Reviewerly/exHarmony huggingface dataset:
Retraction

Retraction: Wang L, Huang W, Zhang S, Liu Z. Complex image denoising framework with CNN‐wavelet under concurrency scenarios for informatics systems. J Concurrency Computat: Pract Exper . 2021; 33–12. The above article, published online on 13 Nov 2018 Wiley Online Library ( https://onlinelibrary.wiley.com/doi/full/10.1002/cpe.5059 ) has been retracted by agreement between the journal Editor, Professor David W. Walker, School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK and Wiley Periodicals, LLC. The retraction has been agreed following an investigation based on allegations raised by a third party. Several inconsistencies were found, including general logical flaws and irrelevant citations, making the motivation and conclusions of the article untrustworthy. The editorial office did not receive a response from the authors, and so requested underlying experimental data was not available for evaluation. Accordingly, the editors consider the conclusions of this manuscript invalid.
Aggregating Procedure for Fuzzy Cognitive Maps

In the field of Knowledge Engineering and Representation, a typical struggle encompasses transferring the Subject Matter’s expertise into computational descriptions that could be used to create digital-twin representations of a given real-world scenario. Fuzzy Cognitive Maps (FCMs) have recently gained relevant attention among multiple techniques developed with this aim. However, one issue remains, when numerous of these representations need to be combined into a unique aggregated structure, it is essential to weigh factors (such as quality) into the final form to ensure its veracity, making the process not too straightforward. This paper proposes an aggregation procedure to combine FCMs into one that represents best its contributors. The technique was utilized for solving a real-life problem, and several configurations were explored. The results are compiled and reported in this paper.
Retraction

Retraction: Li J, Hu B. Sparse data inspired depth of urbanization in Zhejiang based on geographic information system and visual system. J Concurrency Computat Pract Exper . 2021; 33(12). The above article, published online on 27 Feb 2020 Wiley Online Library ( https://onlinelibrary.wiley.com/doi/full/10.1002/cpe.5704 ) has been retracted by agreement between the journal Editor, Professor David W. Walker, School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK and Wiley Periodicals, LLC. The retraction has been agreed following an investigation based on allegations raised by a third party. Several inconsistencies were found, including general logical flaws and irrelevant citations, making the motivation and conclusion of the article untrustworthy. The editorial office did not receive a response from the authors, and so requested underlying experimental data was not available for evaluation. Accordingly, the editors consider the conclusions of this manuscript invalid.
A Novel Approach to Provide Security for Autonomous Vehicles

Abstract: Autonomous Vehicles (AVs) have been developing as the time passes in the events and it reduces the workload of mankind and reducing the errors done in the traffic(accidents). AVs are not bounded to roadways but also take part in other possible transportation modes. The issues ofthe extant process have less privacy due to involvement of sensor components and inappropriate of data transfer which will be subjected to more cyber-attacks. To prevent these issues, a methodology is proposed and the cryptography algorithms is implemented for transferring the data for drones, UAV’s. Here cloud acts as negotiator between user and drones and UAV’s, user shares the files from sender to drones(UAV’s) with enhanced security. The AES algorithm is incorporated to inscribe the data from plain text into unidentified encoded form of text. The concealed text can be deciphered by entering the hash key.
Highlights on Program Governance through AI and Blockchain

Abstract: &#x0D; This research studies the effect of two of the main disruptive technologies – mainly AI and Blockchain – on program governance and management. He aim is to highlight the positive potentials for digital transformation in governance approaches and practices including roles and responsibilities. The research approach utilises quantitative and qualitative methods. The data collection and analysis methods used in the study are interviews, surveys, and document reviews. The research findings highlight the potential of conducting program governance without a centralised entity, could help avoid conflicts between organisations and maintain a single data source, centralisation of financial resources will be necessary in the future, improve governance efficiency and increase transparency.
Educational Management in Times of Pandemic: A Panoramic View in Latin America

As a result of a systematic review obtained, eight levels of constructs: educational inequalities, online education, Covid's impact, educational management, and Covid's effects, innovator leadership, resilience, home as an adaptation source, and, finally, sustainable actions. The results showed that the pandemic put in evidence a big social gap between educational communities in Latin America where access to the obtention of technological resources to teach-learn process and handle digital competencies in teachers. In addition, it showed many inequalities in people to energy and connectivity access as minimal conditions to do educational activities, meanwhile health restrictions during Covid-19.
Self-Supervised Speech Representation Learning for Keyword-Spotting With Light-Weight Transformers

