Reviewerly/exHarmony huggingface dataset:
Pathology Synthesis of 3D Consistent Cardiac MR Images Using 2D VAEs and GANs

We propose a method for synthesizing cardiac MR images with plausible heart shapes and realistic appearances for the purpose of generating labeled data for deep-learning (DL) training. It breaks down the image synthesis into label deformation and label-to-image translation tasks. The former is achieved via latent space interpolation in a VAE model, while the latter is accomplished via a conditional GAN model. We devise an approach for label manipulation in the latent space of the trained VAE model, namely pathology synthesis, aiming to synthesize a series of pseudo-pathological synthetic subjects with characteristics of a desired heart disease. Furthermore, we propose to model the relationship between 2D slices in the latent space of the VAE via estimating the correlation coefficient matrix between the latent vectors and utilizing it to correlate elements of randomly drawn samples before decoding to image space. This simple yet effective approach results in generating 3D consistent subjects from 2D slice-by-slice generations. Such an approach could provide a solution to diversify and enrich the available database of cardiac MR images and to pave the way for the development of generalizable DL based image analysis algorithms. The code will be available at https://github.com/sinaamirrajab/CardiacPathologySynthesis .
A Systematic Literature Review on Progressive Web Application Practice and Challenges

This research aims to establish a knowledge of Progressive Web Application (PWA) method practices based on published empirical investigation, the problems that the PWA method may face, and the PWA method's approach to experimental studies. We also looked for PWA practices that might address the issues with the prior method. We performed a broad, automated search to find SLRs and 43 papers published from 1 January 2015 to 6 November 2021 that discuss PWA. Specific inclusion and exclusion criteria were applied to determine which relevant studies we used for our research purposes. The review identified 31 practices of PWA, Six challenges of the previous approach of mobile application development that get resolved by PWA, and seven challenges posed by the practice of PWA. PWA was introduced in 2015, and since 2017 the studies on PWA topics have started increasing. The distribution of studies based on nations is spread evenly. 74\% of selected studies are about the practice of the PWA. Although it cannot be generalized that the PWA approach handles the challenge of the previous method, some studies reported that PWA covers the problems of the previous methods. However, despite its benefits, PWA still has some challenges, mostly related to browser support.
GRU-based Buzzer Ensemble for Abnormal Detection in Industrial Control燬ystems

Recently, Industrial Control Systems (ICSs) have been changing from a closed environment to an open environment because of the expansion of digital transformation, smart factories, and Industrial Internet of Things (IIoT).Since security accidents that occur in ICSs can cause national confusion and human casualties, research on detecting abnormalities by using normal operation data learning is being actively conducted.The single technique proposed by existing studies does not detect abnormalities well or provide satisfactory results.In this paper, we propose a GRU-based Buzzer Ensemble for Abnormal Detection (GBE-AD) model for detecting anomalies in industrial control systems to ensure rapid response and process availability.The newly proposed ensemble model of the buzzer method resolves False Negatives (FNs) by complementing the limited range that can be detected in a single model because of the internal models composing GBE-AD.Because the internal models remain suppressed for False Positives (FPs), GBE-AD provides better generalization.In addition, we generated mean prediction error data in GBE-AD and inferred abnormal processes using soft and hard clustering.We confirmed that the detection model's Time-series Aware Precision (TaP) suppressed FPs at 97.67%.The final performance was 94.04% in an experiment using an HIL-based Augmented ICS (HAI) Security Dataset (ver.21.03) among public datasets.
İş Memnuniyeti Faktörlerini Belirlemek ve Analiz Etmek için Çevrimiçi Çalışan Değerlendirmelerini Kullanan ilgili Makine Öğrenmesi ve Topluluk Öğrenmesi Tabanlı Yöntem

In this paper it was emphasized that machine learning techniques can achieve high performance in classification and work effectively and scalably with large data sets. The dataset used in this study was obtained from www.kaggle.com. A total of 67529 comments collected from people working at Google, Amazon, Netflix, Facebook, Apple and Microsoft were evaluated. The N-gram model is an important representation scheme in text mining. N-gram models are the unigram model (N = 1), bigram (N = 2), and trigram (N = 3). Three different weighting schemes as TP, TF, and TF-IDF, and three different weighting schemes for traditional machine learning-based analysis as N-gram model (bigram, unigram and trigram) was used. Five supervised learning algorithm was used to train models: Naive Bayes, Support Vector Machines (SVM), Logistic Regression (LR), K-Nearest Neighbor (KNN) and Random Forest (RF).
Nonclassical properties of a deformed atom-cavity field state

