Reviewerly/exHarmony huggingface dataset:
Learning Alphabets Through Infographics

Early stages of reading and writing rest solely on the alphabet. Learning of letters with infographics in the elementary classes makes learning more easy and meaningful. The study examined the potentials of infographics in enhancing learning at an early childhood level especially on letters. The researcher utilized experimental design which including pre and post-test. The package was validated by experts with a reliability coefficient of 0. 77. The findings of this study showed that the experimental group means a score of 30.60 is higher than the control group means a score of 30.50 co-efficient. Moreover, the means score of 30.742 for females and 30.345 for male pupils was obtained. The study concluded that children at the early childhood level could learn better using infographics based approach. It was recommended that incentives should be provided for pre-school teachers to participate in highly effective staff development to help them integrate infographics into their teaching and learning. Also, infographics based approach be used for all subjects in early childhood education in Nigeria
Employ Twitter Data to Perform Sentiment Analysis in the Malay Language

The main intention of this research is to discover the significance of sentiment analysis of Twitter data whether it is positive, neutral or negative.The sentiment analysis is dependent mining about textual content which being extracted and identified as contextual and subjective knowledge in such perceptible origin of recent rapid expanding computer science research.We started with a systematic literature review, where we had adopted both qualitative coding and text mining by scrutinizing 3282 of input of textual data retrieved from Twitter Streaming API.We perceived the problem as the decision trees kind of sentiment analysis in learning and information gaining.Therefore, we showed how basic decision trees are built to calculate the sentiment values of Twitter data.Sentiment analysis has transformed from interpreting online textual output analysis into perceiving contextual social media texts for example from Twitter.Hence, two decision trees were built to observe the performance and information gaining of decision trees.Thus, the precision of both decision trees led to the precision percentage that will be respectively stated, and the best decision tree can be obtained.
Currency Counting for Visually Impaired Through Voice using Image Processing

The Blind people may not perceive the currency as well as count them.Thus, to resolve this difficulty, the framework called "Image-based currency recognition and counting system" is useful.Although the Currency Recognition System helps the blind and visually impaired people for detecting the currency still it is not so adequate.Moreover, they just represent the currency one at a time in the form of voice as the output but don't sum up the total amount.So, it becomes difficult for the blind person to determine the total amount.In this paper we are proposing a currency recognition system that makes use of the SIFT algorithm which is very efficient and fast in detecting the currency and after detecting, it will sum up the total currency detected and will provide the result in the form of output to the blind people.The proposed system is implemented to detect seven different types of Indian currencies.Initially, a pre-processing technique is implemented on the currency note.Then, ROI and OCR are implemented followed with SIFT that provides key descriptors.Finally, the Hamming distance from the KNN algorithm is implemented for matching key descriptors from the feature extraction stage.The results have shown that the proposed system can be implemented in real-time scenarios with an accuracy rate of 93.71% and running time of 0.73 seconds
Research and Application of Multiscale Representation and Regularization Method in Image Recognition

Abstract Image Recognition is the Use of Computers to Process, Analyze and Understand Images to Identify Different Patterns of Objects and Images. in This Paper, We Introduce the Idea of Nonlocal Processing on the Basis of Regularization Tv. the Process of Image Recognition System is Divided into Five Parts: Image Input, Preprocessing, Feature Extraction, Classification and Matching, in Which Preprocessing Can Be Divided into Image Segmentation, Image Enhancement, Two Value Reduction and Refinement and So on. the Paper Presents Research and Application of Multiscale Representation and Regularization Method in Image Recognition. At the Same Time, through Programming in the Matlab Programming Environment to Achieve the Relevant Algorithm, through the Actual Image Recognition Experiments, the Use of These Conclusions, and Finally to Accurately Identify the Results.
Vehicle recognition model for complex scenarios based on human memory mechanism

The prevailing vehicle recognition technology is adversely affected by the environment such as complex traffic scenarios and weather conditions. This paper proposes a robust vehicle recognition model based on human memory mechanism named Memory-based Vehicle Recognition Model (MVRM). Motivated by t he success of memory and attention mechanism, we explore some features of human visual attention model. Fusing short term and long-term memory modules together yield deeper architectures recognizing increasing complex environmental scenarios. Firstly, a rare motion feature has been introduced to measure the visual salience, which improves the accuracy of the visual attention mechanism. Second, a model of vehicle salient region recognition has been established. The results of experiments show that the dynamic vehicle recognition rate of MVRM is 77.10%, while its false recognition rate has only a nominal value of ∼4.5%. Furthermore, the model offers good recognition of vehicle targets under complex environment conditions related to weather and road traffic.
Corpus Pattern Analysis for Learner Lexicography: A Pilot-study of Lithuanian Verbs

