunities, but also faces some practical problems to be solved.First of all, the literature on the COVID-19 outbreak and rural music education is studied and analyzed through on-the-spot visits and investigation on the current situation of music education in rural schools. Then, interviews and records are made with rural music teachers and students. Next, discussions are made on the construction of rural music teachers, information technology of music education, cultural life of the rural masses and other aspects. Finally, relevant strategies for the development of rural music education are put forward.The COVID-19 outbreak has narrowed the channels for rural music teachers' professional promotion, and caused some problems in rural students' mental health. In addition, rural schools have some shortcomings in online learning and information education based on internet plus, and the number of rural cultural performances and activities has decreased.Education managers should take corresponding management measures to innovate the music education model with the aid of modern teaching technology. At the same time, rural schools need to maintain long-term cooperation with all sectors of society and cooperate in public health, medical resources, education resources, curriculum innovation, rural cultural activities and other aspects to ensure the real implementation, promotion and development of music education, aesthetic education and art education in the countryside during the COVID-19 epidemic.
KEBI 1.0: Indonesian Spelling Error Detection System for Scientific Papers using Dictionary Lookup and Peter Norvig Spelling Corrector

Many Indonesian spelling errors occur in research papers published to the public, closely related to academics in all institutions such as research institutions, government, schools, and universities. The spelling errors usually writing punctuation, writing letters, writing words, writing words originating from foreign or regional languages (uptake words), using affixed words, and writing ineffective sentences. The mistakes made by the academics then become a cycle in the academic environment. They usually provide guidance for writing an undergraduate thesis, thesis, dissertations to students, or the other forms of documents and scientific papers. Therefore, the research proposed the application to facilitate all authors of scientific papers in producing quality scientific works based on the General Guidelines for Indonesian Spelling published by the Agency for Development and Language Development. The application is named KEBI 1.0 Checker (Indonesian Spelling Error 1.0 Checker), a web-based application with a built-in algorithm to detect and correct Indonesian Spelling in scientific papers. The experiment result shows that the application has given the best accuracy performance to correct the non-standard words, and typographical errors reached 100% and 55,52%, respectively. The application also has been detected 209 meaningless words. The application processing time is relatively low, the average time needed to correct non-standard words is 0.016 seconds, and typo words are 14.58 seconds. KEBI 1.0 Checker is helpful for the end-user in academics but needs to improve the vocabulary of the large corpus in various fields of science for correcting typo words.&#x0D;
Quantum entanglement and modulation enhancement of free-electron–bound-electron interaction

We study the modulation enhancement of interaction and entanglement between distant atoms in the interaction between free electron and two-level atoms.
The Impact of Integrated Mobile System on Students' Academic Achievement, Interaction and Satisfaction

This study explored the effectiveness of Blended Learning, hereafter (BL) on students’ academic achievement, interaction and satisfaction in higher education. The Integrated M-Learning and ELearning system, (IMELS) was utilized on a sample of students (n = 52) who attended Artificial Intelligent course at a large urban college in Jordan. The pretest/ post-test design was used to investigate the students’ academic achievement, interaction and satisfaction. ANOVA test was conducted to compare the achievement scores between the BL with IMELS group and face-to-face learning group. The post-test revealed a significant difference between the two groups in favour of the treatment group. Findings also showed that interaction types were positively associated with satisfaction of the students exposed to the IMELS in favour of learner-instructor interaction. This study has contributed to a better understanding of new technologies like IMELS in BL environment, which can affect positively the students' academic achievement, interactions and their satisfaction.
Cockroach Swarm Optimization Algorithm for High Utility Association Rule Mining

Association rule mining is an important and widely used data mining technique. It is used to retrieve highly related objects in a database based on the occurrence. Recently, utility-based association rules were proposed to consider significant factors of the object. The main objective of this research work is to retrieve high utility association rules from a database using cockroach swarm optimization algorithm. So far, in the literature, no optimization algorithm was proposed in utility-based association rule mining. In this research work, CSOUAR (cockroach swarm optimization for high utility association rule mining) algorithm was proposed to generate utility association rules. CSOUAR algorithm is based on three behaviours of cockroach: chase-swarming, dispersing, and ruthless. To analyse the performance of CSOUAR, an improved particle swarm optimization (PSO-UAR), animal migration optimization (AMO-UAR), bees swarm optimisation (BSO-UAR), and penguins search optimisation (peSO-UAR) are also proposed in this work.
Sexual and gender-based violence: To tweet or not to tweet?

