Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network to Reproduce Selfie Images after Removing Supportive Hand

Although selfie images have become popular among smartphone users, the supportive hand that holds the camera ruins the beauty of the picture. The captured images will look more realistic if the supporting hand is removed from the original image. This paper proposes a machine learning and a computer vision based approach to remove the supportive hand and reconstruct the removed hand that matches with the person who took the picture. A fully convolution neural network (FCN) and a partial convolution neural network (PConv Net) have been used to accomplish this task. Results indicate that the FCN gives 94.58% validation accuracy with the PConv Net is utilized to train the model for background matching and hand creation. The FCN and PCovNet models minimize validation lose up to 1.73 and 1.95, respectively.
Adaptive General Search Framework for Games and Beyond

The research field of Artificial General Intelligence (AGI) is concerned with the creation of adaptive programs that can autonomously address tasks of a different nature. Search and planning have been identified as core capabilities of AGI, and have been successful in many scenarios that require sequential decision-making. However, many search algorithms are developed for specific problems and exploit domain-specific knowledge, which makes them not applicable to perform different tasks autonomously. Although some domain-independent search algorithms have been proposed, a programmer still has to make decisions on their design, setup and enhancements. Thus, the performance is limited by the programmer's decisions, which are usually biased. This paper proposes to develop a framework that, in line with the goals of AGI, autonomously addresses a wide variety of search tasks, adapting automatically to each new, unknown task. To achieve this, we propose to encode search algorithms in a formal language and combine algorithm portfolios with automatic algorithm generation. In addition, we see games as the ideal test bed for the framework, because they can model a wide variety of complex problems. Finally, we believe that this research will have an impact not only on the AG I research field, but also on the game industry and on real-world problems.
The Goldilocks Principle of Cooperation: Understanding Federated Learning in Healthcare via Iterated Prisoner's Dilemma

Limited access to large-scale data is one of the key obstacles to building machine learning and artificial intelligence applications in healthcare, partly due to a reluctance of information exchange among healthcare institutions out of privacy and data security concerns. To address this issue, federated machine learning techniques have been proposed that enable decentralized model training via an orchestrating platform. Despite its superior privacy protection property, a lack of systematic understanding of the economic incentives and strategic trade-offs in federated learning deters its wide adoption in practice. In this paper, we use an iterated prisoner's dilemma (IPD) framework to characterize the decisions - to share information or not - faced by participants (e.g., healthcare institutions) in a federated learning partnership, and to derive boundary conditions under which stable information exchange can arise. We find that cooperation outcome depends on the participants' cost sensitivities, information acquisition capacities, and their temporal preferences. Stable cooperation requires that participants are not too cost averse and collect not too much information per round during the federated learning process. Interestingly, it also requires participants to be neither too myopic nor too forward-looking, which is different from the conventional IPD results (where cooperation only requires non-myopic players). We further examine the cooperation dynamics (1) when participants differ in several key characteristics and (2) when non-cooperative behaviors take other forms (e.g., by sharing partial or poisoned information). Our work informs the design of contracts and incentive mechanisms for federated learning that promote private and secure information exchange in healthcare.
Error Reconciliation based on Integer Linear Programming in Quantum Key Distribution

Quantum telecommunication has received a lot of attention today by providing unconditional security because of the inherent nature of quantum channels based on the no-cloning theorem.In this mode of communication, first, the key is sent through a quantum channel that is resistant to eavesdropping, and then secure communication is established using the exchanged key.Due to the inevitability of noise, the received key needs to be distilled.One of the vital steps in key distillation is named key reconciliation which corrects the occurred errors in the key.Different solutions have been presented for this issue, with different efficiency and success rate.One of the most notable works is LDPC decoding which has higher efficiency compared to the others, but unfortunately, this method does not work well in the codes with a high rate.In this paper, we present an approach to correct the errors in the high rate LDPC code-based reconciliation algorithm.The proposed algorithm utilizes Integer Linear Programming to model the error correction problem to an optimization problem and solve it.Testing the proposed approach through simulation, we show it has high efficiency in high rate LDPC codes as well as a higher success rate compared with the LDPC decoding method -belief propagationin a reasonable time.
Pathfinding for Disaster Emergency Route Using Sparse A* and Dijkstra Algorithm with Augmented Reality

