Reviewerly/exHarmony huggingface dataset:
IFLNET: Image Forgery Localization Using Dual Attention Network

The fake images and visuals can easily spread among social media users and they largely impact decisionmaking in society.Image forgery has become increasingly common as more non-professionals have access to image manipulation tools.These fake images are so sneaky that an ordinary person cannot guess them.Through social media, such photos are utilized to promote erroneous information in society.Image forgery detection is about segmenting the forged part from the images, primarily a region of interest.This paper suggests a unique method that depends on a dual attention network to detect forged segments.This network contains self-attention modules that contribute to extracting and matching features in the channel and spatial domains.These features help locate and identify the forged portions of digital images at various scales and channels.This experimental study uses typical datasets such as CASIA V1.0, CASIA V2.0, and Columbia.Proposed IFLNet technique outperforms other advanced techniques with a precision of 96 %, recall rate of 95 %, accuracy rate of 98 %, F1-score of 96 % and IoU score of 92 % for Columbia dataset and correspondingly other two datasets also.
Word and Multiword Processing

When we encounter linguistic input, both spoken and written, we need to identify words and multiword sequences (e.g., “spill the beans” meaning “to reveal a secret”), ascertain their meaning, and integrate them into our unfolding understanding of a sentence and/or larger discourse. Recognizing single words and multiword sequences is a fundamental element of language comprehension in both a first and second language and involves a complex set of processes that are influenced by a range of variables. Frequency, or how often language users encounter a word or sequence, plays a central role in their entrenchment in memory and processing. Computational models have been developed to better understand and explain word recognition and the role of frequency in it. This chapter will explore effects of frequency on single and multiword processing as well as how frequency is accounted for in Multilink, a bilingual interactive activation model.
Analysis of four non-drug measures in the treatment of arterial hypertension: a literature review

Systemic arterial hypertension (SAH) can be conceptualized as a chronic degenerative disease of multifactorial nature, in most cases asymptomatic, that fundamentally compromises the balance of the vasodilator and vasoconstrictor systems that maintain the vasomotor tone, leading to a reduction in the lumen of the vessels and damage to the organs they irrigate. In practice, SAH is characterized by an increase in blood pressure levels above what is recommended for a given age group. The objective was to analyze four non-drug measures in the treatment of systemic arterial hypertension which are: dietary control, alcohol control, tobacco control, and physical exercise. This study was based on a bibliographic review in databases and considered systematic review, case report, clinical trial, and included publications in Portuguese and English. SAH is a highly prevalent disease with a high negative social impact. Early identification of hypertensive patients and effective treatment are of great clinical importance. Identification of possibly reversible causes is a fundamental part in the clinical management of these patients. It was concluded that nutritional therapy and lifestyle changes associated with pharmacological therapy can promote benefits in the reduction of systemic blood pressure. Thus, it is of utmost importance to stimulate and make the population aware of the need for adherence to pharmacological antihypertensive treatment along with non-drug therapy, thus contributing to the reduction of morbidity, mortality and health care costs.
An Electronic Program based on The Quality Matters ™ Rubric Standards (QM) and its Impact on Reading Achievement and Executive Functions among Students with Reading Disabilities and Attention Deficit and Hyperactivity Disorder

The present study aims at investigating the impact of an electronic reading program based on Quality Matters (QM) on reading achievement and executive functions among students with reading disabilities and attention Deficit and hyperactivity disorder. &#x0D; The population of the study included all third grade students reading disabilities and attention Deficit and hyperactivity disorder in Asir region. The sample of the study contained seven students who were purposively selected to participate in the experiment. &#x0D; The researcher has prepared a test to measure the students’ reading achievement and executive functions. The test was developed through reviewing the relevant literature of the topic being studied and consulting the experts in special education, psychology and educational measurement. The validity and reliability of the test were ensured as well. &#x0D; In addition, the researcher developed an electronic reading program for developing reading achievement and executive functions of students with reading disabilities and attention Deficit and hyperactivity disorder. The program was developed to fulfill the needs of the students at this particular age. The present study used the quasi-experimental design to examine the impact of the electronic program on reading achievement and executive functions. For the statistical purpose, Wilcoxon Test were utilized to answer the Study questions. The results of this study revealed that there was a statistically significant difference between the Pre and post measurement in reading achievement and executive functions; this difference was in favor of post measurement.
Introduction

