Reviewerly/exHarmony huggingface dataset:
Quality in Use of Digital Wallet based on ISO/IEC 25022

The growth of financial technology (fintech) has led to an increase in cashless transactions. One of the technology that is developing and widely used is digital wallets. Because of the frequent use of digital wallet services, an assessment to measure quality in use needs to be done. Quality in use relates to user interaction with software when the product is used. The assessment standard used to measure quality in use is ISO/IEC 25022. The criteria assessed are effectiveness, efficiency, satisfaction, and freedom from risk. To strengthen the results obtained, a correlation between the existing criteria and the quality in use of digital wallets is sought. From these results, it will be known which criteria have the highest correlation to the quality in use of digital wallets. This study does not focus on assessing the quality in use of each digital wallet, but on digital wallets globally (in this study the digital wallets used are OVO, Gopay, and Dana) because after the results of the questionnaire, almost all respondents use more than one digital wallet, even besides the mentioned digital wallets. The conclusion obtained in this study is that digital wallet product users are satisfied with the use of digital wallets although there are still some risks that may arise.
Proving the conjecture of O’Donnell in certain cases and disproving its general validity

For a function f:{−1,1}n→{−1,1} the relationship between the sum of its linear Fourier coefficients f̂(i) (defined by f̂(i)≔12n∑x∈{−1,1}nf(x)xi for i=1,2,…,n and x=(x1,…,xn)) and its degree d is a problem in theoretical computer science related to social choice. In that regard, in 2012, O’Donnell conjectured that ∑i=1nf̂(i)≤dd−1⌊d−12⌋21−d. In 2020, Wang (Wang, 2020) proved that the conjecture is equivalent to a conjecture about cryptographic resilient Boolean functions and proved that the conjecture is true for every n, when d=1 and d=n−1. In this paper, we contribute theoretically to this problem by showing that if the conjecture is true for some specific relatively small value n0, which depends on d, then it is valid for any n≥n0. In particular, we show that the conjecture is correct for every n, when d=2 and d=3. However, despite these promising results, we present a counterexample when d=4, and so the conjecture is not true in general.
Non-norm-bounded Attack for Generating Adversarial Examples

Recent studies have demonstrated that neural networks are vulnerable to adversarial examples. Numerous attacks have been proposed for crafting various types of adversarial examples. However, almost all the existing attacks adopt the $$L_p$$ -norm or another distance metric to bound the adversarial perturbations, which inadvertently facilitates the implementation of some defenses. We present a novel non-norm-bounded attack (NNBA) for generating adversarial examples. We formulate the process of generating adversarial examples as an optimization problem, which has just the objective of misclassifying the perturbed examples and does not use the $$L_p$$ -norm as the perturbation constraint. The examples generated in this way naturally satisfy the requirements of adversarial examples. Experimental results on the MNIST, CIFAR-10 and ImageNet datasets show that NNBA can successfully generate adversarial examples with small perturbations and high misclassification performance. Moreover, adversarial examples crafted by NNBA achieve high confidence, good robustness and low computational cost. Our work sheds light on a new type of adversarial attack, and we hope it will prompt research on secure and robust machine learning models.
Research and Design of Fast Special Human Face Recognition System

During the use of face recognition systems appears the problems such as the system can not find a face and has not enough information of the image, changing illumination, occlusion, face shape changes and so on. In order to solve the current problems and improve the ability of face recognition and detection, new methods for data acquisition and some tools are adopted, and the application development of improved face recognition system is proposed based on ARM architecture design, software development of face recognition system is taken in ARM embedded platform. The solve project for the acquisition of three-dimensional face data, biometric recognition, computer vision, computer graphics is researched and designed. The image edge contour detection method is used for face image features positioning. The adaptive feature extraction method of image entropy is used for facial feature extraction, the face recognition algorithm is optimized, and the face image output module is constructed. The simulation results show that the face recognition system designed in this paper has good robustness performance, and it can improve the accurate probability of face recognition.
PENGAPLIKASIAN TEKNIK BLOCK PRINTING PADA MATERIAL KAIN TENUN ATBM UNTUK PERANCANGAN PRODUK FASHION