The aim of this paper is to present a pilot study which applies the framework of Corpus Pattern Analysis (CPA, Hanks 2004) to analyse some Lithuanian verbs which form part of the basic vocabulary. CPA draws on the insights of the corpus-driven language analysis and contextual and functional theory of meaning: a meaning of a word is associated with a specific lexical and grammatical environment, e.g. corpus patterns which represent an interconnection of lexical and grammatical elements. The CPA procedure is one of the several corpus-driven methods differing from the pattern grammar (Hunston, Francis 2000) in the way that CPA not only uses typical grammatical categories (e.g. word classes) but also introduces semantic values (e.g. semantic types) to distinguish different senses of a word. Semantic types are often the main separator of meanings, especially when two verb senses are associated with the same grammatical pattern. Concerning learners’ dictionaries, CPA could provide learners with more detailed usage data, and this could lead to a better understanding of meaning differences, important both for language reception and language production. After introducing the CPA methodology, we present the CPA analysis of two Lithuanian verbs, namely, the inductive procedure followed to observe and define meaning-related patterning. We also discuss the problematic issues related to the application of CPA as identified in this study and mentioned by other CPA practitioners. First, observing and defining corpus patterns is a challenging task for lexicographers, especially because of the pattern / meaning division and generalizations related to semantic types. The second problematic aspect is automatization in the process of pattern recognition. The third issue relates to foreign language learners as a target group: meaning-related patterning observed in the data has to be presented in a learner dictionary in a user-friendly way.
The Influence of Diary on the Students' Narrative Writing Skills Quasi-Experimental Study at UIN Syarif Hidayatullah, Jakarta

This study aims to determine the effect of the diary media on the ability to write narratives for Semester III students. The sample in this study was the 60th semester students of the Department of English Language Education at the Syarif Hidayatullah State Islamic University in Jakarta totalling 60 students. Sampling using a multistage random sampling technique. The method used is an experimental method, with non-equivalent experimental design. Data collection was performed using a narrative writing test and analysed using the t test. The results showed that the diary media had a significant effect on the ability to write student narratives with a coefficient of t count &gt; table = 6.41&gt; 1.70 at a significant level α = 0.05. These results indicate that the diary media can be used as an alternative media in learning to write student narratives. Therefore, teachers need to know the importance of using appropriate learning media to develop students' narrative writing skills.
A Study And Experimental Assessment Of The Cognitive Weight, Base Of The Cognitive Metrics

Source code metrics have been used as indicators of code comprehension according to its complexity. Shao and Wang proposed a cognitive complexity measure based on the cognitive weight of the basic control structures. This cognitive weight is a fixed value assigned to each control structure, which was calculated by means of a series of empirical experiments. Given the lack of information and incompleteness of the experiments, improvements to these experiments must be made before a measure can be used. In this paper, a study on the overall state of the cognitive weight, including its experiments, is presented, along with an empirical experiment which will help us to assess the validity and accuracy of the cognitive weight. The experiment consists of applying a survey to 114 students. It contains source code complexity and comprehension questions that allow us to obtain conclusions about the validity of the cognitive weight.
Quality of Visual Aesthetic Facets in Designing Christian and Islamic Religious Websites

This study considers visual aesthetics as a significant contributor to involving the user in online visual communication. It argues that religious websites as an online environment require attention to the impressive communication process. In this study, we focus on how Christian and Islamic religious websites use the five visual aesthetic facets, including Simplicity, Diversity (visual richness), Colourfulness, Typography, and Image on homepages. To address this issue, 10 Christian and Islamic religious websites are selected from the Alexa top religious websites. Using a descriptive-analytics approach, evaluation of visual aesthetics is conducted through a checklist based on the theoretical and empirical literature reviewed on visual aesthetics and website design. It is included some criteria about visual aesthetic items in web design. The results show that Christian and Islamic religious websites have different types of visual aesthetics. Furthermore, Christian websites with high simplicity, low diversity, paying attention to the colour factor, appropriate typography and high-quality images, received the highest overall aesthetics ratings. While Islamic websites, with low simplicity, low diversity, little attention to the colour and typography characteristics, as well as low-quality images, received the low scores.
The Role of Knowledge Management and Technological Innovation in the Performance of New Product Development: An Empirical Study of Pharmaceutical Industry in Jordan

