Reviewerly/exHarmony huggingface dataset:
DEVELOPING THE “QUIET BOOK” BASED ON THE ACEHNESE CULTURE TO CULTIVATE CHARACTER OF YOUNG CHILDREN: PRELIMINARY FINDINGS

The use of a learning media in introducing Acehnese culture to early childhood education is an important element to do. This is because, with the help of the media, efforts to introduce the local culture will become more attractive and easily accepted by children. But now the availability of learning media in introducing a local culture for early childhood is still limited. So the researchers are interested in conducting this research that is a type of development research (Research and Development) which aims to develop a learning media in the form of quiet book based on the Acehnese culture for early childhood. Data collection in this study was conducted through interviews, literature studies and also validation questionnaire sheets from media experts and material experts. The development of the initial concept in this study consisted of the stages of defining, designing and developing. At the development stage, product validity was tested by media experts with a score of 79% (feasible) and a score of assessment by material experts at 86% (very feasible). Then after the revision of the quiet book media based on Acehnese culture, the percentage of assessment scores from media experts was 85% (very feasible) and the percentage of validation scores from material experts was 95% (very feasible). Thus, based on the results of validation by media experts and material experts, it can be concluded that quiet book products based on Acehnese culture are very feasible to be used as learning media for early childhood in introducing Acehnese local culture. Furthermore, the researchers will continue the testing phase of this quiet book media based on the Acehnese culture so that it can be developed and disseminated for the benefit of early childhood learning. Keywords: Quiet Book, Acehnese Culture, Early Childhood Character
PERBAIKAN KONTRAS CITRA MAMMOGRAM PADA KLASIFIKASI KANKER PAYUDARA BERDASARKAN FITUR GRAY-LEVEL CO-OCCURRENCE MATRIX

In this era to recognize breast tumors can be based on mammogram images. This method will expedite the process of recognition and classification of breast cancer. This research was conducted classification techniques of breast cancer using mammogram images. The proposed model targets classification studies for cases of malignant, and benign cancer. The research consisted of five main stages, preprocessing, histogram equalization, convolution, feature extraction, and classification. For preprocessing cropping the image using region of interest (ROI), for convolution, median filter and histogram equalization are used to improve image quality. Feature extraction using Gray-Level Co-Occurrence Matrix (GLCM) with 5 features, entropy, correlation, contrast, homogeneity, and variance. The final step is the classification using Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM). Based on the hypotheses that have been tested and discussed, the accuracy for RBFNN is 86.27%, while the accuracy for SVM is 84.31%. This shows that the RBFNN method is better than SVM in distinguishing types of breast cancer. These results prove the process of improving image construction using histogram equalization and the median filter is useful in the classification process.
COMPETÊNCIAS DOS BIBLIOTECÁRIOS NA GESTÃO DOS DADOS DE PESQUISA

In the scientifc scenario, the intensive generation and use of research data require innovative management models, which, in turn, needs new skills for the implementation of informational infrastructures focused on data. Librarians have an important role in data management services. This fact reinforces the need to develop skills for optimize their actions in this feld. This means improving librarians’ skills in supporting researchers, deploying service and literacy infrastructures, developing methodology for data sharing and reuse among other requirements. Taking as methodology the literature of the area, the study analyzes the role of librarians and the necessaries competences for the professional performance in the management of research data, outlining a professional profle of the data librarian and contributing to the debate on the subject in the area of Information Science.
Research Guides: Sustainable Access to Scholarship: Budget & Costs

This guide outlines how the library will seek to identify a model of access to scholarly content that meets research and instructional needs as well as is fiscally sustainable.
Video-based Human Age and Gender Recognition using Deep Convolutional Neural Networks

In this paper, we propose an automatic age and gender recognition system from a live-video stream. Haar cascade classifier is used for face detection while for age and gender recognition,VGG16 is used. VGG-16 is a famous deep-Convolutional Neural Networks(CNN) architecture.VGG-16 network is trained on ImageNet dataset which has over 14 million images and 1000 classes, and achieves 92.7% top-5 accuracy. It surpasses AlexNet network by replacing large filters of size 11 and 5 in the first and second convolution layers with small size 3x3 filters. Transfer learning is implemented for iterative transfer of knowledge and better recognition with greater accuracy. IMDB-WIKI dataset is used for training and testing of the model which is the largest dataset of human faces with gender, name and age information. This real-time prediction model is characterized by more accuracy when compared to the publicly available methods. As face analysis is a challenging task because of variations in images having different postures, lighting ,angles and expressions, this model successfully overcomes all the barricades and proves to be the most accurate while using less computational resources.
Towards a Requirement-driven Identification and Selection Process for Blockchain Platforms

