Reviewerly/exHarmony huggingface dataset:
A WEB-BASED MODEL FOR INSPECTION INCONSISTENCIES RESOLUTION: A NEW APPROACH WITH TWO CASE STUDIES

This research is aimed at introducing an enhanced inspection model known as Improved Software Inspection Model (ISIM) by making some enhancements to overcome or reduce the weaknesses identified in Formal model. ISIM includes a method of selecting suitable inspectors based on their expertise and work experience; provide a database that stores information on potentials; has a database that stores the possible causes of each of the defects stored in the defect database; and incorporates a few enhancements in the inspection process. To facilitate the selection of suitable inspectors and the inspection process, an inspection tool was developed using PHP with MySQL as database management system. To determine whether the use of ISIM can improve the inspection process, the quality of the process was evaluated using paired T-test which compares the differences between Formal and ISIM models, in terms of the number of defects detected, inspection time, and the productivity of the inspectors. Two case studies were conducted in two companies and inspections were carried out on two software development projects to collect the data needed for the statistical test. The statistical test results show that at&nbsp;ï¡ = 0.05, the mean values of the number of defects are detected; the inspection time; and the productivity of the inspectors when using Formal and ISIM are 19.78 and 28.13; 220.97 minutes and 213.69 minutes; and 0.0891 and 0.1328, respectively. This shows that there are significant improvements in the number of defects detected (i.e. difference = 8.35; 42.21%), and productivity of the inspectors (i.e. difference = 0.0437; 49.05%). However, there is only a slight improvement in the inspection time. It can be concluded that ISIM together with its tool improve the quality of inspection process in the two case studies.
A secure deduplication scheme for encrypted data

Cloud storage (CS) is gaining much popularity nowadays because it offers low-cost and convenient network storage services. In this big data era, the explosive growth in digital data moves the users towards CS to store their massive data. This explosive growth of data causes a lot of storage pressure on CS systems because a large volume of this data is redundant. Data deduplication is a most-effective data reduction technique that identifies and eliminates the redundant data. Dynamic nature of data makes security and ownership of data as a very important issue. Proof-of-ownership schemes are a robust way to check the ownership claimed by any owner. However to protect the privacy of data, many users encrypt it before storing in CS. This method affects the deduplication process because encryption methods have varying characteristics. Convergent encryption (CE) scheme is widely used for secure data deduplication, but it destroys the message equality. Although, DupLESS provides strong privacy by enhancing CE, but it is also found insufficient. The problem with the CE-based scheme is that the user can decrypt the cloud data while he has lost his ownership. This paper addresses the problem of ownership revocation by proposing a secure deduplication scheme for encrypted data. The proposed scheme enhances the security against unauthorized encryption and poison attack on the predicted set of data.
Indonesian Vocabulary Mastery of Early-aged Children in Paud Melati Makassar

Early-aged children are individuals from 0 to 6 year old with unique characteristics and in the stage of growth and development, both physical and mental. Several aspects developed for their education include moral and religious values, social, emotional feelings, autonomy, language, cognitive, physical/motor skill, and art (Ramli, 2005, p.50). The development of these aspects affect each other, that is why it has to be optimally developed in every activity. One aspect of those basic skills should be developed in early-aged children is language. The objectives of this research are to describe (1) the quantity of Indonesian vocabularies in early-aged children; (2) certain Indonesian word classes in early-aged children; (3) certain scopes of Indonesian vocabularies in early-aged children; (4) Indonesian basic sentence pattern in early-aged children; (5) Indonesian basic sentence features considered by the form of integrative phrases; (6) Indonesian basic sentence features considered by the number of integrative phrases. The results of the research show that the vocabulary quantity of early-aged children varies due to external and internal factors. It triggers their inputs to vary from one to another. Word classes mastered by them include noun, verb, adjective, adverb, numeral, pronoun, and preposition. Based on the findings in the field, they have also mastered several vocabulary scopes, such as kinship, activity, place, number, color, universal objects, and animal types. In line with that, sentence patterns that have been mastered by them are FN-FV, FN1-FN2, FN-FA, FN-Num, and FN–FP.
A Deep Recommendation Model Incorporating Adaptive Knowledge-Based Representations

Deep neural networks (DNNs) have been widely imported into collaborative filtering (CF) based recommender systems and yielded remarkable superiority, but most models perform weakly in the scenario of sparse user-item interactions. To address this problem, we propose a deep knowledge-based recommendation model in which item knowledge distilled from open knowledge graphs and user information are both incorporated to extract sufficient features. Moreover, our model compresses features by a convolutional neural network and adopts memory-enhanced attention mechanism to generate adaptive user representations based on latest interacted items rather than all historical records. Our extensive experiments conducted against a real-world dataset demonstrate our model’s remarkable superiority over some state-of-the-art deep models.
STEAM — AN APPROACH TO TEACHING CHEMISTRY ON THE UPDATED CONTENT OF EDUCATION