Abstract The Print Block was received from China after the 3rd start. Materials commonly used for block printing techniques are wood or linoleum as printing plates and fabric materials with the character of flat and smooth fabric surfaces such as silk, cotton, linen, and so on. Indonesia is a nation that has a long-developed literary culture. One typical Indonesian literature is a woven fabric made from Non-Machine Weaving Equipment (ATBM). The use of ATBM woven cloth has the potential that can be used as a print block print media and can be an innovation in the use of materials in block printing techniques. The study was conducted using qualitative research methods with data collection techniques, literature studies, book searches, journals to obtain basic theory and field observations, direct interviews to obtain knowledge about ATBM (handwoven) fabrics and block printing techniques, and to search on ATBM (handwoven) woven fabric media using a block printing technique. The results of ATBM woven fabric using this block printing technique will be made the main ingredient in the design of fashion products using geometric motifs taken from taking the concept of suprematism painting, then the linen fabric results are made of fashion products, ready to wear deluxe clothing to prove the potential application of fabric ATBM woven can be made as print block print media and made as a fashion product. Keywords: Block printing, ATBM, fashion, suprematism, ready to wear deluxe
A Fuzzy Logic Approach in assessing the trustworthiness of Instagram Sellers: A Customer's Perspective

Instagram has become a chosen platform for people to buy and sell online. Nevertheless, some studies have reported that frauds happened from buying and selling through the platform such as product quantity and specification is different as claimed, receipt of a defect product, and other frauds involving Instagram sellers. Hence, trust is vital when customers are engaged in S-Commerce activities on Instagram and there is a need for a trust model to evaluate trustworthiness of sellers. This paper proposes a Fuzzy Logic method in assessing the trustworthiness of Instagram Sellers, from a Customers' perspective. The trustworthiness of three Instagram Sellers was evaluated using the Fuzzy Inference System and values indicated moderate to high trust with values of more than 0.500. The study is expected to provide customers with a guideline on which factors that are important in assessing an Instagram Seller and a model to evaluate and quantify the trustworthiness of Instagram Sellers.
Energy Efficient Next-Gen of Virtualization for Cloud-native Applications in Modern Data Centres

In the new software-driven world this is the need of the hour to achieve success by accelerating development, delivery of applications and services rapidly that make customers happy and business competitive. To achieve faster delivery of resources and applications to strengthen the business with more transformations containers have been introduced. At the existing scenario, the hyper-converged data centre uses the concept of virtualization which creates software abstraction of the underlying hardware using Hypervisor software that enables to execute several Virtual Machines (VMs) with dissimilar Operating System (OS) flavours. But virtualization is heavyweight and it may take more time to boot. The proposed containerization and docker in hybrid cloud composable data centre is lightweight supports Operating System (OS) level virtualization that isolates resources, libraries and other binaries bundled into a single package for agile modern scalable workloads. This paper focuses on the container-based virtualization to shape the future cloud-based modern data centre for supporting the micro-services based applications for faster deployment to Developer Operations (DevOps) teams in IT with resiliency, High Availability and better resource management with energy savings.
The Influence of Hypnoteaching Method on EFL Learners’ Reading Motivation and Achievement

This pre-experimental research was aimed to examine the influence of hypnoteaching method toward students’ reading motivation and result in Madrasah Aliyah Misbahul Ulum Lhokseumawe. The population of this study was all students of class 2 Madrasah Aliyah Misbahul Ulum Lhokseumawe. The sample was class 2C students of Madrasah Aliyah Misbahul Ulum Lhokseumawe. Motivation for reading Questioner (MRQ) and test were the research instruments to gain the data quantitatively. Motivation for reading Questionnaire (MRQ) adopted from Wigfield and Guthrie (1997) to examine students’ reading motivation levels. For reading achievement, pre-test and post-test were distributed to assess the students’ reading outcome about Narrative Text before and after applying hypnoteaching method. The result of this research showed that the implementation of hypnoteaching method affect students reading motivation and learning outcome. The mean score of pre motivation for reading Questionnaire (MRQ) was 40.2 indicated poor reader and it raised to be 81.1 showing good reader after the implementation of hypnoteaching method. Furthermore, hypnoteaching method also affected English reading result which the mean of pre-test was 45.30 indicating poor score criterion of English subject, meanwhile, after being taught applying hypnoteaching method, the score inclined to 90.25 which asserted good score criterion of English subject. The value of t test was 14.88 from data of pre-test and post-test, however the value of t tabel was 1.99. It represented that the alternative hypothesis (H 1 ) was accepted and revealed that hypnoteaching method can influence students’ reading achievement.
Exploring the Skill Retention of Circuit Simulation Learning from the Perspective of Sustainability