We analyse here a nonclassical state produced by an atom-cavity field interaction. The two-level atom is passed through the single-mode electromagnetic cavity field. By deforming the field operators and introducing nonlinearity to the classic Jaynes–Cummings model, we explore the system in respect of a nonlinear Hamiltonian. Assuming that the atom is in an excited state and the field is in a coherent state initially, the analytic expression for the state vector of the entire system state vector is obtained. With the help of the derived state vector, we calculate the photon number distribution, Mandel's QM parameter, Wigner function, anti-bunching, squeezing properties and Q function etc.
Iterative Qubits Management for Quantum Search

Recent advances in quantum computing systems attract tremendous attention. Commercial companies, such as IBM, Amazon, and IonQ, have started to provide access to noisy intermediate-scale quantum computers. Researchers and entrepreneurs attempt to deploy their applications that aim to achieve a quantum speedup. Grover&amp;rsquo;s algorithm and quantum phase estimation are the foundations of many applications with the potential for such a speedup. While these algorithms, in theory, obtain marvelous performance, deploying them on existing quantum devices is a challenging task. For example, quantum phase estimation requires extra qubits and a large number of controlled operations, which are impractical due to low-qubit and noisy hardware. To fully utilize the limited onboard qubits, we develop a distributed application with a key-value data structure based on Grover&amp;rsquo;s algorithm called IQuCS . Consider a database with duplicates. By encoding each element to a binary type with a unique key and forming a key-value pair, we can count the number of occurrences of each element in the database based on quantum computing. We have optimized the operation process by filtering data points to make it more efficient. To determine the effect of this optimization, we evaluate it with datasets of different sizes and with different numbers of duplicates. With the assistance of classical computers, IQuCS can reduce the problem set for each query. Due to this reduction, IQuCS requires fewer qubits. Through the iterative management, IQuCS achieves a reduction of qubit virtualized consumption, up to 66.2%, with reasonable accuracy.
Auxiliary System for Contract Signing Based on Electronic Signature Technology

With the rapid development of the Internet, a large part of people’s work and life has been transferred to the computer. Now, the processes of file transfer and data storage in people’s work are all completed by computers. In the process of working with computers, how to ensure the legality, traceability, antitampering, and uniqueness of documents is an important part of the development of computers. In order to ensure the security in the process of electronic contract authentication, this study has carried out a detailed analysis and design of electronic signature technology. In this study, PKI technology and USB Key technology are combined, which effectively prevents the tampering of electronic signatures and electronic seals in electronic contracts. This study uses PKI technology to strengthen the application and distribution of electronic seals and achieve the purpose of copyright protection of digital media. The improved electronic signature technology in this study can improve the security of signing electronic contracts at work and make users’ electronic signatures indestructible and noncopyable.
Time-Aware Anonymization of Knowledge Graphs

Knowledge graphs (KGs) play an essential role in data sharing because they can model both users’ attributes and their relationships. KGs can tailor many data analyses, such as classification where a sensitive attribute is selected and the analyst analyzes the associations between users and the sensitive attribute’s values (aka sensitive values). Data providers anonymize their KGs and share the anonymized versions to protect users’ privacy. Unfortunately, an adversary can exploit these attributes and relationships to infer sensitive information by monitoring either one or many snapshots of a KG. To cope with this issue, in this paper, we introduce ( k , l )-Sequential Attribute Degree (( k , l )-sad), an extension of the k w -tad principle[10], to ensure that sensitive values of re-identified users are diverse enough to prevent them from being inferred with a confidence higher than \(\frac{1}{l} \) even though adversaries monitor all published KGs. In addition, we develop the Time-Aware Knowledge Graph Anonymization Algorithm to anonymize KGs such that all published anonymized versions of a KG satisfy the ( k , l )-sad principle, by, at the same time, preserving the utility of the anonymized data. We conduct experiments on four real-life datasets to show the effectiveness of our proposal and compare it with k w -tad.
Improving Node Classification through Convolutional Networks Built on Enhanced Message-Passing Graph

