Reviewerly/exHarmony huggingface dataset:
A Hybird Framework based on Autoencoder and Deep Neural Networks for Fashion Image Classification

Deep learning has played a huge role in computer vision fields due to its ability to extract underlying and complex features of input images.Deep learning is applied to complex vision tasks to perform image recognition and classification.Recently, Apparel classification, is an application of computer vision, has been intensively explored and investigated.This paper proposes an effective framework, called DeepAutoDNN, based on deep learning algorithms for apparel classification.DeepAutoDNN framework combines a deep autoencoder with deep neural networks to extract the complex patterns and highlevel features of fashion images in supervised manner.These features are utilized via categorical classifier to predict the given image to the right label.To evaluate the performance and investigate the efficiency of the proposed framework, several experiments have been conducted on the Fashion-MNIST dataset, which consists of 70000 images: 60000 and 10000 images for training and test, respectively.The results have shown that the proposed framework can achieve accuracy of 93.4%.In the future, this framework performance can be improved by utilizing generative adversarial networks and its variant.
The global flood of COVID-19 contact tracing apps: sailing with human rights and data protection standards against the wind of mass surveillance

Research background: Countries all around the world are rapidly introducing contact tracing apps and other surveillance technologies to tackle the spread of COVID-19 raising serious concerns about human rights and democratic principles. Purpose of the article: The article aims to analyse how human rights and data protection law regulate the COVID-19 contact tracing apps and reveal the biggest challenges that countries face in applying the essential requirements. Methods: The article will analyse the legal framework and compare many guidance documents issued by the international organisations, including the Council of Europe, the OECD and many EU institutions on the data protection requirements for contact tracing tools and the main challenges the governments face in different countries. Findings &amp; Value added: The article will reveal that the existing human rights and data protection standards already impose significant requirements for contact tracing apps requiring to comply with such principles as legality, necessity, proportionality, transparency, purpose limitation, temporariness. Although countries tend to deviate from some of these standards a choice between effective response to crises and fundamental rights should not be made. The article argues that the global flood of digital surveillance technologies requires new regulatory framework and governance mechanisms to enable impact assessment, oversight and monitoring of these technologies both during and after the crises not only to ensure that they are lawful and ethical, but also to limit the dependency of governments on large technology companies as well as to prevent mass surveillance becoming the new normal on a global scale.
SISTEM RETENSI BERKAS REKAM MEDIS TERINTEGRASI: PERANCANGAN SISTEM INFORMASI BERBASIS WEB DI KLINIK PRATAMA

PKU Muhammadiyah Cangkringan Primary Clinic serves outpatient and inpatient services.The patient service process was still using microsoft excel, from patient registration to reporting.The problem that occurs was the accumulation of files due to not implemented the medical record file retention process, and the medical record file retention information system does not yet exist.This study aims to design a web based medical record file retention information system.The prototype design method was used as a method for developing web based information systems.The design using visual studio code editorial text and interface design with the help of a bootstrap framework, the system uses the laravel framework, and the database uses mysql.The result, a web based medical record file retention information system that can facilitate officers and doctors in viewing patient forms that have been retained, can save scans of medical record forms before they are destroyed, and can display scan results forms that have been uploaded into the information system.This information system requires patient data to see a list of visits of the last 2 years which is used to determine the medical record file that will be carried out the retention process.
Sentiment prediction based on analysis of customers assessments in food serving businesses

Human activities and behaviour in different domains are usually influenced by other people’s actions and opinion. Nowadays, it is evident that there is a growing research interest in sentiment analysis, evaluation and prediction. Content from web sources and social media is frequently used when people want to see others’ opinion about different things. Our research is focused on ML-based sentiment analysis of food services reviews data. The comparison of several regression models with regards to prediction of customer satisfaction of restaurant and food services is presented. The experimental data collected from food serving businesses located in Shanghai Lujiazui Commercial Zone includes keywords extracted from the customers’ written reviews. Additionally, the data are spatially labelled enabling to conduct separate analyses for different geographical regions. As a conclusion, the keywords extracted from the customer’s reviews were suitable for the prediction of three observed satisfaction criteria: food taste, service, and environment.
FVS-TECHNOLOGY: INTELLECTUAL SEARCH TOOLS

