Reviewerly/exHarmony huggingface dataset:
Toward Formal Data Set Verification for Building Effective Machine Learning Models

In order to properly train a machine learning model, data must be properly collected. To guarantee a proper data collection, verifying that the collected data set holds certain properties is a possible solution. For example, guaranteeing that the data set contains samples across the whole input space, or that the data set is balanced w.r.t. different classes. We present a formal approach for verifying a set of arbitrarily stated properties over a data set. The proposed approach relies on the transformation of the data set into a first order logic formula, which can be later verified w.r.t. the different properties also stated in the same logic. A prototype tool, which uses the z3 solver, has been developed; the prototype can take as an input a set of properties stated in a formal language and formally verify a given data set w.r.t. to the given set of properties. Preliminary experimental results show the feasibility and performance of the proposed approach, and furthermore the flexibility for expressing properties of interest.
A Petrinet-Based Framework for Healthcare Blockchain Systems

Blockchain is a new technology under the interest of many researchers based on the benefits it provides for various systems in different industries. One of the main applications of blockchain is the healthcare sector where security, privacy and integrity of patient data is essential. In this paper, we discuss the problems faced by healthcare systems and how blockchain technology can be used to support these systems and improve the patient journey. A new Petri Net-based blockchain framework is proposed. The framework focuses on two main layers: process layer and data layer along with a mapping of the blockchain technology into the model. The proposed framework is validated using mathematical modeling and Petri Nets.
Reward Prediction for Representation Learning and Reward Shaping

One of the fundamental challenges in reinforcement learning (RL) is the one of data efficiency: modern algorithms require a very large number of training samples, especially compared to humans, for solving environments with high-dimensional observations. The severity of this problem is increased when the reward signal is sparse. In this work, we propose learning a state representation in a self-supervised manner for reward prediction. The reward predictor learns to estimate either a raw or a smoothed version of the true reward signal in environment with a single, terminating, goal state. We augment the training of out-of-the-box RL agents by shaping the reward using our reward predictor during policy learning. Using our representation for preprocessing high-dimensional observations, as well as using the predictor for reward shaping, is shown to significantly enhance Actor Critic using Kronecker-factored Trust Region and Proximal Policy Optimization in single-goal environments with visual inputs.
Conditional Deep Gaussian Processes: Empirical Bayes Hyperdata Learning

It is desirable to combine the expressive power of deep learning with Gaussian Process (GP) in one expressive Bayesian learning model. Deep kernel learning showed success as a deep network used for feature extraction. Then, a GP was used as the function model. Recently, it was suggested that, albeit training with marginal likelihood, the deterministic nature of a feature extractor might lead to overfitting, and replacement with a Bayesian network seemed to cure it. Here, we propose the conditional deep Gaussian process (DGP) in which the intermediate GPs in hierarchical composition are supported by the hyperdata and the exposed GP remains zero mean. Motivated by the inducing points in sparse GP, the hyperdata also play the role of function supports, but are hyperparameters rather than random variables. It follows our previous moment matching approach to approximate the marginal prior for conditional DGP with a GP carrying an effective kernel. Thus, as in empirical Bayes, the hyperdata are learned by optimizing the approximate marginal likelihood which implicitly depends on the hyperdata via the kernel. We show the equivalence with the deep kernel learning in the limit of dense hyperdata in latent space. However, the conditional DGP and the corresponding approximate inference enjoy the benefit of being more Bayesian than deep kernel learning. Preliminary extrapolation results demonstrate expressive power from the depth of hierarchy by exploiting the exact covariance and hyperdata learning, in comparison with GP kernel composition, DGP variational inference and deep kernel learning. We also address the non-Gaussian aspect of our model as well as way of upgrading to a full Bayes inference.
Secure Data Storage Analysis in Cloud Computing: An Advanced Approach

Cloud computing is the delivery of identical IT capacity (services, software, or infrastructure) via internet technologies on a pay-per-use, self-service basis. File management services based on cloud storage, such as DropBox, Google Drive, OneDrive, and Mega, are utilised by millions of people across the world. Features for rising of external file systems with DropBox, FTP, and GoogleDrive allow users to have all of their file sync and distribution tools in a single interface. To trust our data on other people's data centres, we use cloud storage services such as DropBox, Google Drive, OneDrive, and Mega Cloud. OwnCloud is an open source file sync and share platform that allows us to keep our data on our servers and choose which other public cloud services to integrate into our personal cloud of cloud storage size, security, and used in platform between online public cloud as OneDrive, GoogleDrive, DropBox, Mega, and private cloud storage OwnCloud with public cloud OpenShift and some file related storage analysis between them. Study objectives: &nbsp;The main objectives set forth for current study are: Study on Insecure cloud Storage. Study on Partial Secure Cloud Study on Secure Cloud. Comparative study on Comparisons of cloud storage and Analysis on Uploaded Files in OneDrive, Google Drive, DropBox, Mega and OwnCloud-OpenShift platforms.
Efficient Social Distancing Detection Using Object Detection and Triangle Similarity