This article solves the problem in the framework of the state program of development of education and science for 2016-2019, provided for the implementation of STEM Education.To implement the new educational policy, it is planned to include STEM elements in the curriculum, which are aimed at the development of new technologies, scientific innovations and mathematical modeling.Almost none of the existing school programs in chemistry pays enough attention to the quantitative experiment STEM.In practice, only individual chemistry teachers use, and the emphasis is usually shifted to extracurricular activities of students, in particular, the organization of project work, field research, elective courses and circles.Thus, there is a contradiction between the classical quantitative chemical experiment and at the same time Евразийский Союз Ученых (ЕСУ) #3 (60), 2019 almost unclaimed, didactic potential of digital laboratories and the need to increase the cognitive activity of students, awareness of their knowledge of chemistry.Particular attention is paid to the possibility of using the Pasco digital laboratory in the study of natural Sciences, as well as the use of ready-made applications for the development of creative thinking and creating your own learning scenario, such as Storyboard That.The author developed and adapted laboratory work using the PASCO tool, created a methodical case of practical work on the updated chemistry program for 7, 8 classes using various sensors in three languages: English, Kazakh, Russian.The result of using the digital laboratory was the creation of an application by students in the Play market and AppStore applications chemicals.kz"3D map of Kazakhstan chemical elements", this application is also translated into various languages, making it available to a wide educational audience, and most importantly, it is planned to finalize, voice and can be implemented as an inclusive education
Some Aspects of Communication Via Electronic Mail (E-mail)

Electronic mail can be considered from several aspects.Nonetheless, electronic mail should be primarily observed as a form of human communication.The way it is being realized is less important.Since electronic mail is the human form of communication, it necessarily connotes specific forms of human behaviour.Each deformation of that behaviour should be observed through the prism of generally accepted social norms.While those norms reflect cultural, ethical and moral frames of some (specific) social community, they should be respected.In such circumstances, electronic mail communication's participants can anticipate different situations.Some of those should be isolated, removed or sanctioned.The paper offers a comprehensive overview and treatments of different attempts of deceit using the email owned by one of the authors, within a time period of four years.
Lightweight Architecture for Real-Time Hand Pose Estimation with Deep Supervision

The high demand for computational resources severely hinders the deployment of deep learning applications in resource-limited devices. In this work, we investigate the under-studied but practically important network efficiency problem and present a new, lightweight architecture for hand pose estimation. Our architecture is essentially a deeply-supervised pruned network in which less important layers and branches are removed to achieve a higher real-time inference target on resource-constrained devices without much accuracy compromise. We further make deployment optimization to facilitate the parallel execution capability of central processing units (CPUs). We conduct experiments on NYU and ICVL datasets and develop a demo1 using the RealSense camera. Experimental results show our lightweight network achieves an average running time of 32 ms (31.3 FPS, the original is 22.7 FPS) before deployment optimization. Meanwhile, the model is only about half parameters size of the original one with 11.9 mm mean joint error. After the further optimization with OpenVINO, the optimized model can run at 56 FPS on CPUs in contrast to 44 FPS running on a graphics processing unit (GPU) (Tensorflow) and it can achieve the real-time goal.
Analisis Kekeringan Menggunakan Metode Theory of Run pada Sub-sub DAS Riam Kanan Kalimantan Selatan

The drought is a routine problem that occurs at several region in Indonesia. Due to the drought, water depletion in the Riam kanan watershed sub-district is very large. Several methods are used to find out and analyze the drought that occurs, one of them is the Theory of Run method. The Theory of Run method is a method to calculate the drought index in the form of the longest duration and the largest number of droughts with a certain return period in some area. The results showed that the longest duration of drought occurred in 2009, for 12 months with the largest number of cumulative droughts was 1125 mm. Based on the drought level distribution map, Aranio Subdistrict was the worst region of drought. In 1998-2002, 2003-2007, 2008-2012, and 2013-2017 the area of drought reached 88,918.62 ha, 90,873.6 ha, all of the Aranio Subdistrict regions , and 69,710.34 ha, respectively.
AN ANALYSIS OF PRONUNCIATION ERRORS OF ENGLISH CONSONANTS : /?/ and /ð/ BY THE STUDENTS OF THE ENGLISH EDUCATION STUDY PROGRAM OF UNIVERSITY OF BENGKULU.

