Reviewerly/exHarmony huggingface dataset:
AUTHORIZATION OF WOMEN STATUS THROUGH SELF-HELP GROUPS

Women authorization and gender parity have become topics of supreme importance in recent times, because gender disparity and its socio-economic and cultural dimensions are all universal in the third world as well as in the advanced world. However, in the third world the problem of gender inequality is more intensely associated with economic disempowerment of women. Therefore, several initiatives being-taken up in third world countries for the economic empowerment of women to reduce gender inequality. Among these initiatives, the Self-Help Group (SHG) which is speeded all over the world. Therefore, the third world has become the projecting one. The concept of SHG facilitates the principle ‘by the women, of the women and for the women’.
Factors Affecting the Acceptance of E-learning by Learners in the Context of the Covid-19 Pandemic: A Hybrid Artificial Neural Network - SEM Method

This research aims to determine the factors affecting the student’s intention of E-learning adoption in the context of the Covid-19 pandemic in Vietnam. An online survey was used to assess the proposed psychological determinants of E-learning adoption. Confirmatory factor analysis and structural equation modeling were conducted on the collected data (n=310) using the SPSS 20 and the AMOS 24 statistical software. The reliability and validity of the measurement were examined via Cronbach’s alpha, EFA, CFA while the strength and direction of the hypothesized causal paths among the constructs were analyzed via SEM. Finally, the results from SEM were used as the inputs for an Artificial Neural Network (ANN) model to predict acceptance factors. The results of the study indicated that there was a significant positive relationship between the attitude toward risk (Covid-19), PU, PE and the intention of E-learning adoption. Furthermore, combining SEM and neural networks enabled the capture of linear and complex nonlinear relationships between predictors and the dependent variables.
Deep Learning of Image Representations with Convolutional Neural Networks Autoencoder for Image Retrieval with Relevance Feedback

mage retrieval with traditional relevance feedback encounters problems: (1) ability to represent handcrafted features which is limited, and (2) inefficient withhigh-dimensional data such as image data. In this paper,we propose a framework based on very deep convolutionalneural network autoencoder for image retrieval, called AIR(Autoencoders for Image Retrieval). Our proposed frameworkallows to learn feature vectors directly from the raw imageand in an unsupervised manner. In addition, our frameworkutilizes a hybrid approach of unsupervised and supervisedto improve retrieval performance. The experimental resultsshow that our method gives better results than some existingmethods on the CIFAR-100 image set, which consists of 60,000images.
THE EFFECTIVENESS OF GUESING GAME TO TEACH SPEAKING AT RELIGION PROGAM OF STIT AL URWATUL WUTSQO JOMBANG

Speaking as a process of constructing meaning, giving and receiving information is very important to own by students. But they often got problem in mastery it. This research is aimed to know whether or not guessing gams effective to teach speaking. Design of this study is quantitative, using test as collecting data, the data then analyzed using Kolmogrov Smirnov, since the data didn’t meet the normality test criteria. The result of the research is guessing game has not affected the students speaking ability of second grade of STIT al Urwatul Wutsqo Jombang. Based on the result of Z test is empirical Z is lower than theoretical Z, so the guessing games hasn’t affected the students speaking ability, that means there is no difference speaking ability between students who are taught using guessing game and practice method.
ACADEMIC LIBRARY RESEARCH DEVELOPMENT IN THE CONTEXT OF IF OF JOURNALS: BIBLIOMETRIC ANALYSIS OF ARTICLES BASED ON THE LISTA DATABASE (2000-2019)

Thesis/Objective – This paper presents the results of bibliometric analysis of the metadata of 16095 articles about academic libraries published be- tween 2000 and 2019, extracted from the Library, Information Science and Tech- nology Abstracts (LISTA) database. The aim of the authors of this paper was to find out about the growth of research on academic libraries and also about the authors’ choice of the place of publica- tion (journals with and without IF). Method – In order to achieve the set goals, the following research methods were used: bibliometric analysis, method of do- cument analysis, method of literature review, comparative method. Results – The study confirmed that the journals with IF, despite some doubt regarding the Im- pact Factor as such, enjoyed a growing interest of authors throughout the whole analysed period, while the number of articles published in journals without IF clearly decreased.
A review of cyber security risk assessment for web systems during its deployment and operation