It is enough to have 3 basic stages of the modules in the SPD of a diversified corporate network: (F) - the method of submitting the request, i.e. the method of forming the expression of the information needs of the system user (S) - the function of the correspondence of the electronic resource to the request degree of compliance with the request and the found electronic resource; (V) - method of presenting electronic resources. Combining these three stages for models, methods, and software modules of the AML, is referred to as FSV technology (FSV platform, FSV Framework). FSV technology is an instrumental software platform based on a client-server architecture, integration and modification of models, and methods and algorithms of AML in the information environment of corporate networks. The following architecture has been developed for the FSV technology proposed for the search index in data retrieval systems. ABSTRAK: Tiga peringkat asas modul adalah cukup dalam pelbagai rangkaian korporat SPD iaitu: (F) - kaedah penyerahan permintaan, kaedah membentuk ungkapan keperluan maklumat pengguna sistem (S) - fungsi surat-menyurat sumber elektronik bagi permintaan tahap pematuhan permintaan dan sumber elektronik yang dijumpai; (V) - kaedah penyampaian sumber elektronik. Gabungan tiga peringkat model, kaedah dan modul perisian AML, dipanggil teknologi FSV (platform FSV, rangka FSV). Teknologi FSV adalah platform perisian instrumen berdasarkan seni bina pelanggan-pelayan, integrasi dan pengubah suaian model, kaedah-kaedah dan algoritma AML dalam persekitaran maklumat dalam rangkaian korporat. Seni bina ini telah di bina bagi teknologi FSV yang dicadangkan bagi indeks carian dalam sistem dapatan data.
PGL at TextGraphs 2020 Shared Task: Explanation Regeneration using Language and Graph Learning Methods

This paper describes the system designed by the Baidu PGL Team which achieved the first place in the TextGraphs 2020 Shared Task.The task focuses on generating explanations for elementary science questions.Given a question and its corresponding correct answer, we are asked to select the facts that can explain why the answer is correct for that question and answering (QA) from a large knowledge base.To address this problem, we use a pre-trained language model to recall the top-K relevant explanations for each question.Then, we adopt a re-ranking approach based on a pre-trained language model to rank the candidate explanations.To further improve the rankings, we also develop an architecture consisting both powerful pre-trained transformers and GNNs to tackle the multi-hop inference problem.The official evaluation shows that, our system can outperform the second best system by 1.91 points.
PolarNet: Learning to Optimize Polar Keypoints for Keypoint Based Object Detection

A variety of anchor-free object detectors have been actively proposed as possible alternatives to the mainstream anchor-based detectors that often rely on complicated design of anchor boxes. Despite achieving promising performance at par with anchor-based detectors, the existing anchor-free detectors such as FCOS or CenterNet predict objects based on standard Cartesian coordinates, which often yield poor quality keypoints. Further, the bounding box regression is also scale-sensitive. In this paper, we propose a new anchor-free keypoint based detector ``PolarNet, where keypoints are represented as a set of Polar coordinates instead of Cartesian coordinates. The ``PolarNet detector learns offsets pointing to the corners of objects in order to learn high quality keypoints. Additionally, PolarNet uses corner points to localize objects, making the localization scale-insensitive. Finally in our experiments, we show that PolarNet, an anchor-free detector, outperforms the existing anchor-free detectors, and is able to achieve highly competitive result on COCO test-dev benchmark (48.0% AP under the single-model single-scale testing) which is at par with the state-of-the-art two-stage anchor-based object detectors.
Particle Swarm Optimization For Improved Accuracy of Disease Diagnosis

The increasing number of patients suffering from various diseases and the impact and high cost of medical treatment for the community has made the government or health communities seek solutions for prevention from an early age. This valuable information can be found using artificial intelligence and data mining. Most diseases are dangerous; if detected early and adequate diagnosis and treatment are available, there will be a chance for a cure. The main objective of this study was to use Particle Swarm Optimization (PSO) to improve the accuracy of several classification methods, namely Naive Bayes, C4.5, Support Vector Machine (SVM), and Neural Network (NN) to detect heart disease, hepatitis, kidney, and breast cancer. The method used in this research is the CRISP-DM model, with 5 stages. The data used were four disease data from UCI Machine Learning. The result of this research is that PSO can improve the accuracy of Naive Bayes, C4.5, SVM, and NN.
The Role of Visuals in the Communication Process

Information flows, without which it is impossible to imagine a modern society and its cultural environment, penetrate into all spheres of an individual’s life thanks to digital technologies. As a result, social processes experience drastic changes in the ways and forms of cognition and transformation of the surrounding reality, as well as in the processes of formation and representation of the individual in society. As a result of the development of digital information technologies, significant changes have occurred in the living space and the perception of time by a person of the 21st century. The Internet has formed its own unique information space, with its own special social and technological characteristics. A virtual network is a space for global communication and high-speed data dissemination, as well as an environment for people’s interaction that is not limited by the barriers of traditional mass media. All this has created a new type of culture – the culture of virtual reality, since our reality consists mainly of everyday virtual experiences.
Analysis of English Listening Obstacles Based on Computer Assisted Instruction