This research was aimed to know and to analyze how English students in University of Bengkulu, English Education Study program class A and B, academic year 2015/2016 pronounced English consonants /?/ and /ð/. The instrument used for collecting the data was recording. This research used qualitative and quantitative method. The result of this research showed most of English students pronounced English consonants /?/ and /ð/ incorrectly. It showed that the students were not good at pronouncing English consonants /?/ and /ð/ when they were located at the middle or at the final of the words. Based on the findings, the study concluded that the errors were caused by several reasons such as the sound of /?/ and /ð/ which did not exist in Bahasa Indonesia, the influence of similar sound with Bahasa Inonesia consonants /t/ and /d/, and also the carelessness
The Effect of Evidence Transfer on Latent Feature Relevance for Clustering

Evidence transfer for clustering is a deep learning method that manipulates the latent representations of an autoencoder according to external categorical evidence with the effect of improving a clustering outcome. Evidence transfer’s application on clustering is designed to be robust when introduced with a low quality of evidence, while increasing the effectiveness of the clustering accuracy during relevant corresponding evidence. We interpret the effects of evidence transfer on the latent representation of an autoencoder by comparing our method to the information bottleneck method. Information bottleneck is an optimisation problem of finding the best tradeoff between maximising the mutual information of data representations and a task outcome while at the same time being effective in compressing the original data source. We posit that the evidence transfer method has essentially the same objective regarding the latent representations produced by an autoencoder. We verify our hypothesis using information theoretic metrics from feature selection in order to perform an empirical analysis over the information that is carried through the bottleneck of the latent space. We use the relevance metric to compare the overall mutual information between the latent representations and the ground truth labels before and after their incremental manipulation, as well as, to study the effects of evidence transfer regarding the significance of each latent feature.
Hospitalization Behavior Prediction Based on Attention and Time Adjustment Factors in Bidirectional LSTM

Predicting the future medical treatment behaviors of patients from historical health insurance data is an important research hotspot. The most important challenge of this issue is how to correctly model such temporal and high dimensional data to significantly improve the prediction performance. In this paper, we propose an Attention and Time adjustment factors based Bidirectional LSTM hospitalization behavior prediction model (ATB-LSTM). The model uses a hidden layer to preserve the impact state of medical visit sequences at different time on future prediction, and introduces the attention mechanism and the time adjustment factor to jointly determine the strength of the hidden state at different moments, which significantly improves the predictive performance of the model.
Incremental Multiple Hidden Layers Regularized Extreme Learning Machine Based on Forced Positive-Definite Cholesky Factorization

The theory and implementation of extreme learning machine (ELM) prove that it is a simple, efficient, and accurate machine learning method. Compared with other single hidden layer feedforward neural network algorithms, ELM is characterized by simpler parameter selection rules, faster convergence speed, and less human intervention. The multiple hidden layer regularized extreme learning machine (MRELM) inherits these advantages of ELM and has higher prediction accuracy. In the MRELM model, the number of hidden layers is randomly initiated and fixed, and there is no iterative tuning process. However, the optimal number of hidden layers is the key factor to determine the generalization ability of MRELM. Given this situation, it is obviously unreasonable to determine this number by trial and random initialization. In this paper, an incremental MRELM training algorithm (FC-IMRELM) based on forced positive-definite Cholesky factorization is put forward to solve the network structure design problem of MRELM. First, an MRELM-based prediction model with one hidden layer is constructed, and then a new hidden layer is added to the prediction model in each training step until the generalization performance of the prediction model reaches its peak value. Thus, the optimal network structure of the prediction model is determined. In the training procedure, forced positive-definite Cholesky factorization is used to calculate the output weights of MRELM, which avoids the calculation of the inverse matrix and Moore-Penrose generalized inverse of matrix involved in the training process of hidden layer parameters. Therefore, FC-IMRELM prediction model can effectively reduce the computational cost brought by the process of increasing the number of hidden layers. Experiments on classification and regression problems indicate that the algorithm can be effectively used to determine the optimal network structure of MRELM, and the prediction model training by the algorithm has excellent performance in prediction accuracy and computational cost.
Zeka - Friendy Chatterbot

The idea of chatbots firstly appeared in the 1960s. But only after more than half a century passed the world became ready for their implementation into the real life, this being a result of the rapid progress in natural language processing, artificial intelligence, and the global presence of text messaging applications. Today, specialized chatbots exist in different domains, thus helping organizations handle large amount of inquiries. Idea of this project was to develop one friendly chatbot with whom you can talk about politics, movies, weather, sport, emotions and similar everyday things. Friendly chatbot named Zeka, is a web-based chatbot developed with the help of Chatterbot library. Chatbot relies on different natural processing and machine learning algorithms altered by its developers to increase its performance.
Shaping the Concept of Mage Through Image Schemas in Dragon Age Origins

