Reviewerly/exHarmony huggingface dataset:
Algoritma Naïve Bayes Dalam Memprediksi Kepuasan Nasabah

Customer satisfaction is very important in assessing the level of management and services provided by the bank. The purpose of this study is to predict customer satisfaction with the service quality of Bank BTN Pematangsiantar Branch in terms of Tangible, Reliability, Assurance, and Responsiveness. The sample of this study is the customer of Bank BTN Pematangsiantar Branch. Using the Naive Bayes algorithm, the author tries to predict customer satisfaction with the service quality of the bank. After manual calculations, verification is performed using RapidMiner software and a rule model is obtained. From the analysis process, it can be seen that the Naive Bayes algorithm can be implemented in predicting customer satisfaction with the service quality of the Bank. The testing carried out with RapidMiner software equipped with apply model and % Performance, and accuracy of 88% is obtained.
ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing

This paper presents the system used in our submission to the CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing.Our system is a graph-based parser which combines an extended pointergenerator network that generates nodes and a second-order mean field variational inference module that predicts edges.Our system achieved 1 st and 2 nd place for the DM and PSD frameworks respectively on the inframework ranks and achieved 3 rd place for the DM framework on the cross-framework ranks.
Face Super-Resolution via Discriminative-Attributes

The limited resolution of cameras and the wide field of the video surveillance systems lead to low quality captured facial images and difficult to identify. Face super-resolution methods are proposed to enhance the resolution of facial images. However, it remains a challenging issue to restore discriminative features to identify a specific person in surveillance videos. An algorithm that helps face super-resolution and recognition with the aid of discriminative-attributes is proposed in this paper. We introduce discriminative-attributes for face recognition to recover discriminative features in the reconstructed facial images. Attributes with more discriminative power are selected to input the network together with the low-resolution face image. The experimental results of the LFW-a benchmark test show that our method achieves promising results in both subjective visual quality and face recognition accuracy.
Solving write conflicts in GPU-accelerated graph computation: A PageRank case-study

Graph ranking algorithms, such as PageRank, are widely used in a number of real-world applications like web search. As the size of the graphs on which these algorithms are applied gets bigger and bigger, it becomes necessary to devise powerful and flexible techniques to accelerate and parallelize the computation, both at software and hardware level. Leveraging GPUs is a promising direction due to their highly parallel computing capabilities, but execution time is often hampered by write conflicts. In this paper, we present a solution to handle write conflicts in GPU computations exploiting high level of parallelism, and show how this technique can effectively be used to accelerate the computation of PageRank by a factor of 5x, with respect to a baseline in which conflicts are not handled. Our solution is implemented at software level, and doesn't require specific hardware resources.
Heavy Metal Contamination of Selected Foods from the Oil Producing State of Akwa Ibom in Southern Nigeria

Background and Objective: Heavy metals represent an important category of bio-toxic substances that are encountered under various environmental and occupational circumstances.The toxicity of these metals is in part due to their tendency to accumulate in biological tissues, a process known as bioaccumulation that can result from exposure to metals in foods of plant and animal origin.In this study, a survey was carried out to assess the awareness of residents of eight communities in the Akwa Ibom state in Nigeria about heavy metal contamination.Materials and Methods: Standard methods were used to analyse the amounts of the most dominant heavy metals in seven varieties of foods procured from selected farms in these communities as well as microbial contamination of these food samples.Questionnaires were administered for the survey studies.Data generated from the analysis were subjected to one-way analysis of variance and accepted at the 0.05 probability level.Results: All of the survey respondents were between 31 and 60 years-old and 55.1% were male.The awareness of heavy metal contamination, particularly of arsenic and lead (22.5 and 28.8%, respectively), was high.There was no fecal contamination detected in foods sampled from main urban areas.Lead, mercury and copper accumulation was most pronounced in food samples from communities located in riverine areas that are subjected to oil exploration and exploitation.There were also high amounts of heavy metal accumulation in samples from communities having a strong agriculture and commercial presence.The heavy metal content and microbial load of the various food samples varied significantly (p<0.05)among the eight communities.Conclusion: Microbial contamination of the foods surveyed was more common in agrarian and fishing communities than in urban areas.
Model Identifikasi Kata Ucapan Tuna Wicara