So far, firms are ignorant of actual implications of managing knowledge and technology innovation. So far, firms are ignorant of actual implications of knowledge management and technology innovation. This research investigated the role of managing knowledge and innovation technology in new product performance of Pharmaceutical Jordanian manufacturing firms. Data were gathered from 4 firms out of 15 companies by developed questionnaire. The Cronbach&amp;rsquo;s Alpha was utilized to examine reliability, and multiple regressions analysis was conducted for hypothesis testing. &#x0D; &#x0D; According to extant literature, a research frame was designed showing a positive correlations&amp;rsquo; among research constructs. Results of regression analysis reveal that knowledge management with (Knowledge creation and knowledge transfer) and technology innovation with (product innovation and process innovation) impact new product performance with its constructs. Results of regression analysis reveal that knowledge management and technology innovation effect new product performance. Results of this research may assist academics and managers in designing knowledge management programs to achieve higher technological innovations in product and process to develop new products performance through a successful financial and marketing performance. The study concludes and recommended to conduct further research.
Detecting Memory Life-Cycle Bugs With Extended Define-Use Chain Analysis

OS kernels leverage various memory allocation functions to carry out memory allocation, and memory data in kernel space of OS should be cautiously handled, e.g., allocating with kmalloc() and freeing with kfree(). However, real cases do exist where memory data is incorrectly allocated/freed, not checked before dereferenced, or left unfreed when out of use. We define these cases as Memory Life-cycle (MLC) bugs, and according to what we know, this new type of software bugs has not been deeply researched yet. In this paper, we go deep into the life-cycle of kernel memory space, including allocation, dereference and free, and propose the first systematical study of MLC bugs and build an automated and scalable detection framework, MLC bug sanitizer (MLCSan). MLCSan is capable of revealing memory allocation and free functions OS kernels. Besides, the occurrences of allocating, dereferencing and freeing sites can be automatically detected by MLCSan, leading to cases where MLC bugs may appear. Moreover, experiment result of analyzing the latest mainline OS kernels with MLCSan is a strong proof that MLCSan is effective in detecting MLC bugs and can scale to different platforms, in which 41 new bugs are identified in Linux and FreeBSD. And undoubtedly, we will open source MLCSan prototype to contribute to the security research in this area.
SLSort (Smallest-Largest Swap Sort) – A Sorting Algorithm

Sorting problem is one of the classic problems of Computer Science.Sorting is among the most basic tasks involved in computation and is very commonly used in almost every application.Design of a fast sorting algorithm has always been a challenge and there has been a lot of research since many decades.A variety of sorting algorithms have been designed amongst which Quicksort is considered as the fastest in-place sorting algorithm and Timsort is being used in practically for sorting problems in programming languages like Java and Python.Few parallelized versions of sorting algorithms have also been developed.This paper proposes a Divide and Conquer based scheme of in-place sorting which sorts the array using repeated swap operations of largest and small elements of two sub-arrays of the original array.The procedure is recursively applied on the sub-arrays until they are reduced to size 1.Analysis and results show that the algorithm performs better than quicksort for an input that is almost or completely sorted.
Using Fuzzy c-Means for Weighting Different Fuzzy Cognitive Maps

Currently, complex socio-ecological problems have increasingly prevailed with uncertainty that often dominates these domains.In order to better represent these problems, there is an urgent need to engage a wide range of different stakeholders' perspectives, regardless of their levels of expertise and knowledge.Then, these perspectives should be combined in an appropriate manner for a comprehensive and reasonable problem representation.Fuzzy cognitive map (FCM) has proven to be powerful and useful as a soft computing approach in addressing and representing such problem domains.By the FCM approach, the relevant stakeholders can represent their perspectives in the form of FCM system.Normally, relevant stakeholders have different levels of knowledge, and hence produce different representations (FCMs).Therefore, these FCMs should be weighted appropriately before the combination process.This paper uses fuzzy c-means clustering technique to assign different weights for different FCMs according to their importance in representing the problem.First, fuzzy c-means is used to compute the membership values of belonging of FCMs to the selected clusters based on the FCMs similarities that show how convergent and consistent they are.According to these membership values, the importance clusters' values are calculated, in which a cluster with a high membership value from all FCMs is the cluster with the high importance value, and vice versa.Next, the importance values for FCMs are derived from the importance values of the clusters by looking at the amount of contributions of FCMs memberships to the clusters.Finally, FCMs importance values are used to assign weight values to these FCMs, which are used when they are combined.The suitability of the proposed method is investigated using a real dataset that includes an appropriate number of FCMs collected from different stakeholders.
The Advancement of Technology in Schools and Universities

In an age of continual change and growth, PreK–12 school settings and college campuses have the opportunity to expose students to the advancements being made in technology through engagement with n...
Melanoma Detection using Deep Learning