COVID-19 or the Coronavirus Disease has been wreaking havoc all around the globe. People have lost their lives and livelihoods because of this contiguous disease that multiplies at a very fast rate. Although countries have prepared vaccines and have started with the process of vaccination, it has been advised by the government to follow the norms of wearing face masks, following social distancing and hand sanitization for atleast a few months from now so that the vaccine can be effective against the virus. Following Social Distancing is one of the ways we prevent the mass spreading of the virus. The proposed system uses Object Detection and Triangle Similarity techniques to check if people are following Social Distancing or not in Images, Videos and Webcam feeds. If any individual is found not following the norms, an alarm will be sounded to alert the person and the police officials.
Finding Significant Features for Few-Shot Learning Using Dimensionality Reduction

Few-shot learning is a relatively new technique that specializes in problems where we have little amounts of data. The goal of these methods is to classify categories that have not been seen before with just a handful of samples. Recent approaches, such as metric learning, adopt the meta-learning strategy in which we have episodic tasks conformed by support (training) data and query (test) data. Metric learning methods have demonstrated that simple models can achieve good performance by learning a similarity function to compare the support and the query data. However, the feature space learned by a given metric learning approach may not exploit the information given by a specific few-shot task. In this work, we explore the use of dimension reduction techniques as a way to find task-significant features helping to make better predictions. We measure the performance of the reduced features by assigning a score based on the intra-class and inter-class distance, and selecting a feature reduction method in which instances of different classes are far away and instances of the same class are close. This module helps to improve the accuracy performance by allowing the similarity function, given by the metric learning method, to have more discriminative features for the classification. Our method outperforms the metric learning baselines in the miniImageNet dataset by around 2% in accuracy performance.
DEVELOPMENT ONLINE LEARNING SYSTEM FOR SME AND COMMUNITY

The more rapid development of technology has an impact on learning methods. In the past, learning was only done traditionally, namely face-to-face, now the learning method can be done through online media, namely through internet intermediaries. With online media, the learning process can occur without any regional boundaries and all information can be accessed whenever we want. The purpose of this study is to provide a means for Small, Medium Enterprise (SME) and the community in an effort to increase creativity and innovation through the application of an online learning system. The implementation of this system uses an object-oriented programming approach and evaluation using Blackbox testing.
PERBANDINGAN KERNEL SUPPORT VECTOR MACHINE (SVM) DALAM PENERAPAN ANALISIS SENTIMEN VAKSINISASI COVID-19

In early 2020, the first recorded death from the COVID-19 virus in China [3]. Followed by WHO which later stated that the COVID-19 virus caused a pandemic. Various efforts were made to minimize the transmission of COVID-19, such as physical distancing and large-scale social circulation. However, this resulted in a paralyzed economy, many factories or business shops closed, eliminating the livelihoods of many people. Vaccines may be a solution, various International Research Communities have conducted research on the COVID-19 vaccine. In early 2021 the Sinovac vaccine from China arrived in Indonesia and was declared a BPOM clinical trial, but the existence of the vaccine still raises pros and cons, some have responded well and others have not. For this reason, a sentiment analysis of the COVID-19 vaccine will be carried out by taking data from Twitter, then classified using the Support Vector Machine algorithm. The research data is nonlinear data so it requires a kernel space for the text mining process, while there has been no specific research regarding which kernel is good for sentiment analysis, so a test will be carried out to find the best kernel among linear, sigmoid, polynomial, and RBF kernels. The result is that sigmoid and linear kernels have a better value, namely 0.87 compared to RBF and polynomial, namely 0.86
Synthetic Scene Character Generator and Ensemble Scheme with the Random Image Feature Method for Japanese and Chinese Scene Character Recognition