This paper presents the state of the arts in security risk assessment of web systems. The process of assessing security risks and the process of developing and operating information systems in general, web systems in particular, are depicted step by step, showing how the risk assessment is performed during the deployment and the operation of web systems. Based on this analysis, different methods related to the manual and automatic risk assessment are reviewed, focusing on the methods using probability theory and Bayesian networks. The techniques developed for quantitative and qualitative assessment are presented and compared in terms of their objectives, scopes, and results to pick out advantages and limits. Finally, the approaches dedicated to assessing the risks of web systems are presented.
Preparing Secondary Mathematics Preservice Teachers for Rural Schools in Colombia

AbstractThis article describes a qualitative research case study that aimed to explore the complexity levels and types of mathematical thinking and national mathematics standards that a group of preservice teachers (PSTs) used to write mathematics word problems for secondary school students. The context was a teacher preparation course at a public university located in a rural region in Colombia, South America. The collected data included PSTs’ community reports, audio recordings of small-group discussions, and mathematics word problems. The data analysis process encompassed inductively created codebooks, several rounds of coding, and the inter-rater technique. The findings of the study demonstrated that most word problems written by the PSTs aligned with a few national mathematics standards and different potential levels of mathematical thinking. However, there was a disconnect between these levels of mathematical complexity and the types of funds of knowledge identified. The article ends by sharing implications for mathematics teacher education and suggestions for future research.
Evaluating Network Forensics Applying Advanced Tools

Network forensics comes under the domain of digital forensics and deals with evidences left behind on the networkiafter a cyber-attack. It is indication of the weakness that led to the crime and the possible cause. Network focused research comes up with many challenges which involves the collection, storage, content, privacy, confiscation and the admissibility. It is important and critical for any network forensic researcher or the investigator to consider adopting efficient forensic network investigation framework or the methodologies in order to improve investigation process. The main aim of this research contribution was to do a comprehensive analysis of concepts of networks forensics through extensive investigation and by analyzing various methodologies and associated tools which should be used in the network forensic investigations. Detailed and in depth analysis of concepts of network forensic investigation on a designed/conceived network architecture was carried out which was then followed by analyzing various methodologies and tools employed. An innovative framework for the investigation was designed which can be used by any forensic expert. The acquired data was analyzed by using information, strategizing and collecting evidence and by analyzing and reporting of the methodologies on the conceptualized network. Consequently, it led to the researcher to adopt and utilize a powerful and efficient forensic network methodology that will ultimately help in improving the investigation process and providing required tools/techniques along with the requisite guidelines that will determine the approach, methods, and strategies which are to be used for networkiforensiciprocess to be followed and be executed with the use of relevant tools that will tend to help in the simplification and improvement of the forensics investigation process.
On Objectivity in Digital Reality Theory

The philosophical question of objectivity is applied to the ways that Digital Reality theory understands time, space, and quantity in Existence. The roles of the imaginary and transcendental numbers i, e, and π are discussed, including their use to represent quantities in Existence and to help define discrete objects in digital Reality. The significance of Euler’s identity (e i π + 1 = 0) in helping us understand analog-to-digital conversion is examined.
A Multi-view Fuzzy Compactness and Separation Co-clustering Algorithm

With the advent of the era of big data, although the relationship between samples can be found from individual clustering of each view, data in the real world generally has multiple representations, and different data representations can complement each other. Clustering performance can be made more accurate by mining the information hidden between various multi-view data, making real-word data more interpretable. In view of the above problems, the multi-view clustering is studied, and a multi-view fuzzy compactness and separation co-clustering(MvFCSCC)algorithm is proposed. Based on the fuzzy compactness and separation co-clustering (FCSCC), the algorithm clusters the two dimensions of data objects and features for each view, and then optimizes and assigns different weights to different views by iterating the weight vectors, and finally performs collaborative clustering according to the importance of different views. In order to evaluate clustering effectiveness, experiments were carried out on four multi-view datasets to compare the MvFCSCC with the other three clustering algorithms. The experimental results show that this algorithm not only effectively improves the clustering effect, but also has a stronger robustness to noisy data.
Personalized Point-of-Interest Recommendation with Relation-Enhanced Graph Convolutional Network