This research explores the reasons that lead survivors of sexual and gender-based violence to share their experiences on Twitter, as well as the reasons that prevent them from doing so. Using an online survey, we investigated the perspective of 94 survivors of sexual and gender-based violence. Open-ended responses were analysed with the ALCESTE method, a lexical analysis. Considering that in-person self-disclosure differs from in-public self-disclosure, we based our interpretations of the findings on self-disclosure as well as collective action and social activism frameworks. Results showed that those who shared their experience on Twitter did so to render sexual and gender-based violence more visible, and to support the #MeToo movement. They also wanted to change public perceptions of sexual and gender-based violence, while addressing violence at work and denouncing rape culture (the difficulty of filing a complaint, and victim blaming). On the contrary, survivors who did not tweet their experience expressed several concerns, such as feeling ashamed, the fear of being judged by others, and a lack of trust in the Twitter platform and its members. This research concludes that in order to understand the disclosure of victimisation on social network sites, like Twitter, it is worthwhile to complement the self-disclosure framework with insights on collective action and social activism. We also make a call for taking into account differences of social network sites when studying online disclosure of sexual and gender-based violence. Content warning: This article discusses issues of sexual and gender-based violence.
Embodied Argumentation in Young Children in Kindergarten

In kindergarten, children are usually engaged with both verbal activities and non-verbal activities, often requiring the manipulation of physical objects. During technical tasks (e.g., problem solving), children can use argumentation as one of the languages of science that mediates how they interact with the surrounding world. In this paper, we focused on technical tasks in kindergarten in order to understand to what extent activities requiring the manipulation of physical objects also leave space for argumentation. The study involved 25 children engaged in three problem-solving activities requiring the manipulation of Lego® and some recycled materials. To analyze the non-verbal (embodied) side of the argumentative activities, we firstly identified the argumentative structure of each exchange involving the participants. Then, we focused on segments of “incomplete” argumentative dialogues (i.e., presenting only some elements typical of children’s argumentation) by appealing to multimodal representations (speech, gestures, and physical objects). The findings of the study showed that even apparently incomplete exchanges can have an argumentative function generated by non-verbal elements of the interactions. Investigating the role of embodied argumentation during technical tasks in kindergarten can allow teachers to recognize and further develop children’s argumentative resources.
New Hybrid Deep Learning Method to Recognize Human Action from Video

There has been a tremendous increase in internet users and enough bandwidth in recent years. Because Internet connectivity is so inexpensive, information sharing (text, audio, and video) has become more popular and faster. This video content must be examined in order to classify it for different purposes for users. Several machine learning approaches for video classification have been developed to save users time and energy. The use of deep neural networks to recognize human behavior has become a popular issue in recent years. Although significant progress has been made in the field of video recognition, there are still numerous challenges in the realm of video to be overcome. Convolutional neural networks (CNNs) are well-known for requiring a fixed-size image input, which limits the network topology and reduces identification accuracy. Despite the fact that this problem has been solved in the world of photos, it has yet to be solved in the area of video. We present a ten stacked three-dimensional (3D) convolutional network based on the spatial pyramid-based pooling to handle the input problem of fixed size video frames in video recognition. The network structure is made up of three sections, as the name suggests: a ten-layer stacked 3DCNN, DenseNet, and SPPNet. A KTH dataset was used to test our algorithms. The experimental findings showed that our model outperformed existing models in the area of video-based behavior identification by 2% margin accuracy.
Classification of Imbalanced Data Represented as Binary Features

Typically, classification is conducted on a dataset that consists of numerical features and target classes. For instance, a grayscale image, which is usually represented as a matrix of integers varying from 0 to 255, enables one to apply various classification algorithms to image classification tasks. However, datasets represented as binary features cannot use many standard machine learning algorithms optimally, yet their amount is not negligible. On the other hand, oversampling algorithms such as synthetic minority oversampling technique (SMOTE) and its variants are often used if the dataset for classification is imbalanced. However, since SMOTE and its variants synthesize new minority samples based on the original samples, the diversity of the samples synthesized from binary features is highly limited due to the poor representation of original features. To solve this problem, a preprocessing approach is studied. By converting binary features into numerical ones using feature extraction methods, succeeding oversampling methods can fully display their potential in improving the classifiers’ performances. Through comprehensive experiments using benchmark datasets and real medical datasets, it was observed that a converted dataset consisting of numerical features is better for oversampling methods (maximum improvements of accuracy and F1-score were 35.11% and 42.17%, respectively). In addition, it is confirmed that feature extraction and oversampling synergistically contribute to the improvement of classification performance.