Language shapes the human mind and its concept toward things. Using image schemas, in nowadays technology, even AI (artificial intelligence) can concept things in response to their creator negativity or positivity. This is reflected in one of the most selling games around the world in 2012 called Dragon Age
Analisis Tingkat Resapan Air Menggunakan Sistem Informasi Geografis di Das Tabunio

The development of development that is so rapid causes more and more land to change into new buildings and settlements that have an impact on the reduction in the area of water catchment areas. Reduced water catchment areas will result in reduced groundwater reserves and will cause natural disasters such as floods. Based on this, efforts need to be made to determine the condition and distribution of water absorption in the Tabunio watershed area. In determining water absorption conditions there are four parameters, namely soil type, rainfall, land cover and slope. The data analysis method used is the overlay method with Geographic Information System (GIS) software. The results of data analysis showed that the study area with an area of 62,558.56 ha was divided into five conditions for water catchment areas. The water recharge conditions were very critical with an area of 32.38 ha, critical conditions covering 13,391.14, rather critical conditions covering an area of 35,769.47 ha, critical conditions ranging from 13,359.27 ha and natural normal conditions covering an area of 6.10 ha.
High Performance Implementation of Image Scaling Processor in FPGA Using Bilinear Interpolation

Image scaling is an imperative task in most of the machine vision applications like pattern recognition, pattern understanding.The Region of Interest (ROI) in the image must be resized before features are extracted.At different resolutions the region to be processed is described in order to evaluate the content present in the image under interest.In this paper, memory efficient and delay efficient image scaling algorithm is implemented..The proposed method consists of four modules namely sharpeningfilter, clamp filter, edge detector and bilinear interpolator.The bilinear interpolation algorithm is chosen as scale up or scale down algorithm due to its simplicity, high quality output and less operational cycles consumption in hardware compared other well-known methods like cubic interpolation, nearest neighbourhood interpolation.Using Verilog HDL, the RTL description of the algorithm is developed and the functional output is verified using Modelsim10.4a.Using Xilinx 9.2i IDE, the RTL implementation is verified.The target FPGA chosen is Spartan3 XC3S400PG208 board.For the input images of size 64*64, 8 bit gray scale image,the operating frequency of the design is found to be equal to 215.64Mhz.The area occupied for the proposed algorithm is less compared exiting implementations.
The Effect of Learning Motivation and Vocabulary Mastery towards Students’ Writing Skill in Argumentative Text at Private Senior High School in Kabupaten Serang

The purpose of this study was to determine the effect of learning motivation and vocabulary mastery of the argument text writing skills of students, the influence of learning motivation of the argumentative text writing skills of students, the influence of vocabulary on the argumentative text writing skills of students.This study used survey method. The subjects were all students of class XII in private senior high school in Kabupaten Serang. Data was collected by distributing a questionnaire and giving examination to writing. The data analysis was conducted using descriptive statistics, Pearson's correlation coefficient, coefficient of determination and regression analysis. Statistical test using the T test and F test study was conducted in November 2015 - January 2016.The results showed: (1) there is a significant effect of vocabulary mastery and learning motivation towards writing skills of argumentative English text. This is proven by the Sig = 0.018 &lt; 0.05 and 7.749 Fμ, (2) there is a significant influence on the vocabulary skills of argumentative English text. This is proven by the Sig value by 0.008 &lt; 0.05 and 3.953 tμ, (3) there is a significant influence on the learning motivation towards writing skills of argumentative English text. This is shown by the Sig value 0.004 &lt;0.05 and tμ 5.288.
PinchList

Intensive exploration and navigation of hierarchical lists on smartphones can be tedious and time-consuming as it often requires users to frequently switch between multiple views. To overcome this limitation, we present PinchList, a novel interaction design that leverages pinch gestures to support seamless exploration of multi-level list items in hierarchical views. With PinchList, sub-lists are accessed with a pinch-out gesture whereas a pinch-in gesture navigates back to the previous level. Additionally, pinch and flick gestures are used to navigate lists consisting of more than two levels. We conduct a user study to refine the design parameters of PinchList such as a suitable item size, and quantitatively evaluate the target acquisition performance using pinch-in/out gestures in both scrolling and non-scrolling conditions. In a second study, we compare the performance of PinchList in a hierarchal navigation task with two commonly used touch interfaces for list browsing: pagination and expand-and-collapse interfaces. The results reveal that PinchList is significantly faster than other two interfaces in accessing items located in hierarchical list views. Finally, we demonstrate that PinchList enables a host of novel applications in list-based interaction?