Indonesia has many natural disasters, ranging from floods, landslides, earthquakes and other disasters. The disaster caused damage and trauma to the victims. From January 1 to December 31, 2020, natural disasters have claimed 6,203,730 victims and a total of 2,952 disasters. As disaster cases increase from year to year, volunteers and disaster response organizations begin to participate in helping disaster victims. However, problems usually occur for volunteers to easily reach and access the disaster location. Using only a 2D map is not enough to help volunteers to get to the disaster location properly, especially in rural areas that are difficult to navigate on. For this reason, it is necessary to develop a 2D map system which provides to proper route visualization. In addition, a good pathfinding algorithm is able to enhance the map to have an accurate route. In this study the pathfinding algorithms used for is sparse A* and Dijkstra’s algorithms. Then the visualization is assisted by Augmented Reality (AR) technology which can show the route that will be traversed by emergency disaster volunteers. The purpose of this study is to assist those volunteers in finding the shortest path using two algorithms and AR as a visualization of the route.
3D Voxel Reconstruction Based on Shape Layer

3D reconstruction is a basic and important task in computer vision. The overall reconstruction quality evaluation of voxel based 3D reconstruc-tion method is not high currently. This paper mainly aims at the problem that the overall reconstruction quality evaluation of voxel based 3D reconstruction methods is not high. According to the good conversion relationship between shape layer and voxels, a new shape layer based 3D voxel recon-struction network is proposed to transform the prediction problem of 3D voxels into the prediction problem of 2D depth map. Experiments show that it is superior to other voxel based 3D reconstruction methods.
An Image Quality Predictor based on Convolution Neural Networks

Image quality assessment is an essential issue for image encoding, monitoring, and pricing. We developed an image quality predictor by using convolution neural networks (CNN) in this paper to address this issue. This predictor consists of a distortion classifier and five scorers for predicting the quality of images with different distortions. Different from related work, the proposed predictor uses the Laplacian filter in data preprocessing to increase the gradient variances caused by different distortions and replaces the flatten layer with the global average pooling layer in CNN for reducing the amount of data computation. Our experimental results have shown that the proposed predictor can provide higher prediction accuracy and spend less time on image quality assessment than related work.
Contrastive Multi-View Self-Supervised Learning for Heterogeneous Information Network

Self-supervised learning constructs supervised signals inside samples without relying on external labels, which is becoming a promising research direction. Recently, works on self-supervised learning by maximizing local-global mutual information on networks have achieved state-of-the-art performance comparable to semi-supervised graph neural networks (GNNs). However, these methods have not explored the collaborative relationship of multiple meta-path views, and the global representation is weakened by irrelevant nodes which participate in the average operation over all nodes. In this paper, a self-supervised approach based on mutual information for heterogeneous information network embedding is proposed. Specifically, it utilizes the contrast of multiple meta-path views to supervise each other, and positive samples are selected to obtain a robust global representation. Experimental results demonstrate the proposed method has competitive performance over the existing mutual-information-based ones and even outperforms some supervised learning methods.
Random Neural Graph Generation with Structure Evolution

In deep learning research, typical neural network models are multi-layered architectures, and weights are tuned while optimizing a carefully designed loss function. In recent years, studies of randomized neural networks have been extended towards deep architectures, opening a new research direction to the design of deep learning models. However, how the structure of the network can influence the model performance still remains unclear. In this paper, we move a further step to investigate the relation between network topology and performance via a structure evolution algorithm. Experimental results show that the graph would evolve towards a more small-world topology at the beginning of the training session along with gaining accuracy, and would also evolve towards a structure with more scale-free property in the following periods. These conclusions could help explain the effectiveness of the randomly connected networks, as well as give us insights in new possibilities of network architecture design.
A Distributed Learning Algorithm for RBF Neural Networks

Training a radial basis function (RBF) neural network on a single processor is usually challenging due to the limited computation and storage sources, especially for data with large and multi-dimensional features. In addition, in real applications, large-scale data may be collected in a distributed manner, which also makes it difficult to handle the data only with a single processor. To address these issues, we propose a distributed learning algorithm for RBF neural networks. In this algorithm, RBF neural networks can be trained in parallel using multiple processors. Specifically, the large-scale training data is divided into groups and each processor is associated with only one group. By introducing a shared output weight vector, training can be carried out simultaneously on different processors. The formulated optimization problem is addressed with alternating direction method of multipliers (ADMM) framework. Simulation results demonstrate the effectiveness of the proposed algorithm.
Privacy Preserving Machine Learning and Deep Learning Techniques