Enhancing message propagation is critical for solving the problem of node classification in sparse graph with few labels. The recently popularized Graph Convolutional Network (GCN) lacks the ability to propagate messages effectively to distant nodes because of over-smoothing. Besides, the GCN with numerous trainable parameters suffers from overfitting when the labeled nodes are scarce. This article addresses the problem via building GCN on Enhanced Message-Passing Graph (EMPG). The key idea is that node classification can benefit from various variants of the input graph that can propagate messages more efficiently, based on the assumption that the structure of each variant is reasonable when more unlabeled nodes are labeled properly. Specifically, the proposed method first maps the nodes to a latent space through graph embedding that captures the structural information of the input graph. Considering the node attributes together, the proposed method constructs the EMPG by adding connections between the nodes in close proximity in the latent space. With the help of the added connections, the EMPG allows a node to propagate its message to the right nodes at long distances, so that the GCN built on the EMPG need not stack multiple layers. As a result, over-smoothing is avoided. However, dense connections may cause message propagation saturation and lead to overfitting. Seeing the EMPG as an accumulation of some potential variants of the original graph, the proposed method utilizes dropout to extract a group of variants from the EMPG and then builds multichannel GCNs on them. The multichannel features learned from different dropout EMPGs are aggregated to compute the final prediction jointly. The proposed method is flexible, as a brod range of GCNs can be incorporated easily. Additionally, it is efficient and robust. Experimental results demonstrate that the proposed method yields improvements in node classification.
ASK: Adversarial Soft k-Nearest Neighbor Attack and Defense

K-Nearest Neighbor (kNN)-based deep learning methods have been applied to many applications due to their simplicity and geometric interpretability. However, the robustness of kNN-based deep classification models has not been thoroughly explored and kNN attack strategies are underdeveloped. In this paper, we first propose an Adversarial Soft kNN (ASK) loss for developing more effective kNN-based deep neural network attack strategies and designing better defense methods against them. Our ASK loss provides a differentiable surrogate of the expected kNN classification error. It is also interpretable as it preserves the mutual information between the perturbed input and the in-class-reference data. We use the ASK loss to design a novel attack method called the ASK-Attack (ASK-Atk), which shows superior attack efficiency and accuracy degradation relative to previous kNN attacks on hidden layers. We then derive an ASK-Defense (ASK-Def) method that optimizes the worst-case ASK training loss. Experiments on CIFAR-10 (ImageNet) show that (i) ASK-Atk achieves ≥ 13% (≥ 13%) improvement in attack success rate over previous kNN attacks, and (ii) ASK-Def outperforms the conventional adversarial training method by ≥ 6.9% (≥ 3.5%) in terms of robustness improvement. Relevant codes are available at https://github.com/wangren09/ASK.
Theory on Non-Technical Characteristics Affecting Process Adoption in Small Software Companies: A Grounded Theory Study

Despite the inefficiency of software processes and products, small software companies (SSCs) enjoy a promising future. These companies are known to have few employees, creating the inefficiency that makes it challenging for the SSCs to adopt effective software practices. This subsequently introduces additional complexity, affecting software engineering processes’ adoption. Using the Glaserian Grounded Theory, we conducted interviews (N=18) with participants from SSCs intensively engaged in software development from four countries. We looked for the common traits that are identifiable as antecedents to the number of employees in a company to affect the adoption of software engineering processes. From the participants’ experience, five non-technical characteristics (Risk, Competitive advantage, Resilience, Innovative capacity, and Management ability) emerged, complementing the number of employees to affect the process during software practice in SSCs. By the end of this study, we developed five hypotheses for predicting and explaining the adoption of software engineering processes by small software companies.
Algorithm Design for Sharing Orchestral Network Teaching Resources Using New Media Platform

The new educational media platforms were introduced to address the problems related to sharing teaching content and promoting students’ learning abilities. However, there are several issues with distributing educational resources on new media platforms, both globally and domestically. In terms of resources, teaching resources are scarce and individual and lack systematization, which affects the implementation and development of our orchestral education and teaching in new media. Regarding technology, there are currently vacancies in data linkage and exchange between different databases when the resource sharing platform is used and the existing system is in operation. The packet loss rate created by the existing technology is far too high. To address this problem, this paper designs a new platform as well as an algorithm, applies to online teaching resource distribution based on a new media platform, plans the teaching resource attributes of orchestral music majors, and studies the optimal solution for the resource sharing game model regarding load balancing and honesty allocation principles in a distributed scenario of heterogeneous networks. Experiments show that our strategy reduces network demand by a significant amount. The results made the designed algorithm applicable to be created, developed, and deployed practically.
Criteria and indicators for selecting digital tools of video conferencing for e-learning implementation in the institution of postgraduate pedagogical education

This article is devoted to one of the actual problems of today-the organization of the process of professional development in the institution of postgraduate pedagogical education by distance learning.In particular, the selection of digital tools of video conferencing for synchronous electronic learning the implementation.The criteria and indicators for the selection of the necessary digital media are identified and described by the educational institution, and based on the analysis of scientific publications, legal documents, relevant software applications.So, it is advisable to apply operational and functional criteria.The indicators of the first criterion include the following: requirements for user hardware and software; payment for using; duration of the communication session; security and confidentiality; conditions of connection of participants; language of interface; availability of an application for mobile devices.Among the indicators of the second criterion are the following: the number of functionalities of the moderator; the number of functional capabilities of the participant; availability of a virtual interactive whiteboard and its functionality; monitoring the activity of participants.The importance of the proposed criteria and indicators is confirmed by the method of expert Вісник післядипломної освіти.Випуск 21(50) Серія «Педагогічні науки» (Категорія «Б») Bulletin of Postgraduate education.
TASK-BASED LANGUAGE TEACHING FOR ENHANCING STUDENTS’ SPEAKING ABILITY IN JUNIOR HIGH SCHOOL