Speech impaired is the inability of someone to speak, even though speaking ability is important in order to communicate with other people. Dealing with this as someone who has speech impairments has their own way of communicating, namely by using sign language, but not everyone understands the sign language. The MFCC and Backpropagation ANN methods are implemented on a Single Board Computer (SBC) designed to overcome speech impaired communication problems. The MFCC method is used to retrieve the features of speech impairment and the Backpropagation ANN is used for sound pattern recognition.The system was trained using 750 sound samples consisting of 5 speakers, each uttering as many as 30 repetitions of the pronunciation of words (makan, kamar, kerja, harga and lapar), then tested using 125 sound samples consisting of 5 speakers, each saying 5 repetitions of words. Training and testing of Backpropagation ANN using input coefficients generated from MFCC. The results showed that the MFCC and Backpropagation ANN methods were able to identify speech words with 60% accuracy, 40% precision and 40% sensitivity.
A Preliminary Plains Cree Speech Synthesizer

This paper discusses the development and evaluation of a Speech Synthesizer for Plains Cree, an Algonquian language of North America. Synthesis is achieved using Simple4All and evaluation was performed using a modified Cluster Identification, Semantically Unpredictable Sentence, and a basic dichotomized judgment task. Resulting synthesis was not well received; however, observations regarding the process of speech synthesis evaluation in North American indigenous communities were made: chiefly, that tolerance for variation is often much lower in these communities than for majority languages. The evaluator did not recognize grammatically consistent but semantically nonsense strings as licit language. As a result, monosyllabic clusters and semantically unpredictable sentences proved not the most appropriate evaluate tools. Alternative evaluation methods are discussed.
Dense Matching for DSM Generation From ZY-3 Satellite Imagery

A novel method for dense matching for DSM generation is presented in this paper. First, a global energy function that contains matching cost and smoothness constraint is constructed, and then multiple candidates for disparity of each pixel are obtained by solving the global energy function, where multipath dynamic programming (MDP) is employed. Subsequently, a reliability measure is proposed, which evaluates the reliability of a given candidate and is calculated by Parzen estimation. The candidate with the highest reliability is selected as the final disparity for a given pixel. Moreover, postprocessing steps for removing outliers and the interpolation of gaps are presented to refine the disparity result. Consequently, forward intersection is employed to generate DSM with the disparity result. The complexity of this method is linear to the number of pixels and independent of the disparityrange, which is the reason for generating disparity result in a short time. The experiment conducted on ZY-3 imagery demonstrates the efficiency and accuracy of the proposed method.
Analisis dalam Melihat Perkembangan Indeks Harga Perdagangan Besar Menurut Sektor di Indonesia Menggunakan Algoritma Backpropagation

The Large Trade Price Index (IHPB) is one of the economic indicators that contains index numbers and shows changes in the price of goods purchased by traders from consumers. This study uses Artificial Neural Networks (ANN) with the Backpropagation method. Artificial neural networks are branches of artificial intelligence that mimic or imitate the workings of the human brain. The data of this study are secondary data sourced from the Central Statistics Agency (BPS) from 2000 to 2017. The data is divided into 2 parts, namely training data and testing data. There are 5 architectural models used in this study. 8-15-1, 8-25-1, 8-26-1, 8-30-1 and 8-40-1. From the 5 architectural models used 1 best model was obtained, namely 8-25-1 with an accuracy rate of 85%, MSE 0.00100074 and 10000 iterations. So this model is good for predicting large trade price indexes according to sectors in Indonesia in the future.
TEACHING SPEAKING THROUGH CHAIN STORYTELLING TO THE TENTH GRADE STUDENTS OF SMA SRIJAYA NEGARA PALEMBANG