In the past years, the usage of internet and quantity of digital data generated by large organizations, firms, and governments have paved the way for the researchers to focus on security issues of private data. This collected data is usually related to a definite necessity. For example, in the medical field, health record systems are used for the exchange of medical data. In addition to services based on users' current location, many potential services rely on users' location history or their spatial-temporal provenance. However, most of the collected data contain data identifying individual which is sensitive. With the increase of machine learning applications around every corner of the society, it could significantly contribute to the preservation of privacy of both individuals and institutions. This chapter gives a wider perspective on the current literature on privacy ML and deep learning techniques, along with the non-cryptographic differential privacy approach for ensuring sensitive data privacy.
ДУХОВНО-ЕСТЕТИЧНЕ ВИХОВАННЯ МАЙБУТНІХ МУЗИЧНО-ПЕДАГОГІЧНИХ ПРАЦІВНИКІВ

The article states that spiritual and intellectual education is a component of professional training of future music and pedagogical workers. Creating your own musical art products requires spiritual inspiration and great willpower. The development of intellectual thinking inspires future music and pedagogical workers to creativity, activity, initiative, self-expression in competitive programs
Sentiment Classification of Sinhala Content in Social Media: A Comparison between Stemmers and N-gram Features

Sentiment classification for non-English languages has gained significant attention from researchers in the past few years with the increasing use of non-English scripts and Romanized scripts for expressing sentiments over social media. In this study, we begin by classifying Sinhala sentiments on social media into positive and negative polarity classes using N-gram feature extraction. N-grams are a contiguous sequence of words or characters of a text. Then we focus on improving the classification accuracy by employing different stemming methods. Stemming is generally used to reduce the dimensionality of the feature set - something which needs to be carried out with great care as over reducing feature dimensionality causes the classification accuracy to decrease. Finally, we compare the accuracy and efficiency of N-gram feature extraction and stemming based sentiment analysis models.
Research on Performance Evaluation of University Event Volunteers under the Background of Mobile Internet

In order to analyze the documents and interviews of current mobile assessment of the status of college volunteer performance under the background of Internet, precondition by necessity and particularity of the current understanding of the implementation of the proposed emphasis for each thinking of college volunteer performance evaluation under the background of mobile Internet activities, volunteers, volunteers association and other aspects, finally build potential function of performance evaluation the performance evaluation of the mobile Internet under the background of the theoretical foundation and explore the context of mobile internet.
Hand Gesture Recognition Based on LPQ and MRELBP Descriptors

Gesture is an important communication tool, either for human-to-human communication, particularly to the estimated 70 million hearing-impaired people across the globe or for Human-Machine communication such as the human-machine interaction (HMI). In this paper, we propose an implementation of a static and dynamic gesture recognition system using three databases (American Sign Language ASL, Arabic Sign Language ArSL, and dynamic Clic) in an uniform background. The system is based on the Local Phase Quantization (LPQ) and the Median Robust Extended Local Binary Pattern (MRELBP) where the recognition task is applied using the Radial Basis Function Neural Network (RBF-NN) and the Support Vector Machine (SVM) classification techniques. Satisfactory results were obtained where the best recognition rate reached 97,5 % for American Language using LPQ descriptor.
The Role of Demographic Variables in Secondary School Teachers' Digital Literacy: The Case of Indonesia

School closure during the pandemic requires teachers to integrate digital technology for teaching and learning. Therefore, it is necessary to examine mathematics teachers' digital literacy. The purpose of this study was to examine Indonesian secondary school teachers' digital literacy and investigate if digital literacy levels are significantly different in terms of gender, teaching experience, the level of schools, and teacher level of education. We conducted a quantitative study with a cross-sectional survey design. Data were collected from 232 secondary school teachers and analyzed by ANOVA. This study suggests that, to some extent, demographic variables do not play a significant role in teachers' level of teachers' digital literacy. It was found that, among demographic background variables examined in this study, only teachers' levels of education have a significant role in the level of digital literacy. It indicates that researchers and practitioners should take teachers' education level into account when they design and implement professional development on digital literacy.
sylbreak4all: Regular Expressions for Syllable Breaking of Nine Major Ethnic Languages of Myanmar