The mining and application of data in sports training through intelligent computer-aided research, this paper proposes a system development and design method to improve the development and design efficiency of the data visualization system and reduce the development cost by using the Python language to handle the high efficiency of system programming and the convenience of charts.Thus, it further improves the efficiency and interactivity of the information management of training data and the convenience and stability of managing training data.This system is a preliminary exploration of Python language and Echarte technology in the management of training data of sports training programs.Because of the integration of training programs with computer application technology and data visualization technology, the system still needs to be strengthened in terms of data processing methods and the diversity of chart styles generated.In the future, the use of artificial intelligence, big data analysis, and other technologies in sports training data management and analysis has more room for development.The model only needs to measure 14 indicators of bipedal closed-eye standing and single-leg closed-eye standing movements to achieve the ability to predict the balance of motor training, and its accuracy meets the clinical requirements.This method has a shorter measurement time and fewer metrics than traditional methods.
Bilingual Lexicon Induction across Orthographically-distinct Under-Resourced Dravidian Languages

Bilingual lexicons are a vital tool for under-resourced languages and recent state-of-the-art approaches to this leverage pretrained monolingual word embeddings using supervised or semi- supervised approaches. However, these approaches require cross-lingual information such as seed dictionaries to train the model and find a linear transformation between the word embedding spaces. Especially in the case of low-resourced languages, seed dictionaries are not readily available, and as such, these methods produce extremely weak results on these languages. In this work, we focus on the Dravidian languages, namely Tamil, Telugu, Kannada, and Malayalam, which are even more challenging as they are written in unique scripts. To take advantage of orthographic information and cognates in these languages, we bring the related languages into a single script. Previous approaches have used linguistically sub-optimal measures such as the Levenshtein edit distance to detect cognates, whereby we demonstrate that the longest common sub-sequence is linguistically more sound and improves the performance of bilingual lexicon induction. We show that our approach can increase the accuracy of bilingual lexicon induction methods on these languages many times, making bilingual lexicon induction approaches feasible for such under-resourced languages.
Layers of classicality in the compatibility of measurements

The term ``layers of classicality'' in the context of quantum measurements was introduced by T. Heinosaari [Phys. Rev. A 93, 042118 (2016)]. The strongest layer among these consists of the sets of observables that can be broadcast and the weakest layer consists of the sets of compatible observables. There are several other layers in between those two layers. In this work, we study their physical and geometric properties and show the differences and similarities among the layers in these properties. In particular we show that (i) none of the layers of classicality respect transitivity property, (ii) the concept like degree of broadcasting similar to degree of compatibility does not exist, (iii) there exist informationally incomplete positive operator-valued measures that are not individually broadcastable, (iv) a set of broadcasting channels can be obtained through concatenation of broadcasting and nondisturbing channels, (v) unlike compatibility, other layers of classicality are not convex, in general. Finally, we discuss the relations among these layers. More specifically, we show that a specific type of concatenation relations among broadcasting channels decides the layer in which a pair of observables resides.
DocParser: Hierarchical Document Structure Parsing from Renderings

Translating renderings (e. g. PDFs, scans) into hierarchical document structures is extensively demanded in the daily routines of many real-world applications. However, a holistic, principled approach to inferring the complete hierarchical structure in documents is missing. As a remedy, we developed “DocParser”: an end-to-end system for parsing complete document structure – including all text elements, nested figures, tables, and table cell structures. Our second contribution is to provide a dataset for evaluating hierarchical document structure parsing. Our third contribution is to propose a scalable learning framework for settings where domain-specific data are scarce, which we address by a novel approach to weak supervision that significantly improves the document structure parsing performance. Our experiments confirm the effectiveness of our proposed weak supervision: Compared to the baseline without weak supervision, it improves the mean average precision for detecting document entities by 39.1% and improves the F1 score of classifying hierarchical relations by 35.8%.
Neural Relational Inference with Efficient Message Passing Mechanisms

Many complex processes can be viewed as dynamical systems of interacting agents. In many cases, only the state sequences of individual agents are observed, while the interacting relations and the dynamical rules are unknown. The neural relational inference (NRI) model adopts graph neural networks that pass messages over a latent graph to jointly learn the relations and the dynamics based on the observed data. However, NRI infers the relations independently and suffers from error accumulation in multi-step prediction at dynamics learning procedure. Besides, relation reconstruction without prior knowledge becomes more difficult in more complex systems. This paper introduces efficient message passing mechanisms to the graph neural networks with structural prior knowledge to address these problems. A relation interaction mechanism is proposed to capture the coexistence of all relations, and a spatio-temporal message passing mechanism is proposed to use historical information to alleviate error accumulation. Additionally, the structural prior knowledge, symmetry as a special case, is introduced for better relation prediction in more complex systems. The experimental results on simulated physics systems show that the proposed method outperforms existing state-of-the-art methods.
Cut-and-Paste Dataset Generation for Balancing Domain Gaps in Object Instance Detection