The objectives of this study were to find out: (1) whether or not there was a significant improvement in speaking skill of students who were exposed to chain storytelling and (2) whether or not there was a significant difference in speaking skill between students who were exposed to chain storytelling and those who were not. The sample of this study was 60 tenth grade students of SMA Srijaya Negara, which were divided into control and experimental group, and each group had 30 students. To collect the data, each group was assigned pretest and post test. The data analyses used paired sample t test and independent sample t test in SPSS. The result from paired sample t test showed that the mean difference in post test and pretest of experimental group was 16.000 at the significance level of p&lt;0.05 and since t obtained was higher than t table (9.649&gt;2.04523), H01 was rejected and there was a significant improvement in speaking skill of experimental group. The highest contribution was given by story elaboration aspect (content), and then followed by pronunciation, vocabulary, fluency and grammar. The result from independent sample t test showed that the mean difference between post test of experimental group and control group was 2.4000 at the significance level of p&gt;0.05 with t obtained&lt;t table (0.942&lt;2.00171), since t obtained was lower than t table, H02 was accepted and there was no significant difference in speaking skill between students who were taught by using chain storytelling and those who were not. Nevertheless, the mean difference from the experimental group was higher than that of control group (16.600&gt;7.133). Keywords: teaching speaking, chain storytelling
Analisis Jaringan Syaraf Tiruan untuk Memprediksi Jumlah Narapidana pada Lembaga Pemasyarakatan Simalungun dengan Metode Backpropagation

The author has conducted research at the Simalungun District Prosecutor's Office and found the problem of prison rooms that did not match the number of prisoners which caused a lack of security and a lack of detention facilities and risked inmates to flee. Artificial Neural Network which is one of the artificial representations of the human brain that always tries to simulate the learning process of the human brain. The application uses the Backpropagation algorithm where the data entered is the number of prisoners. Then Artificial Neural Networks are formed by determining the number of units per layer. Once formed, training is carried out from the data that has been grouped. Experiments are carried out with a network architecture consisting of input units, hidden units, and output units. Testing using Matlab software. For now, the number of prisoners continues to increase. Predictions with the best accuracy use the 12-3-1 architecture with an accuracy rate of 75% and the lowest level of accuracy using 12-4-1 architecture with an accuracy rate of 25%.
KLASIFIKASI TIPE SEL NORMAL/ABNORMAL BERDASARKAN CITRA PAP-SMEAR MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

The classification of cell types plays an essential role in monitoring the growth of cancer cells. One of the methods to determine the cancer type is to analyze the pap-smear images manually. Nevertheless, the manual analysis of pap-smear images by the expert has several limitations, such as time-consuming and prone to misdiagnosis. For reducing the risks, it requires the automatic classification of cell types based on pap-smear images. This study utilizes the convolutional neural network (CNN) architectures to automatically classify the cell type into two-class categories (normal/abnormal) based on three features. These features, such as the local binary pattern, gray level co-occurrence matrix, and shape features, are extracted from pap-smear images. This study shows the performance of CNN achieved the maximum accuracy of 99.98%, 100.0%, 99.78% in training, validation, and testing data. Our approach also outperforms the performance of the baseline methods. Keywords : CNN, Classification, Cell, Neural Network, Pap-smear
A Fine-grained Conditional Proxy Broadcast ReEncryption Policy for File Sharing System

