Reviewerly/exHarmony huggingface dataset:
Tree-Based Contrast Subspace Mining for Categorical Data

Mining contrast subspace has emerged to find subspaces where a particular queried object is most similar to the target class against the non-target class in a two-class data set.It is important to discover those subspaces, which are known as contrast subspaces, in many real-life applications.Tree-Based Contrast Subspace Miner (TB-CSMiner) method has been recently introduced to mine contrast subspaces of queried objects specifically for numerical data set.This method employs tree-based scoring function to estimate the likelihood contrast score of subspaces with respect to the given queried object.However, it limits the use of TB-CSMiner on categorical values that are frequently encountered in real-world data sets.In this paper, the TB-CSMiner method is extended by formulating the tree-based likelihood contrast scoring function for mining contrast subspace in categorical data set.The extended method uses features values of queried object to gather target samples having similar characteristics into the same group and separate non-target samples having different characteristics from this queried object in different group.Given a contrast subspace of the target samples, the queried object should fall in a group having target samples more than the non-target samples.Several experiments have been conducted on eight real world categorical data sets to evaluate the effectiveness of the proposed extended TB-CSMiner method by performing classification tasks in a two-class classification problem with categorical input variables.The obtained results demonstrated that the extended method can improve the performance accuracy of most classification tasks.Thus, the proposed extended tree-based method is also shown to have the ability to discover contrast subspaces of the given queried object in categorical data.
Multi-Layer Perceptron Training by Genetic Algorithms

In this paper, the authors are presenting one of the ways to train Artificial Neural Networks (ANN) using a predefined set of weights as biases as input parameters, generated by the Genetic Algorithm (GA).This approach solves the problem of hyperparameter tuning for ANN, which is an NP-hard space search problem and will be further explained in the paper.The genetic algorithm generates a population of potential solutions in each iteration and then after a series of solutions variables modification (crossover, mutation, etc.) ranks them based on their fitness values.The algorithm itself is tested on a standard Multi-layer Perceptron (MLP) artificial neural network and results are similar compared to other techniques of training.
The Use of Orthography to Increase Young Learners’ Basic Literacy: Spelling Ability

The mastery of literacy skills of young learners is essential and fundamental which include reading and writing. Literacy skills are closely related to cognitive abilities that provide knowledge, information, and meaning. Spelling ability is one of the essential skills in reading that must be considered. This study aims to examine the use of orthography to improve the ability to spell in reading. This study was conducted with a qualitative approach through a systematical review method with meta-synthesis as the analysis tool. Data obtained from 4 previous research articles with criteria Q1 Scopus index. The results showed that orthography could improve young learners’ spelling ability.Keywords: orthography, literacy, spelling, reading
Implicit pairs for boosting unpaired image-to-image translation

In image-to-image translation the goal is to learn a mapping from one image domain to another. In the case of supervised approaches the mapping is learned from paired samples. However, collecting large sets of image pairs is often either prohibitively expensive or not possible. As a result, in recent years more attention has been given to techniques that learn the mapping from unpaired sets. In our work, we show that injecting implicit pairs into unpaired sets strengthens the mapping between the two domains, improves the compatibility of their distributions, and leads to performance boosting of unsupervised techniques by up to 12% across several measurements. The competence of the implicit pairs is further displayed with the use of pseudo-pairs, i.e., paired samples which only approximate a real pair. We demonstrate the effect of the approximated implicit samples on image-to-image translation problems, where such pseudo-pairs may be synthesized in one direction, but not in the other. We further show that pseudo-pairs are significantly more effective as implicit pairs in an unpaired setting, than directly using them explicitly in a paired setting.
Discussion on the Theoretical Guidance and Practical Exploration of the Integrated Development of Aesthetic Education and Moral Education in Colleges and Universities

Innovative integration is the development trend of education reform in recent years. Teachers can try to promote the high integration of aesthetic education and moral education from the establishment of music studios, and form a “three-in-one” education and education integration mechanism of classroom teaching, extracurricular activities, and campus culture construction, so as to inherit and innovate the outstanding Chinese traditional music culture and red music culture, and explore the necessity and feasibility of the complementary and integrated development between aesthetic education and moral education.Under the background that the country attaches great importance to inheriting and developing the excellent Chinese traditional culture, taking the integration of music education into college students’ moral education as the starting point, exploring the effective ways of vocal music, instrumental music, opera, Chinese folk art, dance, rap and other artistic works to cultivate national builders and successors who have comprehensive development of moral, intellectual, physical, aesthetic, and labor will be definitely to improve the humanistic quality of university and college students, which promotes the Chinese excellent traditional music culture and red music culture, trains college music talents, etc., and promotes the integration and development of large, medium and small aesthetic education and moral education.
ISSUES OF E-COLLABORATION AND TELECOMMUTING