Abstract The purpose of this study is from the perspective of sustainability, from the three aspects of sustainability: environment, society, and economy, design an experimental course to compare the skill retention about three kinds of electronic circuit teaching methods in vocational school. The experimental samples are selected from the 2nd grade students of Department of Control Engineering, Taipei Municipal Nei-Hu Industrial Vocational School. They are divided into three groups for an 8-week teaching experiment. Traditional group (TRA group) uses traditional lecture and operation methods; the second group (DB group) uses Digital Breadboard simulation software to help construct electric circuit conception and actual operation; and the third group (TINA group) uses TINA Pro simulation software to carry out conception learning for electronic circuit and actual operation. The differences in the learning retentions are analyzed using statistical software SPSS 25. For the skill retentions of circuit theory learning, after three times of non-warning methods, the post-skills retention was evaluated. After the 14th day of the experimental course, TRA group and DB group were significantly better than TINA group; and on the 42nd day after the end of the experimental course, TRA group and DB group were significantly better than TINA group; and on the 28th day after the end of the experimental course, all TRA group, DB group, and TINA group were no differences in the three groups of retention. At the same time, from the perspective of sustainable education, the teaching and evaluation of this experimental course has also achieved the purpose of sustainability.
Detection of Duplicate Bug Reports in Jira and Bugzilla Tools

Re-reporting a bug by the same or another person is defined as duplicate bug report. Bugzilla and Jira are two of most widely used bug tracking tools in the world. When both tools are evaluated in terms of having automatic detection of duplicate bug report, it is shown that Jira with Find Duplicates plugin is better compared with Bugzilla.
Recognizing Human Gestures Using Ambient Light

In this paper, we present LiGeR, a gesture recognition system that leverages ambient light to recognize human gestures in indoor environments. LiGeR is based on the observation that when a user performs a gesture in a room that is lit with light, the amount of light that he/she reflects and blocks changes, resulting in changes in the intensity of light in all parts of the room. The patterns of changes in the intensity of light are different for different gesture. LiGeR first learns these patterns for different gestures by applying machine learning techniques on the training samples of those gestures, and then recognizes the gestures in real-time when the user performs them. In designing LiGeR, we overcame several challenges such as automatic detection of gestures, handling changes in the intensity of light, and handling varying gesture speeds. We implemented LiGeR using cheap commercially available light sensors and Arduino boards and evaluated it in several real-world environments. Our results show that LiGeR achieves an average gesture recognition accuracy of over 95%.
Generative Image Inpainting

Recently, image is becoming more and more important as a carrier of information, and the demand of image inpainting is increasing. We present an approach for image inpainting in this paper. The completion model contains one generator and double discriminators. The generator is the architecture of AutoEncoders with skip connection and the discriminators are simple convolutional neural networks architecture. Wasserstein GAN loss is used to ensure our model’s stable training. We also give the algorithm of training our model in this paper.
A compositional view of derivations as interactive processes with applications to regulated and distributed rewriting

The derivation process in rewriting systems, especially in Chomskyan grammars, can be seen as consisting of four stages: (1) selection of the production to apply from a supply of productions available at that step, (2) check of applicability of the production, possibly on different matches, (3) selection of the match at which to apply the production, (4) actual application. In this paper, we explore the different ways in which models of regulated and distributed rewriting have extended these four basic mechanisms and we propose a unifying perspective on them in terms of interaction between two agents, expressed through a particular form of conditional rewriting systems. This introduces a compositional view of derivation processes, where different components, playing specific roles and exhibiting specific behaviours, can be made to communicate and combined into different models of derivation. Existing models of regulated and distributed rewriting can thus be reformulated in this new framework, and new ones can be obtained.
Product Consistency Joint Detection Algorithm Based on Deep Learning

At present, the consistency detection of products mainly relies on manual method which has defects such as difficulty in identifying similar categories and low efficiency. Recently, the methods which are based on image classification have been widely used in it. However, image classification is based merely on simple visual features without making full use of other categrizable attributes. The approach this paper adopt to solve the problem is a joint detection algorithm which is based on deep learning. This algorithm locates product areas and implements image segmentation according to the default information code. Then it takes the segmented image as input of convolutional neural network model for classification. A reflection-assisted decision-making algorithm is used to solve the defect that the approximate class output probability is unstable. Finally, the consistency judgment is made according to the extracted default class information and product category. The experimental results obtained in this research show that the proposed algorithm can segment the products image accurately and quickly. The joint decision-making algorithm has better accuracy and stability than the single classification network.
Multi-Scale Dense Attention Network for Stereo Matching

To improve the accuracy of stereo matching, the multi-scale dense attention network (MDA-Net) is proposed. The network introduces two novel modules in the feature extraction stage to achieve better exploit of context information: dual-path upsampling (DU) block and attention-guided context-aware pyramid feature extraction (ACPFE) block. The DU block is introduced to fuse different scale feature maps. It introduces sub-pixel convolution to compensate for the loss of information caused by the traditional interpolation upsampling method. The ACPFE block is proposed to extract multi-scale context information. Pyramid atrous convolution is adopted to exploit multi-scale features and the channel-attention is used to fuse the multi-scale features. The proposed network has been evaluated on several benchmark datasets. The three-pixel-error evaluated over all ground truth pixels is 2.10% on KITTI 2015 dataset. The experiment results prove that MDA-Net achieves state-of-the-art accuracy on KITTI 2012 and 2015 datasets.
Decision Making with Abundance of Information