There is a lot of scope for automation in health care and health management. Let us consider the dermatology sector, which deals with different types of skin diseases .In which cancer is most difficult for diagnosis different types of cancer like melanoma can be cured if found in the early stages. In this paper we introduce a method to digitize and automate the process of diagnosis of melanoma using deep learning techniques. Deep learning techniques have achieved high accuracy rates in classifying images in various fields. Deep learning is a technique that attempt to teach a computer to gain knowledge through example. Use of deep learning to automatically detect melanomas in dermoscopy images. The system first preprocesses the images by removing unwanted artifacts and it then classifies the images using Convolution Neural Network (CNN). Then the classifier has been tested on preprocessed and unprocessed dermoscopy images to evaluate its effectiveness.
PENGARUH PENGGUNAAN MEDIA VIDEO KARTUN TERHADAP KETERAMPILAN MENULIS CERPEN SISWA KELAS IX SMP

The Effect of Using Media Cartoon Videos Toward The Skill of Writing Short Story for 3rd Year Students of SMP. This study aims to describe: (1) skill of writing short story for 3rd year students of SMP Negeri 20 Makassar before using media of cartoon videos, (2) skill of writing short story for 3rd year students of SMP Negeri 20 Makassar after using media.of cartoon videos, (3) the effect of using media of cartoon videos toward the skill of writing short story for 3rd year students of SMP Negeri 20 Makassar. This research is a pre-experimental design research with one-group pretest-posttest design using pretest at the beginning of conducting research and post test as the final test. The results of the study found that: (1) skill of writing short story for 3rd year students of SMP Negeri 20 Makassar before using media of cartoon videos are categorized as less skilled, (2) skill of writing short story for 3rd year students of SMP Negeri 20 Makassar after using media of cartoon videos are categorized as skilled, (3) using media of cartoon videos affect the skill of writing short story for 3rd year students of SMP Negeri 20 Makassar.
Locator Bracelet with QR Code for Elderly People with Alzheimer’s

An accessory for monitoring and offering personal information related to risk situations of patients with Alzheimer's, thereby providing support, security and tranquility for family members and caregivers.A bibliographic research is carried out in this study with focus on on Alzheimer's Disease as well as on the patient's relationship with the family and the care offered to them.Furthermore, a quantitative study is performed, using specific forms and documentary based on public domain documents about the disease.Alzheimer's is an incurable and degenerative disease that affects nerve cells, and is responsible for causing spatial disorientation on memory.Spatial disorientation is one of the recurrent symptoms in with a patient suffering from AD.When the symptoms of this disease start to evolve, some patients do not feel familiar and do not even recognize certain places.The difficulty for family caregivers is noticeable when in dealing with these risk factors.This project is aimed at facilitating the reunion of the elderly with their family by proposing an accessory that provides 24 hours a day monitoring, along with a code that contains the patient's personal information.Given this context, cognitive research is relevant to express the need for minorities, leveraging home security, allowing patients the ability to have their independence for a longer time, without the disease preventing them from completely exercising their daily activities.
Crowd counting via an inverse attention residual network

Deep learning has enabled substantial progress in crowd counting, but existing methods still face difficulties due to significant scale variations, severe occlusion, and overcrowding. To explicitly address these problems, we propose a crowd counting method based on the inverse attention residual network. Initially, the first 10 layers of VGG-16 with a strong transfer learning ability and a feature extraction ability are used as the front-end network for preliminary extraction of head features. Afterward, we use the inverse attention residual module to divide the background information and crowd information and retain the accurate pixel position information to reduce the impact of various types of noise in the input image. Finally, the mini multiscale dilated convolution is used as the backbone of the back-end network to perform the aggregation of feature maps, so the network can learn the details of heads at different scales and use the 1 × 1 convolutional layer to regress the feature maps to the density map. As a result, the above problems are resolved to a certain extent. Experimental results show that the mean absolute error and the mean square error of the proposed algorithm are significantly lower than those of the comparison algorithms.
The Effectiveness of Retelling Short Story towards Students’ Accuracy in Speaking Skill

The objectives of this research is to find out the effectiveness of retelling short story toward students’ accuracy in speaking skill. This research employed pre-experimental method which entails pre-test, treatment, and post-test. Speaking test was used in collecting the data. A total of 39 students of MTsN Makassar as the subject of this research.. The data on the students’ speaking skill were analyzed using descriptive and inferential statistics. The results demonstrated that there was a differentiation between the students’accuracy in speaking skill before and after being thought through retelling short story. The null hypotesis (H0) was rejected and the alternative hypothesis (HI) was accepted because the t-test value was 5,42 which was higher than t-table value 2,024.. The mean score of the students before teaching retelling short story was 2,95 classified as poor score. After teaching retelling short story the mean score was 3,38 classified as a good score. Based on result of data analysis above, the researcher drew conclusion that retelling short story could effective toward students’ accuracy in speaking skill.