With the proliferation of electronic devices, cloud storage has become an integral part of synchronizing files between devices.However, data stored on the cloud is vulnerable to unauthorized access during the sharing process.The traditional conditional proxy broadcast re-encryption solution is suitable for the above application scenarios but does not support flexible control of conditions.This article describes the concept of finegrained conditional proxy broadcast re-encryption (FGC-BPRE).The scheme utilizes an access tree to generate a re-encryption key.If the ciphertext keyword satisfies the conditions in the access tree, the proxy can convert the user's ciphertext into a new ciphertext for a group of users.In addition, this paper also constructs a fine-grained conditional proxy broadcast re-encryption scheme and verifies the security of selective ciphertext attacks without oracles.
The Effect of Problem Based on Learning Models Using Macromedia Flash and Motivation Against Student Learning Outcomes on Environmental Pollution Materials

This study aims to determine: 1) whether the learning outcomes of students taught with problem-based learning models using macromedia flash are better than students taught with direct learning models, 2) whether the learning outcomes of students who have high motivation are better than students who have low motivation,3) is there an interaction between the two learning models with the level of motivation in influencing student learning outcomes. This research is a quasi experimental study. The population in this study were all grade VII students of MTs Lab. UINSU Medan Academic Year 2018/2019 which consists of three classes. Samples were selected by cluster random sampling of two classes. . The instrument used was a student learning motivation questionnaire and student learning outcomes test. Data obtained from the research instrument were then analyzed using two-way anova in the SPSS 14.0 for Windows program. The results showed that: Student learning outcomes taught by the problem-based learning model were better than students taught by the direct learning model (sig. = 0.027 &lt;0.05), Student learning outcomes that were highly motivated were better than students who were motivated low (sig. = 0,000 &lt;0.05), There is an interaction between the two learning models with the level of motivation in influencing student learning outcomes (sig. = 0.018 &lt;0.05).
KAJIAN ARSITEKTUR HIJAU DALAM PENGEMBANGAN DESAIN GEDUNG PEMERINTAHAN

&#x0D; &#x0D; &#x0D; Title: Green Architecture Study in The Development of Government Building Designs&#x0D; The development of constructing the building especially the government building develop rapidly and increasing the number of needs in each region or province in Indonesia. Issues concerning environmental problems resulting from the impact of the development of government buildings in Indonesia at this time such as environmental degradation, global warming, energy and water crises into the background of the emergence of the concept of green architecture or environmentally friendly architecture. The components of green architecture within the building and its environment relate to the site management, energy efficiency, water use efficiency, indoor air quality, environmentally friendly materials, waste management, wastewater management, and vegetation placement are necessary to be researched. The results of this study is expected to be a reference and design guidelines on the development of government buildings in Indonesia to become green buildings that are environmentally friendly.&#x0D; &#x0D; &#x0D;
Implementasi Support Vector Regression pada Prediksi Inflasi Indeks Harga Konsumen

Inflation reflects an increase in the prices of these items as well as those used by the Indonesian government, especially Bank Indonesia, in determining monetary policy. An indicator that can be obtained by Bank Indonesia in measuring inflation is the Consumer Price Index. This study discusses inflation prediction using the SVR method. Inflation test data issued by Bank Indonesia. As a comparison material for the kernel used in the SVR method using two kernels, namely Linear and Radial Base Function. The error rate evaluation results show that linear kernels produce better values, with a MAPE rate of 8.70% and MSE of 0.0037
IMPROVING LISTENING ABILITY BY ACTIVATING STUDENTS’ BEFORE KNOWLEDGE

This research paper aims at finding whether the students’ ability in listening can be improved by activating students’ previous knowledge. The research was conducted at English education study program of faculty of IKIP Siliwangi Bandung. The research used random sampling class and test by giving pretest and post test. Two classes are experimental class and control class, when the experimental class got a better score than control class.The experimental class was increased in listening ability. Most of the student’s of experimental class students’ stay in fair category.
A Low-Rank Minimization Method for Depth Map Denoising