Blockchain is a decentralized transaction and data management technology firstly developed for the Bitcoin cryptocurrency. In recent years, the interest in this technology has increased and its use has extended to other scenarios to address, for example, traceability and transparency issues in different application domains (e.g. health, government, art). On the other hand, there is a growing offer of platforms that support this technology with different characteristics (e.g. smart contracts, permissioned). Therefore, the task of identifying which platform is the most suitable for a given scenario is not usually easy. This paper proposes a requirement-driven process for the identification and selection of Blockchain platforms. The process takes as input the business requirements of a particular scenario in order to determine, firstly, the set of characteristics required by the scenario and, secondly, the most suitable Blockchain platforms based on a taxonomy also proposed in this work. The process was assessed through existing projects in the industry that use Blockchain platforms, which allowed validating the adequacy of the proposal.
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space

Invariance (defined in a general sense) has been one of the most effective priors for representation learning. Direct factorization of parametric models is feasible only for a small range of invariances, while regularization approaches, despite improved generality, lead to nonconvex optimization. In this work, we develop a convex representation learning algorithm for a variety of generalized invariances that can be modeled as semi-norms. Novel Euclidean embeddings are introduced for kernel representers in a semi-inner-product space, and approximation bounds are established. This allows invariant representations to be learned efficiently and effectively as confirmed in our experiments, along with accurate predictions.
PENINGKATAN PRESTASI BELAJAR IPA MELALUI PEMBELAJARAN KOOPERATIF TGT PADA SISWA KELAS VIII D SMP NEGERI 2 SUKOHARJO SEMESTER I TAHUN PELAJARAN 2017/ 2018

This research is an attempt to describe the increase of interest in IPA of students of class VIII D SMP Negeri 2 Sukoharjo after the application of Teams Games Tournament (TGT) learning model.This research uses Class Action Research (PTK) method which includes planning, action, observation and reflection.The subjects in this study were students of class VIII D SMP Negeri 2 Sukoharjo, which amounted to 32 students.The object of this study is interest in learning IPA of students through Teams Games Tournament learning model (TGT).Data is fed by observation, test and documentation.Data analysis using comparative descriptive analysis technique with percentage.The results of this study indicate an increase in learning achievement of IPA students.This can be seen from the average score of learning achievement of IPA students also experienced an increase before the action of 69,2, in the first cycle of 78,3 and on the second cycle of 82,5.In addition, the percentage of students' learning mastery, example
플립러닝의 학습동기가 미용관련 수업의 흥미도 및 수업의 만족도에 미치는 영향

The purpose of this study is to substantiate the impact of class motivation that is identified as an impact variable that affects class satisfaction of students who took flip learning aesthetics departments on interest levels in class and their satisfaction of classes. It was conducted on a total of 270 male and female college students who took flip learning classes in their majors related to aesthetics in Gwangju and Jeonnam. A total of 235 questionnaires were used for the final analysis. Data was analyzed using SPSS for Windows 21.0 statistics package, and technical statistics analysis, frequency analysis, reliability analysis, and regression analysis were conducted. The results of this study can be summarized as follows. First, in order to identify the perception and class participation status of flip learning classes related to aesthetics majors, preferences prior to participating in classes were categorized into the seven factors of learning activities, frequency of watching videos in flip-learning classes, frequency for class participation, activities that are most helpful in classes, class difficulty emphasized in flip learning classes, and intent to participate in classes again. Second, study motivation of flip learning was found to have statistically significant positive impact on interest in class (p<.001). Third, it was found that flip learning class motivation had a statistically significant positive impact on class satisfaction (p<.001). Flip learning study motivation affecting class satisfaction was found to have statistically significant positive (+) impact in the order of relativity motivation, attention span, and confidence motivation (p<.05).
Effective Text Detection in Composite Images with Simple Background

Text detection in simple and complex background composite images is a challenging task in computer vision. This paper proposes an effective Uyghur text detection system based on channel-enhanced maximally stable extremal regions (MSERs) algorithm and convolutional neural network (CNN) in simple and some complex background composite images. First, using a channel-enhanced MSERs algorithm to detect component candidates. Next, many non-text candidate regions are removed according to prescribed heuristic rules. Then, the remaining candidate regions are classified with the trained CNN binary classification model and keep the regions which are predicted as text candidate regions. Last, the candidate regions are connected or removed according to the features of the Uyghur word, and merge together the remaining regions to text line. In this paper, a new big images dataset is built to make the small images dataset and evaluate our system.
Analysis of Cluster based Self Organization Map (SOM) Algorithm in Various Color Spaces Models