Scene character recognition has been intensively investigated for a couple of decades because it has a great potential in many applications including automatic translation, signboard recognition, and reading assistance for the visually-impaired. However, scene characters are difficult to recognize at sufficient accuracy owing to various noise and image distortions. In addition, Japanese scene character recognition is more challenging and requires a large amount of character data for training because thousands of character classes exist in the language. Some researchers proposed training data augmentation techniques using Synthetic Scene Character Data (SSCD) to compensate for the shortage of training data. In this paper, we propose a Random Filter which is a new method for SSCD generation, and introduce an ensemble scheme with the Random Image Feature (RI-Feature) method. Since there has not been a large Japanese scene character dataset for the evaluation of the recognition systems, we have developed an open dataset JPSC1400, which consists of a large number of real Japanese scene characters. It is shown that the accuracy has been improved from 70.9% to 83.1% by introducing the RI-Feature method to the ensemble scheme.
The influence of Using Virtual Reality Technology on Consumer Behavioral Intention in Real Estate Industry

Today, virtual reality (VR) technology as a new tool has been used by various organizations and a lot of research has been done on how it affects consumer behavior. Despite extensive studies, in Iran and especially in real estate industry, no research has been observed in this area and there is a research gap. On the other hand, given the global corona epidemic, which seems to be repeating itself, the need to develop online platforms and tools in this area is felt more than ever. Therefore, considering the importance of the issue, the present study has for the first time investigated the influence of using VR on consumer behavioral intention. The purpose of this study is to determine the relationship between factors affecting the sense of presence when using VR with WOM intention and patronage intention by mediating role of shopping experience and change in perceived value in real estate industry in Tehran. This study is categorized as applied and descriptive. The statistical population includes all housing buyers in Tehran who have used the VR housing site and application. Available sampling method was used to select the sample and data collection was done by a questionnaire. Data analysis was performed using the structural equation modeling (SEM) in Amos software. Findings showed that there is a positive and significant relationship between emotions and sense of presence with VR, and also between sense of presence and shopping experience. The shopping experience has a positive and significant effect on the change in perceived value. Perceived value has a positive and significant relationship with WOM intention and patronage intention. The mediating role of the shopping experience in the relationship between a sense of presence and the change in perceived value and also the mediating role of perceived value change in the relationship between the shopping experience with WOM intention and patronage intention was confirmed. Due to the novelty of VR technology in Iran and especially in real estate industry, the results of this study can be a way to use this new technology in the field of marketing.
PROBLEMS AND PROSPECTS OF STEM EDUCATION IMPLEMENTATION IN UKRAINE

It has been established that despite significant obstacles and stereotypes regarding the introduction of STEAM education in Ukraine, this process is inevitable, as leading Ukrainian educators claim that the previous Soviet concept of education is utopian and ineffective. It is determined that if Ukraine aspires to become a leading European state, then changing the education system is the main factor of success. It is stated that in the conditions of transition to the New modernized Ukrainian school the introduction of STEM education remains the most priority direction of the educational system of Ukraine. It is determined that due to the consolidation of efforts the introduction of STEAM education is becoming one of the real goals of the New Ukrainian School. Only in the last few years, the legal framework for the implementation of STEAM in Ukraine has been created, the Web-STEAM-school is working, the collection of materials "STEM-week" is published, scientific conferences are systematically held, a virtual STEM-center is opened, which is a huge step forward.
A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware  Image Synthesis

The advancement of generative radiance fields has pushed the boundary of 3D-aware image synthesis. Motivated by the observation that a 3D object should look realistic from multiple viewpoints, these methods introduce a multi-view constraint as regularization to learn valid 3D radiance fields from 2D images. Despite the progress, they often fall short of capturing accurate 3D shapes due to the shape-color ambiguity, limiting their applicability in downstream tasks. In this work, we address this ambiguity by proposing a novel shading-guided generative implicit model that is able to learn a starkly improved shape representation. Our key insight is that an accurate 3D shape should also yield a realistic rendering under different lighting conditions. This multi-lighting constraint is realized by modeling illumination explicitly and performing shading with various lighting conditions. Gradients are derived by feeding the synthesized images to a discriminator. To compensate for the additional computational burden of calculating surface normals, we further devise an efficient volume rendering strategy via surface tracking, reducing the training and inference time by 24% and 48%, respectively. Our experiments on multiple datasets show that the proposed approach achieves photorealistic 3D-aware image synthesis while capturing accurate underlying 3D shapes. We demonstrate improved performance of our approach on 3D shape reconstruction against existing methods, and show its applicability on image relighting. Our code will be released at https://github.com/XingangPan/ShadeGAN.
Data Augmentation of Backscatter X-ray Images for Deep Learning-Based Automatic Cargo Inspection