International Journal of Neural SystemsVol. 32, No. 12, 2202002 (2022) No AccessIntroductionLazaros IliadisLazaros IliadisLab of Mathematics and Informatics (ISCE), School of Engineering, Head of the Department of Civil Engineering, Sector of Mathematics, Programming and G.C., Democritus University of Thrace, University Campus, Kimmeria, Xanthi, PC 67100, Greecehttps://doi.org/10.1142/S0129065722020026Cited by:0 Next AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out our titles in neural networks today! FiguresReferencesRelatedDetails Recommended Vol. 32, No. 12 Metrics History Published: 26 October 2022 PDF download
Internal Communication Campaign and its effects on Trust in organization and Commitment to change: focusing on bank employees

Recently, many organizations have difficulty in attracting and maintaining new employees with the traditional human resource management. To motivate their employees, firms are required to set up more systemized and active internal communication. This study aims to examine how the willingness and interest, continuous investment on internal communication of top management have an impact on internal communication campaign. Also, this empirical study examines the effects of internal communication campaign on trust in organization and commitment to change. In doing this, the authors used a quantitative survey research methodology. Focusing on banking sector employees, the study used 485 collected data. Using LISEL, the results of the empirical analysis are as followed. First, the antecedents (the willingness and interest, continuous investment of top management) have a statistically positive impact on internal communication campaign. In particular, the effect of the continuous investment of top management is more significant. Second, internal communication campaign enhances both trust in organization and employees' commitment to change. Finally, trust in organization also positively affects on employees' commitment to change. In conclusion, the findings and implications are discussed.
An interactive game for rehabilitation based on real-time hand gesture recognition

Currently, cardiovascular and cerebrovascular diseases have become serious global health problems related to their high incidence and fatality rate. Some patients with cardiovascular cerebro-cardiovascular diseases even may face motor or cognitive dysfunction after surgery. In recent years, human–computer interactive systems with artificial intelligence have become an important part of human well-being because they enable novel forms of rehabilitation therapies. We propose an interactive game utilizing real-time skeleton-based hand gesture recognition, which aims to assist rehabilitation exercises by improving the hand-eye coordination of the patients during a game-like experience. For this purpose, we propose a lightweight residual graph convolutional architecture for hand gesture recognition. Furthermore, we designed the whole system using the proposed gesture recognition module and some third-party modules. Finally, some participants were invited to test our system and most of them showed an improvement in their passing rate of the game during the test process.
Empirical Evaluation of Local Model for Just in Time Defect Prediction

Just in time defect prediction abbreviated as JITDP refers to a software that helps to detect instantaneously whether any change made to that software leads to defect or not and that too in no time. It helps us to develop a defect-free software helping both developers and testers to reduce their efforts. We have built a local model for predicting defects just in time for improved performance. A local model makes clusters of the available training data based on their homogeneity, and then each cluster is trained individually, while global models refer to all those models which use all the available training data as a whole and are used to predict a test instance. In this study, we are focusing on (i) the implementation and (ii) execution performance of local models followed by (iii) the comparison of local and global models in three scenarios such as cross-validation, cross-project validation and time-based chronological scenario. Some of the observations made in this study from the comparison are as follows: Local model performs significantly low in classification. However, local models perform better in effort aware prediction in both cross-validation and cross-project validation scenarios. Also, local models have optimum performance when the value of k is set to 2 in k-medoids. Thus, for effort aware prediction in any software, using local models can be a good choice.
Cosine-Based Embedding for Completing Lightweight Schematic Knowledge in DL-Litecore

Schematic knowledge, an important component of knowledge graphs (KGs), defines a rich set of logical axioms based on concepts and relations to support knowledge integration, reasoning, and heterogeneity elimination over KGs. Although several KGs consist of lots of factual knowledge, their schematic knowledge (e.g., subclassOf axioms, disjointWith axioms) is far from complete. Currently, existing KG embedding methods for completing schematic knowledge still suffer from two limitations. Firstly, existing embedding methods designed to encode factual knowledge pay little attention to the completion of schematic knowledge (e.g., axioms). Secondly, several methods try to preserve logical properties of relations for completing schematic knowledge, but they cannot simultaneously preserve the transitivity (e.g., subclassOf) and symmetry (e.g., disjointWith) of axioms well. To solve these issues, we propose a cosine-based embedding method named CosE tailored for completing lightweight schematic knowledge in DL-Litecore. Precisely, the concepts in axioms will be encoded into two semantic spaces defined in CosE. One is called angle-based semantic space, which is employed to preserve the transitivity or symmetry of relations in axioms. The other one is defined as translation-based semantic space that is used to measure the confidence of each axiom. We design two types of score functions for these two semantic spaces, so as to sufficiently learn the vector representations of concepts. Moreover, we propose a novel negative sampling strategy based on the mutual exclusion between subclassOf and disjointWith. In this way, concepts can obtain better vector representations for schematic knowledge completion. We implement our method and verify it on four standard datasets generated by real ontologies. Experiments show that CosE can obtain better results than existing models and keep the logical properties of relations for transitivity and symmetry simultaneously.
REDEFINING LABORATORY REFERENCE RANGES FOR FEMALE REPRODUCTIVE AND THYROID HORMONES