Image segmentation means separation process that can divide the original image into smaller area with similar attributes. In this proposed system, input images are taken from the Berkley Image Segmentation Database (BSD) for color image segmentation.  Various color space of images such as RGB, HSV and L*A*B* are used for the segmentation process. Due to effect of the color conversion function reduce the input images size is not flexible, Image J software is used to get the same size of images for different color space. The subjective and objective measured is applied to analyze the color images. Then the cluster based self-organization map (SOM) is applied to produce a low-dimensional input space of the training samples. SOM method develops the ratio of color images similarity and spatial relationship of objects within an image. In this system, the features of color similarity in the image is first segmented into cluster regions and  the resulting regions are treated by computing the spatial distance between any two cluster regions and then the labeling process is made by SOM.
WOP 2019: Workshop on Ontology Design and Patterns 2019 : Proceedings of the 10th Workshop on Ontology Design and Patterns (WOP 2019), co-located with 18th International Semantic Web Conference (ISWC 2019), Auckland, New Zealand, October 27, 2019

WOP 2019: Workshop on Ontology Design and Patterns 2019 : Proceedings of the 10th Workshop on Ontology Design and Patterns (WOP 2019), co-located with 18th International Semantic Web Conference (ISWC 2019), Auckland, New Zealand, October 27, 2019
Phish Detect-Real Time Phish Detecting Browser Extension

Now a days, hacking has become a trend, where the personal information or user data including login credentials, credit card numbers, such as transaction from our bank accounts, key information from government office, defense etc., which threatens the privacy and property security of netizens in wireless communication. This is being done by creating a shadow website which has similar looks and semantics of the legitimate website. So as to overcome these kind of circumstances, the concept of Phishing is introduced. The phishing is a type of social engineering attack to detect false URL’s. So, the paper introduces a new phishing website “PhishDetect” to check for the phishing or fake sites. The paper proposes a detection technique of phishing websites based on checking URL of webpages. The detected attacks are reported for prevention of hacking.
TOWARD A THRESHOLD MODEL OF CONSUMER AUTONOMY FOR HUMAN-SMART SYSTEM INTERACTIONS: A QUALITATIVE STUDY

As a consequence of digitalization, several consumer products and services are already functioning as artificial intelligence empowered systems, such as virtual assistants, smart homes and other Internet-of-Things services. These intelligent systems are gradually obtaining agency, autonomy and authority by which can have complex interactions with their environment. In the interaction between these systems and their human consumers the question of the need for consumer autonomy also arises. This research assesses the possible factors determining consumer autonomy, and decision mechanisms influencing the need for more or less individual autonomy in the consumers’ interactions with an intelligent system. Qualitative methodology is used based on the multistage interpretation of six in-depth clinical interviews. Results seem to confirm some facilitating and inhibiting factors of the need for consumer autonomy. The main result consists of the evidence regarding the presence of continuous evaluation and decision-making mechanism of consumers about their need for autonomy in the interaction with an intelligent system.
Secure and Efficient Attribute-Based Access Control Optimization Scheme for EMR System

Electronic Medical Record (EMR) system based on the cloud has serious leaks of patient data and private information. Access control and privacy protection are considered to be the keys to ensuring the privacy of patients. Attribute-Based Access Control (ABAC) can realize fine-grained control of cloud medical data, but lacks the corresponding encryption mechanism. Moreover, as the scale of medical data grows, the number of entity attributes and policies required by ABAC is increasing, leading to performance degradation. Most of the existing research schemes focus on security but ignore efficiency, and the encryption scheme does not fit ABAC mechanism well. This paper proposed a retrieval method that enables attribute value, which filtered invalid policies, and improved Ciphertext-Policy Attribute-Based Encryption (CP-ABE), shared attribute set with ABAC to save attribute processing time, thus constructing a secure and efficient SE-ABAC solution in EMR system. Finally, through simulation experiment analysis, the optimizes solution is effective and more efficient than traditional ABAC.
In Praise of Software Practice

It has been almost a third of a century since the world was caught up in a complex computing and software political clash. The clash was over something originally called the Strategic Defense Intiative (SDI), later somewhat-facetiously called "Star Wars," the notion of an anti-missile system that would shoot down an incoming enemy missile. The time was around the mid- 1980s. The two most vehement sides on the issue were - academic and theoretic computer science types, who thought it couldn't be done. - practitioner engineers and software people, who thought it could.
A Survey Paper on Android Operating System