Training an object instance detector where only a few training object images are available is a challenging task. One solution is a cut-and-paste method that generates a training dataset by cutting object areas out of training images and pasting them onto other background images. A detector trained on a dataset generated with a cut-and-paste method suffers from the conventional domain shift problem, which stems from a discrepancy between the source domain (generated training dataset) and the target domain (real test dataset). Though state-of-the-art domain adaptation methods are able to reduce this gap, it is limited because they do not consider the difference of domain gaps of foreground and background. In this study, we present that the conventional domain gap can be divided into two sub-domain gaps for foreground and background. Then, we show that the original cut-and-paste approach suffers from a new domain gap problem, an unbalanced domain gaps, because it has two separate source domains for foreground and background, unlike the conventional domain shift problem. Then, we introduce an advanced cut-and-paste method to balance the unbalanced domain gaps by diversifying the foreground with GAN (generative adversarial network)-generated seed images and simplifying the background using image processing techniques. Experimental results show that our method is effective for balancing domain gaps and improving the accuracy of object instance detection in a cluttered indoor environment using only a few seed images. Furthermore, we show that balancing domain gaps can improve the detection accuracy of state-of-the-art domain adaptation methods.
Distance Learning in the Polish and Korean Universities During COVID-19 Pandemic

At work, distance learning methods have been compared in Silesian University of Technology in Gliwice (Poland) and Hankuk University of Foreign Studies in Seoul (South Korea).Th e results of the empirical research among the academic teachers and students conducted in both universities are similar: distance learning is not satisfying for both groups and it is not eff ective.
MoTBFs: An R Package for Learning Hybrid Bayesian Networks Using Mixtures of Truncated Basis Functions

This paper introduces MoTBFs, an R package for manipulating mixtures of truncated basis functions.This class of functions allows the representation of joint probability distributions involving discrete and continuous variables simultaneously, and includes mixtures of truncated exponentials and mixtures of polynomials as special cases.The package implements functions for learning the parameters of univariate, multivariate, and conditional distributions, and provides support for parameter learning in Bayesian networks with both discrete and continuous variables.Probabilistic inference using forward sampling is also implemented.Part of the functionality of the MoTBFs package relies on the bnlearn package, which includes functions for learning the structure of a Bayesian network from a data set.Leveraging this functionality, the MoTBFs package supports learning of MoTBF-based Bayesian networks over hybrid domains.We give a brief introduction to the methodological context and algorithms implemented in the package.An extensive illustrative example is used to describe the package, its functionality, and its usage.
Wayang Image Classification Using SVM Method and GLCM Feature Extraction

Wayang is a masterpiece of art that has been able to survive centuries of change and development as a reflection of life for the majority of society. Wayang has a high value because it does not only function as a "entertainment" spectacle, but also has many lessons and life values that can be learned from a wayang show. Puppet itself has various types and forms, and these forms have their own uniqueness, because of the many types of Puppet, many people do not know all the names and types of wayang. Therefore, in this research, we will discuss how to recognize wayang objects based on wayang images using the SVM and GLCM methods as feature extraction. The results showed that the classification of wayang using the SVM (Support Vector Machine) method and the GLCM (Gray Level Co-Occurrence Matrix) feature extraction can recognize wayang objects based on wayang images and classify them quite accurately and a maximum total accuracy of 83.2% is obtained.
Image denoising based on BCOLTA: Dataset and study

Robot deburring is an effective method for improving the surface quality of the high-voltage copper contact. The first step of robot deburring is to acquire the burr images. We propose a new burr mathematical model and build a real burr image dataset for burr image denoising. In order to improve burr image denoising effects of the high-voltage copper contact, this study proposes an online burr image denoising algorithm, that is, block cosparsity overcomplete learning transform algorithm (BCOLTA). The penalty term and the condition number are affected by the burr parameter. The clustering and transform alternate minimisation algorithms are adopted to achieve lower computational cost and better denoising effect. In addition, BCOLTA also has a good adaptibility to inherent noise images, especially in Gaussian noise. Compared with other traditional and deep learning algorithms by no reference and full reference image quality assessment methods, BCOLTA has state-of-the-art denoising effects and computational complexity on dealing with burr images. This research will play an important role in the intelligent manufacturing field.