Self-supervised speech representation learning (S3RL) is revolutionizing the way we leverage the ever-growing availability of data. While S3RL related studies typically use large models, we employ light-weight networks to comply with tight memory of compute-constrained devices. We demonstrate the effectiveness of S3RL on a keyword-spotting (KS) problem by using transformers with 330k parameters and propose a mechanism to enhance utterance-wise distinction, which proves crucial for improving performance on classification tasks. On the Google speech commands v2 dataset, the proposed method applied to the Auto-Regressive Predictive Coding S3RL led to a 1.2% accuracy improvement compared to training from scratch. On an in-house KS dataset with four different keywords, it provided 6% to 23.7% relative false accept improvement at fixed false reject rate. We argue this demonstrates the applicability of S3RL approaches to light-weight models for KS and confirms S3RL is a powerful alternative to traditional supervised learning for resource-constrained applications.
UFO2: A Unified Pre-Training Framework for Online and Offline Speech Recognition

In this paper, we propose a Unified pre-training Framework for Online and Offline (UFO2) Automatic Speech Recognition (ASR), which 1) simplifies the two separate training workflows for online and offline modes into one process, and 2) improves the Word Error Rate (WER) performance with limited utterance annotating. Specifically, we extend the conventional offline-mode Self-Supervised Learning (SSL)-based ASR approach to a unified manner, where the model training is conditioned on both the full-context and dynamic- chunked inputs. To enhance the pre-trained representation model, stop-gradient operation is applied to decouple the online-mode objectives to the quantizer. Moreover, in both the pre-training and the downstream fine-tuning stages, joint losses are proposed to train the unified model with full-weight sharing for the two modes. Experimental results on the LibriSpeech dataset show that UFO2 outperforms the SSL-based baseline method by 29.7% and 18.2% relative WER reduction in offline and online modes, respectively.
Sequential Datum–Wise Joint Feature Selection and Classification in the Presence of External Classifier

We introduce a supervised machine learning framework for sequential datum–wise joint feature selection and classification. Our proposed approach sequentially acquires features one at a time during testing until it decides that acquiring more features will not improve label assignment. At that point, and in contrast to prior art, it assigns a label to the example under consideration by selecting between a simple internal and a more powerful external classifier. Easy–to–classify examples are handled by the internal classifier, which assigns labels based on the lowest expected misclassification cost. On the other hand, difficult–to–classify examples are forwarded to the external classifier to be assigned a label based on the acquired features. We demonstrate the performance of the proposed approach compared to existing methods using six publicly available datasets. Experiments indicate that the proposed approach improves accuracy up to 50% with respect to existing sequential methods, while acquiring up to 85% less number of features on average.
Robust Autoencoders for Collective Corruption Removal

Robust PCA is a standard tool for learning a linear subspace in the presence of sparse corruption or rare outliers. What about robustly learning manifolds that are more realistic models for natural data, such as images? There have been several recent attempts to generalize robust PCA to manifold settings. In this paper, we propose ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> - and scaling-invariant ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> /ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> -robust autoencoders based on a surprisingly compact formulation built on the intuition that deep autoencoders perform manifold learning. We demonstrate on several standard image datasets that the proposed formulation significantly outperforms all previous methods in collectively removing sparse corruption, without clean images for training. Moreover, we also show that the learned manifold structures can be generalized to unseen data samples effectively.
Discovering Reusable Functional Features in Legacy Object-Oriented Systems

Typical object-oriented (OO) systems implement several functional features that are interwoven into class hierarchies. In the absence of aspect-oriented techniques to develop and compose these features, developers resort to object-oriented design and programming idioms to separate features as well as possible. Given a legacy OO system, discovering existing functional features helps understand the design of the system and extract these features to ease their maintenance and reuse. We want to discover candidate functional features in OO systems. We first define functional features and then discuss the footprints that such features are likely to leave in an OO system. We identify three such footprints: (1) multiple inheritance, (2) delegation, and (3) ad-hoc. We develop a set of algorithms for identifying such footprints in OO code and implemented them for the Java language using Eclipse JDT. In this article, we present the algorithms, and the results of applying the corresponding tools on five open-source systems: FreeMind, JavaWebMail, JHotDraw, JReversePro, and Lucene. Our experimental results show that: (1) the different algorithms can identify interesting and useful candidate functional features in OO systems, (2) they can identify opportunities for refactoring, and (3) they are complementary and could help developers.
Towards Deviation-Robust Agent Navigation Via Perturbation-Aware Contrastive Learning