Smart phone is considered an important innovation that has changed the human life in several aspects. Android has emerged as the most widely used operating system in Smart Phonesand is rapidly gaining market share, with dozens of smart phones and tablets either released or set to be released. This paper also provides different Version history and Android Security.
Enhancing the Vocabulary Skills through ICT based Teaching to High School Students of Marathi Medium: A Case Study

Vocabulary development refers to the knowledge of stored information about the meanings and pronunciations of words. Vocabulary development is important in learning to read in Second or Foreign language. Oral language skill development and vocabulary development go together. A person's vocabulary is the set of words within a language that are familiar to the person. Words make sense based on his or her understanding of the word. Acquiring an extensive vocabulary is one of the largest challenges in learning a second language. Vocabulary development is also a primary determinant of reading comprehension. There are various ways of vocabulary enhancement. In Marathi medium schools, still traditional rote learning methods are emphasized which makes the vocabulary enhancement process boring and time consuming. Students feel disinterested in such activities. The present study aims at finding out the problems and difficulties of Marathi medium students, through a case study of a village school in Muktainagar Tehsil of Jalgaon district, Maharashtra State. The sample is taken from a single school by purposive sampling method.
PMC-GANs: Generating Multi-Scale High-Quality Pedestrian with Multimodal Cascaded GANs.

Recently, generative adversarial networks (GANs) have shown great advantages in synthesizing images, leading to a boost of explorations of using faked images to augment data. This paper proposes a multimodal cascaded generative adversarial networks (PMC-GANs) to generate realistic and diversified pedestrian images and augment pedestrian detection data. The generator of our model applies a residual U-net structure, with multi-scale residual blocks to encode features, and attention residual blocks to help decode and rebuild pedestrian images. The model constructs in a coarse-to-fine fashion and adopts cascade structure, which is beneficial to produce high-resolution pedestrians. PMC-GANs outperforms baselines, and when used for data augmentation, it improves pedestrian detection results.
A Critical &amp; Comparative Study of Freudian Concept with Bhagwad Geeta

“Shrimad Bhagwad Geeta” is a part of the great epic ‘Mahabharata” written thousands of years back by the sage Ved Vyaas. The book has eighteen chapters, which contain the quintessence of the entire philosophy and vast ocean of spiritual knowledge of Vedas, Upnishads, Shastras, combined with all the other scriptures and literatures of the ancient India and Sanatan Hindus. But Bhagwad Geeta is not for a particular person, or religion, race, caste, or country, rather it is that divine treasure of knowledge that has been given by the Lord Krishna Himself to the entire humanity. Each individual, who is alive and breathing on this earth, has an equal claim and right over the unparalleled and liberating knowledge of this wonderful song sung by Krishna. Saying anything about Bhagwad Geeta is like showing a lamp to the sun but at the same time it is absolutely pertinent and important to keep spreading the most invaluable gems and pearls of this divine source of knowledge with people in this era when everyone around seems to be stressed and depressed mentally, psychologically, emotionally, and spiritually because this great scripture has the immense potential within itself to answer all the questions and complications that are facing the modern civilizations and men today. It is the elixir that can bring the humanity to immortality and can free it of all the psychological bondages and make it healthy and blissful forever. This research paper attempts to draw the similarities between the theories of Freudian psychologically and the teachings from Geeta and at the same time it endeavours to highlight how this scripture marvelously resolves all the conflicts that remain unanswered by the modern scientific psychological thought.
REVIEW ON CONTEMPORARY SECURITY ISSUES IN CLOUD COMPUTING ARENA: AN APPLICATION-CENTRIC APPROACH

Cloud computing is the technology that produced an unprecedented impact in the information technology domain worldwide. Many versatile applications have been built around this technology with cloud hosting a broad spectrum of data generated from sources ranging from high-computationally intensive data applications to lightweight service applications. With the advent of Internet of Things (IoT), cloud services have reached pinnacle which made innovative ideas into technical reality. There are revolutionary changes in the way people interact among themselves as well as with the environment around them. Along with the innovations come the potential threats. The IoT devices installed across the world generates huge volumes of data and cloud provides storage infrastructure for this data. Sensitive data such as body vital parameters, a financial transaction or parameters from a control system are typically part of this IoT data. Data integrity and security is a need forever in these networks. Most of the security mechanisms developed are cloud-centric and no security standards are devised for the data in transit from data source to cloud. If the transit data is corrupted, there are hardly any mechanisms that detect this tamper. Cloud based security is a dynamic research area and there are always challenges posed to the security developers by the attackers. This paper presents a review on various security frameworks, and methods developed. Application-specific security requirements were analyzed and challenges encountered with the incorporation of these requirements were elucidated. Finally, the research concludes how the security of the cloud based infrastructure improves by the integration of blockchain technology.