Point-of-Interest (POI) recommendation recommends different personalized services to interested users, which are widely used in people's daily life. However, with the massive increase in users and POIs, the POI recommendation system faces the following challenging problems: (1) The results of the recommendation service are not personalized enough, and little attention is paid to the details and semantic relevance. (2) Often face the problem of cold start (3) Recommended accuracy can be further improved. In order to cope with these difficulties, this paper proposes a POI recommendation method POI-Graph Convolutional Network with relation-enhanced Graph Convolutional Network(P-GCN). First, this paper mining the user's preferred POI information with a quantitative emotion from the user's comment information and historical information in the Yelp and DianPing datasets, and builds a representation of the relationship between the service user preferences knowledge graph (KG). On this basis, the P-GCN model is introduced, and uses end-to-end learning to mine the associated attributes between projects, and to make the high-order structural information and semantic information in the knowledge graph discovered. Finally, through the method of node aggregation of multi-hop neighbor node information, the high-order semantic relevance of user nodes and their potential preferences are obtained, and POI recommendations are made to users. Through extensive experiments, our method can better utilize the characteristics and POI of different users who own the same preferences, which can improve the precision and personalization of recommendation. The experimental results are superior to the baseline method in terms of evaluation indexes.
Caregiver Experience in Potty Training Children with Down Syndrome

Potty training assistance for children with Down syndrome is challenging because it takes longer to adapt and has physical and cognitive limitations. In addition, educating children with Down syndrome differs from one child to another. The study aimed to explore the role of parents or caregivers in teaching the potty training process to children with Down syndrome at States Special Needs School Semarang. The research was conducted using a qualitative phenomenological approach to determine parental participation in potty training in children with Down syndrome. Data collection was carried out by in-depth interviews and observations supplemented by field notes which were then transcribed verbatim. The search for informants used a purposive sampling method selected based on specific considerations with a total of 10 primary informants and those who cared for children with Down syndrome aged 7-14 years and were still in elementary school and two triangulated informants from teachers. Data analysis was carried out thematically. The results of the study found that the knowledge of most caregivers about potty training was limited to taking their children to the toilet, most of the children with Down syndrome were unable to urinate independently, the caregiver's difficulties when teaching potty training were problems communicating with the child, the caregiver's task in the potty training process was as follows: by finding solutions to make it easier for children to communicate and understand children's conditions. This qualitative research concludes that there is a theme regarding the role of caregivers with Down syndrome in potty training: caregiver knowledge about potty training, the behavior of children with Down syndrome during potty training, caregiver difficulties in potty training, and the caregiver's role or task in potty training.
Effects of a Competency-based Learning Environment for Elementary Health Instruction

The study tried to analyze effects of a competency-based learning environment for elementary school health education. Basically, it attempted to prove the effectiveness of the competency-based learning environment paradigm, which is one of the key topics in the era of 4th industrial revolution in the field of elementary health education. Two homogeneous groups were selected, at the outset, out of the 5th grade classes at an elementary school located in Seoul and experimented with them. As a result, first, there was a statistically significant difference in the health core competency between the groups. Second, there was a statistically significant difference in the health inclusive competency between the groups. This study will contribute to systematizing a theoretical paradigm of competency-based learning environment, which would be a vital theory in the field of educational technology for the era of 4th industrial revolution.
In Search of Policy Implications Through a Comparative Study on the National Policy of Fostering World-Class Universities in Korea and China

This study aims to identify the similarities and differences in recent academic excellence initiatives between Korea and China, focusing on BK21 Plus project and Double First policy. To this end, those policies were compared and analyzed based on the five-dimensional analysis framework such as policy goals, selection criteria, selection process, selection results, and funding allocation approach. It found that while they had something in common, such as the principle of choice and concentration, the use of competition mechanism, and a large injection of public funding, several key differences emerged in the time dimension of policy goals, emphasis on selection criteria, transparency in the selection process, regional and institutional concentration, and the use of funding. This study contributes to expanding understanding of the two countries' academic excellence initiatives and offering implications for building world-class universities and designing a national academic excellence initiative in the future.
Syntactical method for reconstructing highly fragmented OOXML files