Vision-and-language navigation (VLN) asks an agent to follow a given language instruction to navigate through a real 3D environment. Despite significant advances, conventional VLN agents are trained typically under disturbance-free environments and may easily fail in real-world navigation scenarios, since they are unaware of how to deal with various possible disturbances, such as sudden obstacles or human interruptions, which widely exist and may usually cause an unexpected route deviation. In this paper, we present a model-agnostic training paradigm, called Progressive Perturbation-aware Contrastive Learning (PROPER) to enhance the generalization ability of existing VLN agents to the real world, by requiring them to learn towards deviation-robust navigation. Specifically, a simple yet effective path perturbation scheme is introduced to implement the route deviation, with which the agent is required to still navigate successfully following the original instruction. Since directly enforcing the agent to learn perturbed trajectories may lead to insufficient and inefficient training, a progressively perturbed trajectory augmentation strategy is designed, where the agent can self-adaptively learn to navigate under perturbation with the improvement of its navigation performance for each specific trajectory. For encouraging the agent to well capture the difference brought by perturbation and adapt to both perturbation-free and perturbation-based environments, a perturbation-aware contrastive learning mechanism is further developed by contrasting perturbation-free trajectory encodings and perturbation-based counterparts. Extensive experiments on the standard Room-to-Room (R2R) benchmark show that PROPER can benefit multiple state-of-the-art VLN baselines in perturbation-free scenarios. We further collect the perturbed path data to construct an introspection subset based on the R2R, called Path-Perturbed R2R (PP-R2R). The results on PP-R2R show unsatisfying robustness of popular VLN agents and the capability of PROPER in improving the navigation robustness under deviation.
DIFICULDADES E BARREIRAS DE PAIS E PACIENTES DE FIBROSE CÍSTICA À ADESÃO DE TELEFISIOTERAPIA DURANTE A PANDEMIA DE COVID-19

CASE REPORTSa middle-aged man with acute intestinal obstruction due to an ileo-ileal intussusception of inverted Meckel's diverticulum with a lipoma that was managed by laparoscopy.Worthy of note is the very scarce number of case reports on this exceeding uncommon association.
Directly Observed Treatment for Iron Tablet Supplements Consumption Among Female Senior High School Students

Anemia is one of the reproductive health problems in adolescents. The incidence of anemia is still high in Indonesia. Supplementation of Iron tablets per week is one of the policies to decrease the anemia incident. This study aims to determine the effectiveness of the implementation of Directly Observed Treatment (DOT) for Iron Tablet Supplements. Consumption was observed in high school students for 12 weeks. This study was a quantitative study with a quasi-experimental design, with the experiment group being peers as DOT and the control group being guidance counseling teachers. This research was conducted from July to October 2019 in Sleman and Bantul. Samples used for both groups were 70 respondents. Data analysis used univariate and bivariate. The results of this study showed that the incidence of anemia for both groups was still high at 51.4% before giving the iron tablets to 34.3% after the tablets were given. The implementation of DOT in the consumption of iron tablets in the teacher group showed non-adherence to drinking iron tablet only 2.9% and by peers up to 31.4%. Observed by the teacher showed an effect on the difference in Hemoglobin levels before and after treatment with p=0.037 and peer as observed with p=0.247.
Ethnocultural concept of conducting and choral training of future teachers of musical art at the faculties of arts

arts.To achieve the goal, general scientific
The Effect of Group Guidance Using Modeling Techniques to Reduce Online Game Addiction in SMK Negeri 1 Sukamara

The background of this research is that students are often sleepy during class hours, students often fall asleep during distance learning so they don't participate, students' school grades decrease, parents or guardians of students say that while at home their children play online games more often, students spend time playing games. from after the evening prayer until just before dawn, students forget to eat and worship because they are preoccupied with playing online games, students also experience imsonia (sleep disturbance), the purpose of this study is to determine the effect of group guidance with modeling techniques to reduce online game addiction for students of SMK Negeri 1 Sukamara . The research method used is true experimental with pretest-posttest control group design. The population in this study were all students of class XI ATPH SMK Negeri 1 Sukamara, totaling 65 students consisting of two classes, namely class XI ATPH 1 and XI ATPH 2. The try out will be held in class XI ATPH 1 with a total of 32 students. The sampling technique used is simple random sampling technique. The analysis used to test the validity is the product moment and reliability using the alpha formula. The final conclusion is based on the results of data analysis that the pretest results in the experimental group with an average of 45.8 and the control group with an average of 50.1, while the posttest results in the experimental group with an average of 63.3 and the control group with an average average 59.8. In addition, from the results of the t-test analysis, the difference between the posttest between the experimental group and the control group for online game addiction using the t-test formula was obtained at 10.3 ttable at 2.021 at a significant level of 5%, so tcount is greater than the mean ttable, meaning Ho is rejected and Ha is accepted, so that it can be concluded by going through the hypothesis that says "there is an effect of group guidance with modeling techniques to reduce online game addiction for students at SMK Negeri 1 Sukamara. Suggestions that can be conveyed by researchers are that supervising teachers at schools provide advanced services regarding online game addiction.
Impoliteness in Jeremy Clarkson’s humor in the tv series Top Gear