Custom inspection using X-ray imaging is a very promising application of modern pattern recognition technology. However, the lack of data or renewal of tariff items makes the application of such technology difficult. In this paper, we present a data augmentation technique based on a new image-to-image translation method to deal with these difficulties. Unlike the conventional methods that convert a semantic label image into a realistic image, the proposed method takes a texture map with a special modification as an additional input of a generative adversarial network to reproduce domain-specific characteristics, such as background clutter or sensor-specific noise patterns. The proposed method was validated by applying it to backscatter X-ray (BSX) vehicle data augmentation. The Fréchet inception distance (FID) of the result indicates the visual quality of the translated image was significantly improved from the baseline when the texture parameters were used. Additionally, in terms of data augmentation, the experimental results of classification, segmentation, and detection show that the use of the translated image data, along with the real data consistently, improved the performance of the trained models. Our findings show that detailed depiction of the texture in translated images is crucial for data augmentation. Considering the comparatively few studies that have examined custom inspections of container scale goods, such as cars, we believe that this study will facilitate research on the automation of container screening, and the security of aviation and ports.
Sentence Compression Using BERT and Graph Convolutional Networks

Sentence compression is a natural language-processing task that produces a short paraphrase of an input sentence by deleting words from the input sentence while ensuring grammatical correctness and preserving meaningful core information. This study introduces a graph convolutional network (GCN) into a sentence compression task to encode syntactic information, such as dependency trees. As we upgrade the GCN to activate a directed edge, the compression model with the GCN layers can distinguish between parent and child nodes in a dependency tree when aggregating adjacent nodes. Furthermore, by increasing the number of GCN layers, the model can gradually collect high-order information of a dependency tree when propagating node information through the layers. We implement a sentence compression model for Korean and English, respectively. This model consists of three components: pre-trained BERT model, GCN layers, and a scoring layer. The scoring layer can determine whether a word should remain in a compressed sentence by relying on the word vector containing contextual and syntactic information encoded by BERT and GCN layers. To train and evaluate the proposed model, we used the Google sentence compression dataset for English and a Korean sentence compression corpus containing about 140,000 sentence pairs for Korean. The experimental results demonstrate that the proposed model achieves state-of-the-art performance for English. To the best of our knowledge, this sentence compression model based on the deep learning model trained with a large-scale corpus is the first attempt for Korean.
Maritime navigational assistance by visual augmentation

Abstract Several types of equipment have been developed over the years to assist ship operators with their tasks. Nowadays, navigational equipment typically provides an enormous volume of information. Thus, there is a corresponding need for efficiency in how such information is presented to ship operators. Augmented reality (AR) systems are being investigated for such efficient presentation of typical navigational information. The present work is particularly interested in an AR architecture commonly referred as monitor augmented reality (MAR). In this context, the development of MAR systems is briefly summarised. The projection of three-dimensional elements into a camera scene is presented. Potential visual assets are proposed and exemplified with videos from a ship manoeuvring simulator and a real experiment. Enhanced scenes combining pertinent virtual elements are shown exemplifying potential assistance applications. The authors mean to contribute to the popularisation of MAR systems in maritime environments. Further research is suggested to define optimal combinations of visual elements for alternative maritime navigation scenarios. Note that there are still many challenges for the deployment of MAR tools in typical maritime operations.
Influence of Realistic Virtual Environments and Humanlike Avatars on Patients With Social Phobia

Abstract The practice of treating phobias with Virtual Reality-based therapies is a well-established field. Understanding the level of realism required by the therapy to be most effective is an essential matter of study. This research aims to explore the effects of visual realism on the emotional response in subjects with social phobia when exposed to VR-based applications. Social phobias are triggered by the presence of other people, which translated into virtual environments, refers to avatars. Our hypothesis is that patients with social phobia experience different emotional response to humanlike avatars compared to people without social phobia. To try the hypothesis, a prototype-based survey is conducted. Three types of avatars are implemented with different levels of human likeness: low, medium, and high. The analysis of the collected data suggests that for people with social phobias the anxiety is lowest for avatars with high levels of human likeness. This result is in direct contrast with the uncanny valley effect theory. The research explores how we should design virtual environments to make them more effective in the treatment of phobias. Moreover, the research produces new knowledge about the perception of humanlike avatars in virtual reality.