Unlike many other western languages, the Myanmar language uses a syllabic writing system and no space between words. Syllable segmentation is the necessary preprocess for natural language processing (NLP) tasks such as grapheme-to-phoneme (g2p) conversion, machine translation, romanization, and so on. In this study, sylbreak4all, a syllable segmentation tool, was developed for nine major ethnic languages of Myanmar, and they are Burmese, Shan, Pa’o, Pwo Kayin, S’gaw Kayin, Rakhine, Myeik, Dawei, and Mon by using regular expression (RE) patterns.
Teaching GANs to sketch in vector format

Sketching is a fundamental human cognitive ability. Deep Neural Networks (DNNs) have achieved the state-of-the-art performance in recognition tasks like image recognition, speech recognition etc. but have not made significant progress in generating stroke-based sketches a.k.a sketches in vector format. Though there are Variational Auto Encoders (VAEs) for generating sketches in vector format, there is no Generative Adversarial Network (GAN) architecture for the same. In this paper, we propose a standalone GAN architecture called SkeGAN and a hybrid VAE-GAN architecture called VASkeGAN, for sketch generation in vector format. SkeGAN is a stochastic policy in Reinforcement Learning (RL), capable of generating both multidimensional continuous and discrete outputs. VASkeGAN draws sketches by coupling the efficient representation of data by VAE with the powerful generating capabilities of GAN. We have validated that SkeGAN and VASkeGAN generate visually appealing sketches with minimal scribble effect and is comparable to a recent work titled Sketch-RNN.
Quantitative research of the effects of cooperative learning

Quantitative research of the effects of cooperative learning represents an important source of information for scientists in this area. So, the goal of our work is to look at the effects of quantitative research content published in referent international magazines in the 21st century (N=11), and review the advantages and weaknesses of the quantitative research method in concrete examples. For the purpose of this work we created the protocol for analyses, which we used to provide detailed review of the chosen research. We noticed that, within the effects of quantitative research cooperative learning, there were neglected important research questions which include effects of cooperative learning on emotional sphere of student's personality and the effects of cooperative learning in the context of digital technology. Based on the analysis characteristics method of the chosen quantitative research of the effects of cooperative learning, having in mind the advantages and weak sides of the quantitative method, we could notice a need for the effects of cooperative learning to use various research methods, which would provide complementary reviews in relation to the explored occurrence. Analysed content of the chosen quantitative researches of the effects of cooperative learning has opened important questions for the future scientists which include consideration of contribution of modern research compared to the earlier research in this area. Also, we recommend reviewing and analysing of modern quantitative research for the effects of cooperative learning.
A Knowledge-Model for AI-Driven Tutoring Systems

A powerful new complement to traditional synchronous teaching is emerging: intelligent tutoring systems. The narrative: A learner interacts with a digital agent. The agent reviews, selects and proposes individually tailored educational resources and processes – i.e. a meaningful succession of instructions, tests or groupwork. The aim is to make personal tutored learning the new norm in higher education – especially in groups with heterogeneous educational backgrounds. The challenge: Today, there are no suitable data that allow computer-agents to learn how to take reasonable decisions. Available educational resources cannot be addressed by a computer logic because up to now they have not been tagged with machine-readable information at all or these have not been provided uniformly. And what’s worse: there are no agreed conceptual and structured models of what we understand by “learning”, how this model-to-be could be implemented in a computer algorithm and what those explicit decisions are that a tutoring system could take. So, a prerequisite for any future digital agent is to have a structured, computer-accessible model of “knowledge”. This model is required to qualify and quantify individual learning, to allow the association of resources as learning objects and to provide a base to operationalize learning for AI-based agents. We will suggest a conceptual model of “knowledge” based on a variant of Bloom’s taxonomy, transfer this concept of cognitive learning objectives into an ontology and describe an implementation into a web-based database application. The approach has been employed to model the basics of abstract knowledge in engineering mechanics at university-level. This paper addresses interdisciplinary aspects ranging from a teaching methodology, the taxonomy of knowledge in cognitive science, over a database-application for ontologies to an implementation of this model in a Grails service. We aim to deliver this web-based ontology, its user-interfaces and APIs into a research network that qualifies AI-based agents for competence-based tutoring.
Constructing a crowdsourced linked open knowledge base of Chinese history

This paper introduces a crowdsourced approach to knowledge base construction for historical data based upon annotation of historical source materials. Building on an existing digital library of premodern Chinese texts and adapting techniques from other annotation and knowledge base projects, this lays the groundwork for a scalable, sustainable, linked open repository of data covering around 3000 years of recorded Chinese history.