Reviewerly/exHarmony huggingface dataset:
GSA-Net: gated scaled dot-product attention based neural network for reading comprehension

Reading Comprehension (RC) is concerned with building systems that automatically answer questions about a given context passage. The interactions between the context and question are very important to locate the correct answer. In this paper, we propose a Gated Scaled Dot-Product Attention based model for RC task. The character-level embedding is incorporated into the word embedding which is helpful to deal with Out-of-Vocabulary (OOV) tokens. The attention distribution is obtained by scaled dot product which captures the interaction between question and passage effectively. Further, self-matching attention mechanism is adopted to resolve the problem of long-distance dependency. These components provides more information for the prediction of the starting and ending position of the answer. We evaluate our method on Stanford Question Answering Dataset (SQuAD) and the results show that different components in the model boost the performance.
Semi-Supervised Breast Histological Image Classification by Node-Attention Graph Transfer Network

As a major cause of leading female death, breast cancer is often diagnosed by histological images which has been resolved by many deep learning methods with the assistance of large amounts of annotated data. However, their performances are severely limited by the lack of sufficient labeled data in clinical practice. This paper aims to relieve the annotating workload by a semi-supervised transfer learning algorithm to conduct knowledge distillation from a completely labeled source domain. To achieve this goal, we propose a node-attention graph transfer network to exploit the inherent correlation between individual samples by graph convolutional network, along with a cross-domain graph learning module to stimulate the graph construction in target domain. In the meanwhile, we design a node-attention mechanism to learn the individual contribution of each source image for target domain, which can further leverage the domain-gap by our node-attention transfer learning. Results of semi-supervised breast histological image classification with various scales of annotated training images are performable and further experiments demonstrate the significant contributions of each component we proposed.
Why Communicate in Disguises: A Study on Ownership of Fake Identity Accounts in Instagram

The selection of Instagram as a means of communication through social media in Indonesia, currently occupies the fourth position in the world.Unfortunately, the increase of using this technology is also accompanied by many problems such as identity/profile forgery, online impersonation and other cybercrimes.Why did this happen?This research aims to find motivations for ownership of fake identity accounts on social media Instagram in Indonesia.Using the case study method with a qualitative approach, extracting data through interviews with account owners identified using false or disguised identities.The results of the study found the motivation of ownership of fake identity accounts on Instagram social media, namely: interest in using Instagram because it is easy to invite actions and reactions, inform current events, make virals, help people get real information, get financial and social benefits.
An Antiforensic Method against AMR Compression Detection

Adaptive multirate (AMR) compression audio has been exploited as an effective forensic evidence to justify audio authenticity. Little consideration has been given, however, to antiforensic techniques capable of fooling AMR compression forensic algorithms. In this paper, we present an antiforensic method based on generative adversarial network (GAN) to attack AMR compression detectors. The GAN framework is utilized to modify double AMR compressed audio to have the underlying statistics of single compressed one. Three state-of-the-art detectors of AMR compression are selected as the targets to be attacked. The experimental results demonstrate that the proposed method is capable of removing the forensically detectable artifacts of AMR compression under various ratios with an average successful attack rate about 94.75%, which means the modified audios generated by our well-trained generator can treat the forensic detector effectively. Moreover, we show that the perceptual quality of the generated AMR audio is well preserved.
Copper Price Prediction using Wave Count with Contribution of Elliott Waves

Within the last few decades, copper has been identified as one of the most applicable metals by many researchers. These researchers have also been enthusiastic to predict the price of this valuable metal. These days, the available technical analysis methods have been highly applied in the financial markets. Moreover, the researchers have used these methods to predict the suitable price trends. In the present work, some technical analysis tools including the Fibonacci series, Elliott waves, and Ichimuko clouds were practiced to scrutinize the price changes and predict the copper price. All copper prices from 2008 to 2016 were considered. Regarding the fractal property of these methods, the relations among prices were obtained within an eight-year time sequence. Since 2016, the copper price has been gradually deviated from its previous trend. Using the wave count and Elliott waves has specified that the wave number 1 and wave number 2 have been completed. Now, the time has come to develop the wave number 3. According to the relations introduced by the Elliott waves and the clouds made by Ichimiku, it was determined that the copper price would be almost $16000 per ton in 2022.
Lemmatization and morphological analysis for the Latin Dependency Treebank

The present article presents some challenges posed by lemmatization and PoS tagging of Latin, with reference to the ongoing work to revise the Latin Dependency Treebank. Current options available for lemmatization and morphological analysis of Latin are reviewed and discussed. The pipeline to annotate the morphological layer of the Latin Dependency Treebank is shown to consist of three main steps: (i) tokenization/sentence split, which is performed via a documented rule-based algorithm, (ii) pre-population by means of COMBO, a state-of-the-art joint lemmatizer, PoS tagger, and parser trained on the data of the Latin Dependency Treebank 2.1, and (iii) manual error correction informed by the attempt to identify and document lemmatization and morphology annotation rules.
Improving Students’ Vocabulary Through Words Games at Grade VI of SDS Setia Padang