The diagnoses and clinical management of many reproductive health conditions rely on categorically interpreting hormone measurements as either within or outside a ‘normal’ reference range. Historically these ranges represent the 95% confidence intervals of a small population without regard to the distributional asymmetry expected by compositional data. We establish a more robust statistical framework that considers continuous quantiles, derived from a large dataset, in order to better define normal reference ranges of reproductive and thyroid hormone levels.
Acquisition and Tracking of BOC Signals

Build and operate multi-GNSS and multi-frequency receivers with state-of-the-art techniques using this up-to-date, thorough and easy-to-follow text. Covering both theory and practice and complemented by MATLAB© code and digital samples with which to test it, this package is a powerful learning tool for students, engineers and researchers everywhere. Suggestions of hardware equipment allow you to get to work straight away and to create your own samples. Concisely but clearly explaining all the fundamental concepts in one place, this is also a perfect resource for readers seeking an introduction to the topic.
A Dual-Frequency Software Receiver

Build and operate multi-GNSS and multi-frequency receivers with state-of-the-art techniques using this up-to-date, thorough and easy-to-follow text. Covering both theory and practice and complemented by MATLAB© code and digital samples with which to test it, this package is a powerful learning tool for students, engineers and researchers everywhere. Suggestions of hardware equipment allow you to get to work straight away and to create your own samples. Concisely but clearly explaining all the fundamental concepts in one place, this is also a perfect resource for readers seeking an introduction to the topic.
Cohesive Devices on the Abstracts of Select Research Articles Presented in the International Academic Forum (AIFOR): A Discourse Analysis

This linguistic research analysis investigates the cohesive devices found in the Abstract of select Asian Conference of Education 2019 research articles. This study also endeavors to disclose the contributions of the cohesive devices in the comprehensibility of the text. This corpus-based study utilizes qualitative descriptive research that specifically employs the evocative discourse analysis. It considers the use of Halliday and Hassan’s taxonomy on Cohesive devices as a basis for investigation. A total of twenty-eight (28) research abstracts are chosen from abstracts compiled in the program and abstract book of the Asian Conference on Education (ACE) presented during the International Academic Forum (IAFOR) held in Toshi Hotel, Tokyo, Japan last October 31 to November 3, 2019, as the main source of data. The data are analyzed using Halliday and Hasan’s taxonomy of cohesion which is divided into two aspects, grammatical and lexical cohesion. The result of the study reveals that researchers utilize two grammatical cohesions namely: reference and conjunctions in their research abstracts. The conjunctive features of the grammatical device found are additive, causal, adversative, and temporal. Moreover, the referential grammatical devices found are demonstrative, personal, article, and comparative. There is no usage of substitution and elliptical cohesion or ellipsis found. In terms of lexical cohesion, Reiteration devices such as repetition, synonymy, and antonymy are utilized by the researchers in their abstracts. There is no evidence of meronomy found in the analysis. Collocation devices are widely used in the abstract. In the analysis, the presence of the cohesive devices in the abstracts functions to keep track of the participants, organized in logical order the concepts in the abstract, and help in avoiding repetition and text redundancy which significantly contributes to the text comprehensibility of the research abstracts. Thus, the researcher designs and presents an English workbook for classroom use in which the drills provided focus on the correct usage of cohesive devices in any written discourse.
Blockchain Mechanism

A blockchain is essentially a distributed database of records or public ledger of all transactions or digital events that have been executed and shared among participating parties. Each transaction in the public ledger is verified by consensus of a majority of the participants in the system. And, once entered, information can never be erased. The blockchain contains a certain and verifiable record of every single transaction ever made. Bitcoin, the decentralized peertopeer digital currency, is the most popular example that uses blockchain technology. The digital currency bitcoin itself is highly controversial but the underlying blockchain technology has worked flawlessly and found wide range of applications in both financial and nonfinancial world.
THE RESULTS OF REFORMS IN THE FIELD OF TRAINING HIGHLY QUALIFIED PERSONNEL IN THE LATE XX- EARLY XXI CENTURY: POSTGRADUATE AND DOCTORAL STUDIES