This research was conducted to observe impoliteness strategies employed by Jeremy Clarkson in his humor in the TV Series Top Gear with the intention to unveil which strategy he utilized most and elucidate the reason upon the most and least occurring impoliteness strategies. The present research utilizes Culpeper’s (1996) model of impoliteness super strategies as the main framework to identify, analyze, and elaborate the research data. Other theories related to impoliteness such as face-threatening act and politeness was also utilized to assist the analysis process. The result of the research revealed that negative impoliteness strategy was the most commonly used strategy through which Jeremy Clarkson delivers his humor. Contrary to the withhold politeness strategy which accounted for the least occurring strategy. The explanation upon the result to be the way they are could be referred back to Jeremy Clarkson’s main intentions of uttering impolite remarks—in an effort to amuse the audience and keep the show entertaining.
Upaya Meningkatkan Keterampilan Menulis Procedure Text Mata Pelajaran Bahasa Inggris melalui Model Mind Mapping Siswa Kelas IX.B SMP Negeri 6 Mataram

The objectives of this classroom action research were: (1) To find out the application of the mind mapping model to improve the ability to write Procedure Text for English subjects for Class IX.B SMP Negeri 6 Mataram Academic Year 20222/2023; (2) To find out the increase in the ability to write Procedure Text for English subjects through the application of the Mind Mapping model for Class IX.B students of SMP Negeri 6 Mataram in the Academic Year 2022/2023. This study used three rounds of action research. Each round consists of four stages: design, activity and observation, reflection, and revision. The target of this research was students of Class IX.B SMP Negeri 6 Mataram with a total of 38 students consisting of 16 boys and 22 girls. The data obtained is in the form of formative test results, observation sheets of teaching and learning activities. The results of the study proved that there was an increase in the English subject's procedure text writing skills as follows: The learning achievements of students in English subjects were as follows: the average value before the action was 85.74, in the first cycle 85.82 was obtained and the average value was average in cycle II obtained 88.47. The same thing happened to the increase in the number of students who reached the minimum completion limit (&gt; 80), namely before the action 26 students (68.42%), who completed in cycle I were 31 students (81.58%) and in cycle II increased to 38 students (100%).
Investigation and Adjudication of Cybercrimes in India

Now a days the basic need of humans are food, water, shelter and internet.The internet in our daily life has become one of the member of our family.It occupies our spaces in much larger.Although internet is a medium of learning, entertainment, technology, knowledge, art, science, etc., it has a both advantage and disadvantage over it.The cyber space which is a digital space in which all the technology based ideas were created, designed and which leads to male use of people.The people in every country were relied on the internet which affects the minds of people and it causes the offences in cyberspace.The cybercrimes, offences, attacks which were considered as an inappropriate cause to the cyber world.To avoid these issues the Information Technology Act,2000 was created and in that the process for cybercrime rules, regulation, procedures, investigation, adjudication, penalization, compensation, etc., were stated.
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A Qualitative Study on the Experience of Connecting between the 2019 Revised Nuri Curriculum and the Individualized Education Plan of Early Childhood Special Education Teachers at Special Classes in Public Kindergarten

The purpose of this study is to find out the experience and meaning of the connection between the 2019 revised Nuri curriculum and the Individualized Education Plan(IEP) of early childhood special education teachers at special classes in public kindergarten. To this end, 7 early childhood special education teachers at special classes in public kindergartens who have qualifications as teachers in special schools (kindergarten) were sampled for the purpose, and data were collected through in-depth interviews. The collected data were categorized according to the continuous comparison method, and 2 upper categories, 8 sub-categories, and 35 main contents were derived. The results of the study are as follows. First, the participants recognized the experience of connecting the 2019 revised Nuri curriculum with the IEP as ‘education made by early children and teachers together’. The sub-categories were ‘unified education met by the early childhood special education teacher’, ‘barrier met by the early childhood special education teacher’, and ‘required support in an inclusive environment’. Second, the participants recognized the experience of connecting the 2019 revised Nuri curriculum with the IEP as an ‘educational practice process for one early child’. The sub-categories were ‘connecting with the curriculum’, ‘putting the early child at the center’, ‘teachers who create fences’, ‘obstacles encountered in connecting with the curriculum’, and ‘required support for an early child's education’. The results of this study is expected to be a useful resource for early childhood special education teachers and related researchers who are striving to successfully link the 2019 revised Nuri curriculum with IEP.