The problem of this research is based on the phenomena of students at VI class of SDS Setia Padang.Based on the observation, the students' still have lack of vocabulary.There are various teaching methods have been offered by the experts, one of them is through of word games.Word games mostly used as it can motivate your learners English.Therefore, this paper explore how word games can be used to improve students' vocabulary mastery at elementary school level.This study applied classroom action research by using two cycles to find how word games increase the vocabulary mastery of the English language learner at elementary school.The results of pre-cycle show that the score of leaners' vocabulary mastery is 59 before the treatment.After implementing word game in teaching, the score of the students' is increased into 68.6.Then, the score in average is increased into 81.7 in the second cycle.Based on these data, the average scores of students are getting improved after they were taught by using word games.Based on the scores obtained, it indicates that the achievement of the students' vocabulary mastery is getting better.It can be concluded that word games can be used to improve the vocabulary mastery.Moreover, word games is also able to attract students' interest and motivation.Thus, by using Word Games as a medium for learning English for sixth grade can also foster confidence.
New fuzzy local contrast measures: definitions, evaluation and comparison

In this contribution the concept of a local contrast of a fuzzy relation with the use of a consensus measure is introduced. A construction method of such local contrast using aggregation functions and fuzzy implications is considered. Other construction methods using similarity measure are also pointed out. Several examples of local contrasts are provided. The usability of introduced local contrast measures is evaluated by applying them in image processing for salient region detection.
Self-Processing Private Sensor Data via Garbled Encryption

Abstract We introduce garbled encryption , a relaxation of secret-key multi-input functional encryption (MiFE) where a function key can be used to jointly compute upon only a particular subset of all possible tuples of ciphertexts. We construct garbled encryption for general functionalities based on one-way functions. We show that garbled encryption can be used to build a self-processing private sensor data system where after a one-time trusted setup phase, sensors deployed in the field can periodically broadcast encrypted readings of private data that can be computed upon by anyone holding function keys to learn processed output, without any interaction. Such a system can be used to periodically check, e.g., whether a cluster of servers are in an “alarm” state. We implement our garbled encryption scheme and find that it performs quite well, with function evaluations in the microseconds. The performance of our scheme was tested on a standard commodity laptop.
O2O Iterative Project-based Teaching Model for Literature Course

As a main course of Chinese language and literature major in colleges, Literature Course is one of required courses for students majoring in journalism, hosting and broadcasting, which can cultivate students’ favorable humanistic quality and promote their specialty development. However, some problems exist in traditional teaching process, such as low interest in linguistics, insufficient interaction between teachers and students, and single evaluation system. Starting from Merrill’s chief teaching principle and aiming at teaching deficiencies of Literature Course, this study innovatively introduced O2O iterative project-based teaching model to establish an iterative teaching model based on online and offline integration. In combination of the question-based teaching method, four teaching steps including “construction, guidance, tutoring and introspection” were designed under the cooperation of O2O space environment and collaborative mechanism, and a brand-new implementation framework for linguistics teaching was tentatively constructed. To test the effect of this teaching mode, classification and regression tree (CART) algorithm of data mining technology was applied to compare and evaluate teachers’ teaching ability before and after the implementation of this teaching mode. It is found that, this teaching reform contributes to cultivating students’ critical thinking and innovation ability, and enhancing their academic research spirit and ability of language theory application.
The Effect of Constitutional Mantra Meditation on Mindful Attention Awareness Scale

Since the advent of transpersonal psychology, studies have been actively conducted on the verification of the effectiveness of meditation, a long-standing self-discipline system in the East.The purpose of this study is to examine the effect of mantra meditation on mindful attention and awareness, and whether there are differences in mindful attention awareness according to the distinction of Ayurvedic constitutions.This study was conducted on 40 office workers in D city recruited by random sampling.This study used the Korean version of the Mindful Attention Awareness Scale(K-MAAS), which consists of a single factor in mindfulness: attention and awareness.The results of this study are as follows: first, through the mantra meditation, the degree of mindful attention awareness increased by an average of 8.67, and; second, the effects of mindful attention awareness through constitutional mantra meditation increased in the order of Pitta(11.00),Vata(7.83), and Kapha(7.34).Since this study targets a small number of people in a specific city, it seems to need to study a variety of groups across the country and different age groups.The subsequent study could conduct a study in detail on how the sub-factors of mindfulness influenced by constitutional mantra meditation are different.
Feature Expansion using Lexical Ontology for Opinion Type Detection in Tourism Reviews Domain