A common task in computer forensics is to recover files that lack file system metadata. In the case of searching for file fragments in unallocated space, file carving is the most often used method, which is ideal for unfragmented data. However, such methods and the tools based on them are ineffective for recovering OOXML files with a high fragmentation level. These methods do not provide reliable determination of the correct order of fragments. Techniques for reconstructing documents based on the analysis of words and phrases are also ineffective in fragmented OOXML documents. The main reason is that OOXML files are ZIP archives and, as a result, store data on disk space in a compressed form. This paper proposes a syntactical method for reconstructing OOXML documents based on knowledge about the internal structure of this file type, regardless of their content. The details of the implementation of the reconstruction algorithm and the peculiarities of restoring certain types of local elements of the document were considered. The efficiency of the algorithm was tested on the Govdocs1 and NapierOne datasets. The proposed method was applied to 4096-byte data blocks, which correspond to the standard cluster size in different file systems. The experimental results confirmed the method's suitability for practical use with 82.97 % of recovered files, including 34.38 % reconstructed completely, 0.43 % excluding the last 21 bytes at most, and another 48.16 % excluding embeddings that require other approaches. In the latter case, obtaining a fully working document without displaying graphic images and other contents of different embeddings is possible. The presence in OOXML files of CRC-32 hashes of the uncompressed data stream of each local element allows us to confirm the correctness of information recovery and its integrity unambiguously. Simultaneously, the method's effectiveness depends mainly on data verification methods during the reconstruction of local elements that occupy at least three clusters in the file. Therefore, this method is supposed to be improved by developing new mechanisms for verifying XML elements.
LaserNet: a method of laser stripe center extraction under non-ideal conditions

The extraction of the center of a laser stripe is a key step in line-structure measurement, where noise interference and changes in the surface color of an object are the main factors affecting extraction accuracy. To obtain sub-pixel level center coordinates under such non-ideal conditions, we propose LaserNet, a novel deep learning-based algorithm, to the best of our knowledge, which consists of a laser region detection sub-network and a laser position optimization sub-network. The laser region detection sub-network is used to determine potential stripe regions, and the laser position optimization sub-network uses the local image of these regions to obtain the accurate center position of the laser stripe. The experimental results show that LaserNet can eliminate noise interference, handle color changes, and give accurate results under non-ideal conditions. The three-dimensional reconstruction experiments further demonstrate the effectiveness of the proposed method.
Survey of State Parole Agencies on Reentry Services (DOJ)

Federal Grants & ContractsVolume 47, Issue 9 p. 4-5 Grants Alerts Survey of State Parole Agencies on Reentry Services (DOJ) First published: 04 April 2023 https://doi.org/10.1002/fgc.32951Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume47, Issue96 April 2023Pages 4-5 RelatedInformation
Transdisciplinary Research Program of N.I. Zhinkin: Inner Speech and Its Experimental Projections

The philosophical and theoretical legacy of the famous Russian philosopher, psychologist, film theorist Nikolai Ivanovich Zhinkin (1893–1979) is compre­hended in the article as an integral cultural and historical phenomenon. Based on his fundamental works and archival materials (letters, drafts, abstracts of re­ports and articles), the authors attempt to reconstruct the main characteristics of his transdisciplinary research program and demonstrate its current experimen­tal projections. This approach allows us to show that in the early philosophical, phenomenologically oriented works N.I. Zhinkin, speaking about the “thing” as a fundamentally social and historical phenomenon, outlined a methodological path, which he consistently implemented in the humanities and sciences – psy­chological, semiotic, logical and ethological – research of the late period. The authors pay special attention to experimental ethological studies, which N.I. Zhinkin carried out in the Sukhum monkey nursery in the 1950s. In addi­tion to the traditional interpretative and translational strategies that made it pos­sible to clarify the specific meanings of social “things”, he experimentally recorded the presence of a communicative system in the acoustic screams of monkeys. Thus, the relevance of the methodological path of N.I. Zhinkin (his transdisciplinary research program) lies in the fact that he used the experi­mental method to study social “things”, i.e. sign-symbolic reality (language, thinking as inner speech, language memory, information)
CYBER SECURITY IN INDIA: IS IT TIME TO AMEND THE LAWS?

The paper focuses on the laws related to cyber security in India. India is a growing country, with developments in many fields, the technology is one of them. The Internet has evolved into a weapon for wiped activities, which have given rise to the cybercrimes and to stop them cybersecurity laws were Introduced. Cyber laws are the area of the judicial systems which specifically deals with the Internet and crimes related to it. In India Information Technology Act, 2000 deals particularly with the cybercrimes and its criminals. It has emerged as a serious concern for law enforcement authorities throughout the world in their efforts to safeguard people who are harassed and mistreated online. Thus, this study concludes that, at a time when many other nations have already begun adopting stricter and specific cyber laws, India is slightly behind the pace. In this sense, suitable intervention is essential. To meet the challenges posed by fast evolving technology, the government should implement more appropriate amendments in Indian cyber legislation.