Decision-making models in the presence of uncontrolled factors are considered. It is assumed that the operating party has access to large amounts of information characterizing these uncontrolled factors. Estimates of the amount of "useful" information are obtained. Three different ways of determining the concept of "quantity of information" are considered.
High-dimensional data visualisation with the grand tour

In physics we often encounter high-dimensional data, in the form of multivariate measurements or of models with multiple free parameters. The information encoded is increasingly explored using machine learning, but is not typically explored visually. The barrier tends to be visualising beyond 3D, but systematic approaches for this exist in the statistics literature. I use examples from particle and astrophysics to show how we can use the “grand tour” for such multidimensional visualisations, for example to explore grouping in high dimension and for visual identification of multivariate outliers. I then discuss the idea of projection pursuit, i.e. searching the high-dimensional space for “interesting” low dimensional projections, and illustrate how we can detect complex associations between multiple parameters.
A General Model for the Design of Efficient Sign-Coding Tools for Wavelet-Based Encoders

Traditionally, it has been assumed that the compression of the sign of wavelet coefficients is not worth the effort because they form a zero-mean process. However, several image encoders such as JPEG 2000 include sign-coding capabilities. In this paper, we analyze the convenience of including sign-coding techniques into wavelet-based image encoders and propose a methodology that allows the design of sign-prediction tools for whatever kind of wavelet-based encoder. The proposed methodology is based on the use of metaheuristic algorithms to find the best sign prediction with the most appropriate context distribution that maximizes the resulting sign-compression rate of a particular wavelet encoder. Following our proposal, we have designed and implemented a sign-coding module for the LTW wavelet encoder, to evaluate the benefits of the sign-coding tool provided by our proposed methodology. The experimental results show that sign compression can save up to 18.91% of bit-rate when enabling sign-coding capabilities. Also, we have observed two general behaviors when coding the sign of wavelet coefficients: (a) the best results are provided from moderate to high compression rates; and (b) the sign redundancy may be better exploited when working with high-textured images.
Cloud Agent-Based Encryption Mechanism (CAEM): A Security Framework Model for Improving Adoption, Implementation and Usage of Cloud Computing Technology

Fast Growth of (ICT) Information and Communication Technology results to Innovation of Cloud Computing and is considered as a key driver for technological innovations, as an IT innovations, cloud computing had added a new dimension to that importance by increasing usage to technology that motivates economic development at the national and global levels. Continues need of higher storage space (applications, files, videos, music and others) are some of the reasons for adoption and implementation, Users and Enterprises are gradually changing the way and manner in which Data and Information are been stored. Storing/Retrieving Data and Information traditionally using Standalone Computers are no longer sustainable due to high cost of Peripheral Devices, This further recommends organizational innovative adoption with regards to approaches on how to effectively reduced cost in businesses. Cloud Computing provides a lot of prospects to users/organizations; it also exposes security concerns which leads to low adoption, implementation and usage. Therefore, the study will examine standard ways of improving cloud computing adoption, implementation and usage by proposing and developing a security model using a design methodology that will ensure a secured Cloud Computing and also identify areas where future regularization could be operational.
An Optimized Hybrid Clustering Method Using Salp Swarm Optimization with K-Means

The field of data clustering is one of the commonly used techniques to analyze the data based on their structure. The use of clustering has found many applications in the field of data mining, image analysis, pattern recognition, and signal processing. This paper presents a hybrid scheme of data clustering by utilizing K-means clustering with salp swarm algorithm (SSA). The SSA is used to solve the problem of local optima for K-means and provide best suitable fitness value for the dataset. The experimental results show best suitable fitness value for different standard datasets by m the standard deviation. The experiment is carried out in MATLAB.
Metadata: The accelerant we need

Large-scale pandemic events have sent scientific communities scrambling to gather and analyze data to provide governments and policy makers with information to inform decisions and policies needed when imperfect information is all that may be available. Historical records from the 1918 influenza pandemic reflect how little improvement has been made in how government and policy responses are formed when large scale threats occur, such as the COVID-19 pandemic. This commentary discusses three examples of how metadata improvements are being, or may be made, to facilitate gathering and assessment of data to better understand complex and dynamic situations. In particular, metadata strategies can be applied in advance, on the fly or even after events to integrate and enrich perspectives that aid in creating balanced actions to minimize impacts with lowered risk of unintended consequences. Metadata can enhance scope, speed and clarity with which scholarly communities can curate their outputs for optimal discovery and reuse. Conclusions are framed within the Metadata 2020 working group activities that lay a foundation for advancement of scholarly communications to better serve all communities.