Tourism reviews platform such as Trip Advisor become a major source for tourists to share their experiences and get some ideas for decision making.Since there are millions of reviews generated daily in the travel websites, tourist is often overwhelmed with huge information.This is where opinion type detection is important as it makes it easy for a tourist to obtain useful reviews for their understanding and planning processes based on the reviews' opinion type.The opinion type of texts in travel mostly involves different aspects of opinion related to the travel process, such as transportation, accommodation, price, food, entertainment, and so on.The challenge of this research is to improve this detection by proposing the lexical ontology approach to address the issue of out-of-vocabulary (OOV) keywords during a supervised detection of opinion type.Besides, there are also issues where the training data for detection has poor coverage or limited in a certain domain.In this paper, we propose a review opinion type detection approach by integrating the word (feature) expansion approach in machine learning.The suggested approach consists of two stages namely feature expansion and classification.For feature expansion, Lexical Ontology (LO) is used to expand the feature-related word to the domains such as synonyms.For classification, the expanded feature is corporate to the Machine Learning approach to detect the opinion type.
ANALYSIS OF STUDENT TUITION FEE PAY DELAY PREDICTION USING NAIVE BAYES ALGORITHM WITH PARTICLE SWARM OPTIMATION OPTIMAZATION (CASE STUDY : POLITEKNIK TEDC BANDUNG)

One source of funds that plays a very important role in education or teaching and learning activities is the Donation of Education Development or tuition fee. The problem faced by TEDC Polytechnic of Bandung is based on historical data from the financial department there are still many students who are late in making tuition payments, for that the authors make an analysis in predicting late payment of fees by using the Naive Bayes algorithm which is compared with Particle Swarm Optimization (PSO) with the purpose of determining the classification pattern is right or late and to find out what indicators most influence the prediction of late payment of tuition fee. In testing the author uses 115 datasets from the results of a questionnaire filled out by students and these testing these divided into three models, the first is testing using the entire dataset and all attributes of the questionnaire results, the second test is done by using the entire dataset based on Particle Swarm Optimation (PSO), as well as the third test carried out using a dataset using the attributes that most influence to the prediction of late tuition payment. Off the 13 indicators, there are only 8 indicators that are most influential in predicting late payment of tuition fee, namely father's income, number of dependents of parents, pocket money / month, financial services, academic services, study programs, payment methods for tuition payment, and mother's occupation. The testing result of those three classification models using the highest Naive Bayes accuracy algorithm are testing using the Naive Bayes algorithm based on Particle Swarm Optimization (PSO), with an accuracy of 73.94%, precision 78.50%, 69% recall, and AUC 0.771 , even though the execution time is 3 seconds longer.
Efficient Computation of 2-Covers of a String.

Quasiperiodicity is a generalization of periodicity that has been researched for almost 30 years. The notion of cover is the classic variant of quasiperiodicity. A cover of a text T is a string whose occurrences in T cover all positions of T. There are several algorithms computing covers of a text in linear time. In this paper we consider a natural extension of cover. For a text T, we call a pair of strings a 2-cover if they have the same length and their occurrences cover the text T. We give an algorithm that computes all 2-covers of a string of length n in 𝒪(n log n log log n + output) expected time or 𝒪(n log n log² log n / log log log n + output) worst-case time, where output is the size of output.If (X,Y) is a 2-cover of T, then either X is a prefix and Y is a suffix of T, in which case we call (X,Y) a ps-cover, or one of X, Y is a border (that is, both a prefix and a suffix) of T, and then we call (X,Y) a b-cover. A string of length n has up to n ps-covers; we show an algorithm that computes all of them in 𝒪(n log log n) expected time or 𝒪(n log² log n / log log log n) worst-case time. A string of length n can have Θ(n²) non-trivial b-covers; our algorithm can report one b-cover per length (if it exists) or all shortest b-covers in 𝒪(n log n log log n) expected time or 𝒪(n log n log² log n / log log log n) worst-case time. All our algorithms use linear space.The problem in scope can be generalized to λ > 2 equal-length strings, resulting in the notion of λ-cover. Cole et al. (2005) showed that the λ-cover problem is NP-complete. Our algorithms generalize to λ-covers, with (the first component of) the algorithm’s complexity multiplied by n^{λ-2}.
Penyusunan Model Pengamanan Aset Tidak Bergerak di Kabupaten Sumenep