The article is a continuation of the article previously published in the journal «The results of reforms in the field of training highly qualified personnel in the late XX - early XXI century: scientific personnel». The authors made an attempt to analyze the reproduction of scientific and scientific-pedagogical personnel by scientific and research, educational organizations of higher education and organizations of additional professional education in the Russian Federation. In society, there has always been an interest in higher education, especially in the system of training highly qualified personnel, postgraduate education and doctoral studies. The current institutional crisis of the education system has affected both undergraduate and postgraduate education. The presented results evaluate the process of training highly qualified educational personnel, the effectiveness of the institute of postgraduate and doctoral studies in Russia.
Using Task-based Supplementary Material for the Enhancement of Reading Proficiency of Grade VI Pupils

This study was conducted to focus on determining if using task-based supplementary material would bring out an enhancement to the level of reading proficiency of the pupils. This study utilized an experimental research design, and the respondents of this study were 30 Grade VI, pupils. The respondents of the study were selected using purposive sampling because the researcher had prior knowledge about the characteristics of the respondents participating in this study. The researcher used a researcher-made task-based supplementary material, and pre-test and post-test instruments in gathering the data of the respondents’ reading proficiency levels in terms of noting details, sequencing of events, cause and effect, and predicting outcomes. Regarding respondents’ post-assessed proficiency level in noting details, they were at approaching proficiency to an advanced level; approaching to advanced level of proficiency in the sequencing of events; approaching proficiency to an advanced level in cause and effect, and approaching to advanced level in predicting outcomes. These results revealed that there is a significant difference between their test scores before and after their exposure to the task-based supplementary material. The use of researcher-made task-based supplementary material has significantly influenced the reading proficiency levels of the respondents in terms of noting details, sequencing of events, cause and effect, and predicting outcomes. Keywords: Task-Based Supplementary Material, Reading Proficiency, Pupils’ Reading Performance
JoVE Video Dataset

Design Application Fault Detection Method Based On Adaptive Filters - Video. JoVE publishes peer-reviewed scientific video protocols to accelerate biological, medical, chemical and physical research. Watch our scientific video articles. JoVE is the world-leading producer and provider of science videos with the mission to improve scientific research, scientific journals, and education. Millions of scientists, educators and students at thousands of universities, colleges, hospitals and biopharmaceutical companies worldwide use JoVE for their research, teaching and learning.
Abnormal behavior capture of video dynamic target based on 3D convolutional neural network

The use of computers to understand video content can accurately and quickly label various videos. Behavior recognition technology can help users filter the video by screening the content. However, this calculation mode, which is only sensitive to the features in a pixel neighborhood, cannot effectively extract cross-frame long-range video features. In addition, the common long-range dependency capture methods are based on pixel pairs, which contain less semantic information and cannot accurately model dependencies. Based on this, this paper generates semantic units with rich semantic information in the form of neighborhood pixel aggregation and proposes a multi-semantic long-range dependency capture algorithm to solve this problem, which makes the established dependency relationship more accurate. At the same time, this paper proposes an early dependency transfer technology to speed up the reasoning speed of the multi-semantic long-range dependency capture algorithm. By embedding the proposed algorithm into the original convolutional neural network, and conducting sufficient performance tests and evaluations on different data sets, it is shown that the proposed algorithm outperforms other current algorithms in terms of recognition accuracy and achieves the optimal recognition effect, which can effectively enhance the long-range dependency capture ability and temporal modeling ability of the convolutional network, and improve the quality of video feature representation.
Persepsi Mahasiswa Biologi Terhadap Penggunaan Aplikasi Daring (Video Conference) Sebagai Sarana Perkuliahan Daring Pada Masa Pendemi Covid-19

The purpose of this study was to find out how students perceive the use of online video conference applications as a means of online lectures during the Covid-19 pandemic at Raden Intan State Islamic University Lampung majoring in Biology Education and to find out what obstacles’ students experience. This study uses a quantitative descriptive research method, in which the researcher describes the data based on the results of the questionnaire scores and is supported by the respondent's interview answers. It was shown that students' perceptions of the results of the total percentage on each indicator, namely the acceptance indicator consisted of 55%, the understanding indicator was 61.23%, while the evaluation indicator was 68.2% and had an average value of 61.47% which included in the good category. The perception of students is said to be good and can support the online learning process. Meanwhile, the obstacles felt by students when using video conferencing applications were still technical which are connections, networks, and time constraints.