This study aimed to determine whether the application of TBLT in learning English can improve students' speaking skills. This research was conducted in one of the Junior High Schools in Magetan with the participation of 16 students in second grade and an English teacher. This study used pre-experimental research with one group pre-test- post-test design. The researcher used observations and interviews with the teacher to collect data and conducted tests (pre-test and post-test) on students as the research instruments. SPSS statistic was used to analyze the data, in particular using the paired sample test. The average value of the post-test was (85.25), which was higher than the mean score of the pre-test (60.25). The t-test score is -25,000 and the degree of freedom (df) is 15. Thus, it could be concluded that the implementation of TBLT may help students enhance their speaking skills.
Comment moderation strategies in Canadian news media and their principles

This paper offers a critical examination of Canadian news guidelines and policieson user-generated content (UGC) posted on news-related comment sections andsocial media.The outline of how news-related UGC is moderated within Canadian news is achievedby looking at the online comment policies of major Canadian news organizations like .the CBC, CityNews (Halifax), CTV News, Glacier Media, Global News, The Globeand Mail, Narcity Media, Postmedia, QUB (Québecor), and Torstar.The policies highlight how Canadian news organizations have practical strategiesto manage news-related UGC but also call upon positive and negative socialprinciples, to flag ill practices, foster democracy, and fight against online hate speechand libel. The analysis shows how guidelines reflect many of the key principles highlightedin the literature review but make no reference to economic principles thatare emphasized as important in academic and journalist concerns with moderatingnews-related UGC.
Factors Influencing Teachers' Implementation of Online Teaching and Learning Mode during Covid-19

Brunei first detected the covid-19 pandemic on March 9, 2020, which closed physical schooling immediately, and schools later continued online in response to the pandemic.This study investigates teachers' feedback on the new school norm of online teaching and learning mode.The objective of this study is to test the conceptual research model derived from the Unified Theory of Acceptance and Use of Technology (UTAUT) for determining how the facilitating factors influence teachers' intentions and their beliefs about their behavioural implementation of online teaching and learning mode.The participant of the study consists of 6,078 teachers in public schools in Brunei Darussalam.An electronic survey questionnaire was developed and disseminated to the teachers.The analyses of the quantitative data involved factor analysis and structural equation modelling (SEM).The significant findings are (i) teachers' Behavioural Intention directly correlates to motivation factor (Satisfaction), facilitating factor (Effort Expectancy), and the belief factors (Risk and Trust); (ii) teachers' Behavioural Intention are indirectly correlated to motivational factor (Perceived Enjoyment) and facilitating factor (Self-efficacy); (iii) teachers' Implementation of online teaching and learning mode relates directly to Behavioural Intention and belief factor (Trust).The study concludes that teachers can be encouraged and supported to implement online teaching and learning mode by addressing the factors influencing their behavioural intentions.
Covid-19 Effect on the Assessment of the Use of Smartphone Apps in STEM Learning

The purpose of the study is to analyze the impact of the Covid-changed learning environment on the assessment of mobile applications for learning STEM disciplines.The study presents the results of the surveys of teachers (n = 38, age 32-52) and students (n = 188, age 19-21) of Sechenov First Moscow State Medical University and Kazan Federal University.The participants evaluated the effectiveness of mobile applications designed to study STEM disciplines.A thorough analysis of three apps dedicated to STEM learning and nine auxiliary niche apps allowed revealing their pros and cons, in particular, expanded student opportunities, the ability to study while staying in the Russian Federation, the availability of a Russian-language version, and the ability to make an appointment (for teachers or students).Due to the increased use of mobile devices by young people, the time frame for completing lessons and motivation to accomplish assignments (tasks related to engineering, solving problems in a notebook, taking notes, completing assignments on the device and in the notebook) were also defined as important.The analysis of mobile apps in the context of distance and blended learning unveiled that, in addition to the well-known criteria, the assessment of such learning means should also make allowance for the possibility to complete tasks outside the digital space, screen time reduction capacity, and accessibility for users.Learning based on a mobile application must be planned to encourage student thinking, the desire to obtain good results, and involvement in project activity.