This paper proposed a novel low-rank minimization method for depth map denoising. Firstly, the color image is used to help searching for similar patches of each patch in a noisy depth map. Then we assemble them into a data matrix. Because the similar patches usually lie in a very low rank subspace, the low rank minimization model is exploited to solve the depth map denoising problem. However the original convex constraints, such as nuclear norm constraint, may be suboptimal to the low rank. To improve the recovery performance, the weighted nuclear norm constraint is used in the low-rank minimization as a nonconvex regularization. By using iterative regularization technique and a generalized soft thresholding operator, low rank minimization with weighted nuclear norm regularization can be efficiently solved. The denoised data matrix can be obtained after several iterations. A patch may be covered by multiple data matrices. We average them as final depth values. Our method is compared with the other competing methods by experiments to validate its effectiveness.
ANALYSIS OF STUDENTS’ COGNITIVE AND SOCIAL READING STRATEGIES IN READING COMPREHENSION AT THE THIRD SEMESTER OF THE ECONOMIC AND BUSINESS DEPARTMENT OF UPI YPTK PADANG

This study examined students’ cognitive and social reading strategies in Economic and Business Department of UPI YPTK Padang. A total of 156 students in three classes were surveyed regarding their reading strategies. The method used in this research was descriptive correlation method. 15 students were taken as samples by using random sampling technique. The results found that there were high statistically significant correlation among the three classes. These findings were discussed in regards to whether correlation in reading strategies could potentially influence learning outcomes and effectiveness of teaching reading.
Mixing Property Tester: A General Framework for Evaluating the Mixing Properties of Initialization of Stream Ciphers

In this paper, a general framework for evaluating the mixing properties of initialization of stream ciphers, called Mixing Property Tester-MPT, is exploited and formalized. Based on this general framework, we propose a concrete and efficient algorithm, which can compute the maximum number of initialization rounds of a given stream cipher such that any internal state bit or generated keystream bit does not achieve full mixing properties. Our algorithm has linear time complexity and needs a negligible amount of memory. As illustrations, we apply our algorithm to ZUC-128, ZUC-256 and Trivium stream ciphers. The results show that though ZUC-256 has a much larger initial input size than ZUC-128, its mixing properties are almost as good as ZUC-128. As for Trivium, the tap positions of keystream output function are not chosen optimally with respect to this tester and we provide some better selections of tap positions. As a general cryptanalytic tool, MPT can help to give the designers more insights to choose the initialization functions and the required number of initialization rounds.
Personality-dependent Neural Text Summarization

In Natural Language Generation (NLG) systems, personalization strategies -i.e., the use of information about a target author to generate text that (more) closely resembles human-produced language -have long been applied to improve results.The present work addresses one such strategy -namely, the use of Big Five personality information about the target author -applied to the case of abstractive text summarization using neural sequence-tosequence models.Initial results suggest that having access to personality information does lead to more accurate (or humanlike) text summaries, and paves the way for more robust systems of this kind.
THE NARRATOR’S VOICE OF FREEDOM IN RALPH ELLISON’S INVISIBLE MAN: A STUDY OF POSTCOLONIAL LITERATURE

&lt;p&gt;The purpose of this research is to&lt;strong&gt; &lt;/strong&gt;describe&lt;strong&gt; &lt;/strong&gt;the position of Black people as the folk who get discrimination in America and explain the Narrator’s voice as the Black people in conveying the equality between Black and White people in America that is represented in&lt;em&gt; Invisible Man&lt;/em&gt; novel. This research uses qualitative research. The researcher needs postcolonial literature by Lois Tyson to get evidences the effects and goals of the author in creating a story. The analysis reveals that Black people include subordinate people, oppressed minority group, and lower class that always suppressed and exploited by superior people who have power. Black people try to fight against superior to show their feeling of freedom for getting the same position and equality as White by conveying their voice through protest. The conclusion shows that inferiority makes Black people who have lower position in society get oppression that cause physical and mental disturbance from superior and the Narrator tries to struggle and get confession of Black people from the domination of White in order to survive their existence in society by conveying the voice with non-violence way through speech, action and music.&lt;/p&gt;