The problem of management asset is one of many problems faced by local government whose material asset. The research purpose is to create security model on immovable assets in Sumenep. This research uses qualitative method with soft system methodology approach. The results showed that Sumenep district government in implementing the immovable asset security is not optimal because there is no standard reference, so immovable assets security model is needed. Administrative security model starting from demand the land assets report from the entire agencies accompanied by supporting documents. Furthermore, the report synchronized and verified directly to the assets location. If there are assets that administratively incomplete, than it must performed by legal security that starts from tracking the land assets status, if uncertified assets are found than certification is needed. Certified land assets then followed up through the physical security of land assets that used by the other party or misused by parties who violate the provisions of the land use agreement by fencing/installing a sign of ownership
An Analysis of the Students’ Writing Skill in Hortatory Exposition Text at the First Semester of Public Administration Study Program of the Faculty of Social and Political Science

This research aimed to describe the students’ difficulties in writing Hortatory Exposition Text and describe the students’ scores in writing Hortatory Exposition Text. The research was descriptive qualitative research. It was conducted at the first semester of Public Administration Study Program of the faculty of Social and Political Science Universitas B. The population of this research was 40 students of the first semester of Public Administration Study Program of the Faculty of Social and Political Science Universitas B. Simple random sampling was applied to select the sample. The researcher took 3 participants consisting of one low, one medium, and one high student based on English language proficiency level. In collecting the data, the researcher used test, observation, and interview. In analyzing the data, the researcher used a flow model. The result of the research showed that the low-level student (PE) got the lowest score, and the high-level student (JN) got the highest score. In addition, the aspects of writing the students had the most difficulties in were organization, unity, coherence, sentences, and paragraphs.
Canonical Correlation Discriminative Learning for Domain Adaptation

Domain adaptation aims to diminish the discrepancy between the source and target domains and enhance the classification ability for the target samples by using well-labeled source domain data. However, most existing methods concentrate on learning domain invariant features for cross-domain tasks, but ignore the correlation and discriminative information between different domains. If the learned features from the source and target domains are not correlated, the adaptability of domain adaptation methods will be greatly degraded. To make up for this deficiency, we propose a novel domain adaptation approach, referred to as Canonical Correlation Discriminative Learning (CCDL) for domain adaptation. By introducing a novel correlation representation, CCDL maximizes the correlations of the learned features from the two domains as much as possible. Specifically, CCDL learns a latent feature representation to reduce the difference by jointly adapting the marginal and conditional distributions between the source and target domains, and simultaneously maximizes the inter-class distance and minimizes the intra-class scatter. The experiments certify that CCDL is superior to several state-of-the-art methods on four visual benchmark databases.
Algoritma K-Means untuk Pengelompokan Topik Skripsi Mahasiswa

In helping to develop technology in the field of education as well as bringing about a major change in competitiveness between individuals and groups, to be able to do so requires sufficient information and data to be analyzed further. In this case STT Wastukancana Purwakarta is under the auspices of Bunga Bangsa Foundation, seeing that STT Wastukancana Purwakarta students have several obstacles in their final project, one of which is difficult in determining the topic of the thesis title to be made so that sometimes the topic of the thesis title taken is not in accordance with their abilities each student. This problem can be overcome by applying the clustering method. The analytical method used is Knowledge Discovery in Database (KDD). The method of grouping students uses the clustering method and the K-Means algorithm as a clustering calculation where the Clustering aims to divide students into clusters based on grades obtained from semester 1 to 7, so as to produce student recommendations in taking thesis topics. The tool used to implement the algorithm is Rapidminer. The results of this study are grouping students according to their expertise, which is obtained based on the cluster that has the highest score and is dominated by the most subjects according to the subjects that have been grouped by each expertise. So, the results of this cluster are used as a reference for students to take the thesis title topic.
Using Raindrops Removal Algorithm to Improve Vehicle Recognition via AttentiveGAN

When the vehicle image with raindrops is converted into a clear image, the biggest difficulty is that the area blocked by the raindrops is random, and the entire image loses many feature points. Therefore, the method of removing raindrops from a single image via Deep-Learning can effectively extract and mine the depth features in the image. We propose to use AttentiveGAN to remove raindrops from images, which is based on Generative Adversarial Networks (GAN). Using Generator and Discriminator in the GAN, raindrops in the vehicle images are better removed, thereby effectively improving the recognition rate of vehicles. From the final experimental evaluation, the images generated via this algorithm have greatly improved the recognition appearance, PSNR and SSIM, and the recognition rate of the vehicles can be increased by up to 30%.
On the Current Situation: Normal Violences, Pandemics, Emergencies, Necropolitics, Zombies, and Creepy Treehouses?

This paper argues that the university is changing in the pandemic but it is the broader contexts of the university and specifically neoliberal politics that originated the change.  It then identified several situations that upon witnessing undermine the neoliberal systems of legitimation.