This paper examines the benefits and issues in using information technologies, to support collaboration of teams in a virtual environment, the emerging methods and technologies and socio-technical issues associated with collaboration and teams in virtual environments. With the globalization of the economy, more and more employees are working with team members half way around the world. In order to reduce the negative effects, developers and users of e-collaboration tools for virtual environments should address human interaction issues, as well as social issues and organizational issues.
Predictive topology refinements in distributed stream processing system

Cloud computing has evolved the big data technologies to a consolidated paradigm with SPaaS (Streaming processing-as-a-service). With a number of enterprises offering cloud-based solutions to end-users and other small enterprises, there has been a boom in the volume of data, creating interest of both industry and academia in big data analytics, streaming applications, and social networking applications. With the companies shifting to cloud-based solutions as a service paradigm, the competition grows in the market. Good quality of service (QoS) is a must for the enterprises, as they strive to survive in a competitive environment. However, achieving reasonable QoS goals to meet SLA agreement cost-effectively is challenging due to variation in workload over time. This problem can be solved if the system has the ability to predict the workload for the near future. In this paper, we present a novel topology-refining scheme based on a workload prediction mechanism. Predictions are made through a model based on a combination of SVR, autoregressive, and moving average model with a feedback mechanism. Our streaming system is designed to increase the overall performance by making the topology refining robust to the incoming workload on the fly, while still being able to achieve QoS goals of SLA constraints. Apache Flink distributed processing engine is used as a testbed in the paper. The result shows that the prediction scheme works well for both workloads, i.e., synthetic as well as real traces of data.
Identification of Student Academic Performance in Computer Science Based on Naive Bayes

Jurusan Teknik Informatika is one of the study programs at UIN Maulana Malik Ibrahim. Based on the current curriculum in Jurusan Teknik Informatika, the curriculum refers to the IEEE/ACM Computer Science Curricula 2013. The IEEE/ACM Computer Science Curricula 2013 has a knowledge area classification, which is mentioned in the curriculum as having 18 knowledge areas. The curriculum used in the current technical study program is formulated and determined from the entire content or collection of knowledge in the IEEE/ACM Computer Science Curricula 2013. In the Jurusan Teknik Informatika curriculum at UIN Malik Ibrahim Malang currently there are 76 subjects, 58 of which are Teknik Informatika subjects and 18 others are general subjects. To identify the academic performance of students it is necessary to classify the curriculum in the Department of Informatics Engineering to the knowledge area in the IEEE / ACM Computer Science Curricula 2013. Classification is done using the Naïve Bayes method by calculating the probability of each course of the knowledge area, after it is done classification, data will appear in the form of subject distribution to the knowledge area. After classification, it is necessary to determine the level of contribution of each course that has spread to the knowledge area. This contribution level is entered into the Joint formula with the value of the student transcript to calculate the student's academic performance. Testing is done by comparing the output in the form of knowledge area with the highest performance produced by the program with input in the form of knowledge area from the expert for each student. This research resulted in an accuracy of 78.95% from the results of twenty times experiment.
Quantum Technologies Are Speeding to Commercialization

Abstract We are now commencing the second quantum revolution when engineering is needed to develop future quantum systems! With multiple 2019 announcements related to quantum computing, the path to a universal quantum computer has become a bit easier. But the question is still the same: when will universal quantum computers be available?
Cell Block: A Tool to Improve Cytopathologic Diagnostic Value of Fine Needle Aspiration Material

Background: The various advantages of Fine-needle aspiration (FNA) biopsy/ cytology can be limited due to inadequacy of the specimen and expertise required for diagnosis. Cell blocks (CBs), though routinely used in cytology of body fluids, can also be used in FNA material with considerable increase in diagnostic accuracy. Aims &amp; objectives: To compared FNA &amp; CB as diagnostic tool and to evaluate whether cell block adds to the diagnostic accuracy of FNA.&#x0D; Methods: This is a two years prospective study carried out in the pathology department of a tertiary heath care hospital and medical college of South India which included the cases undergoing the FNAC, and followed by the biopsy. CB was prepared using Tissue coagulum clot (TCC) method. The smears and tissue sections were assessed for cellularity and adequacy for diagnosis. CBs were grouped into four diagnostic categories. FNA &amp; CB were compared with histopathology (HPE) diagnosis to calculate Sensitivity, Specificity and Diagnostic Accuracy.&#x0D; Result: A total of 195 cases were included. Cellularity and adequacy for diagnosis were higher in CB. They were diagnostically better (superior) in 13.84% (27) cases. The sensitivity (95.77%) and diagnostic accuracy (94.87%) of CB was found to be considerably higher than FNA (78.84% and 78.46% respectively).&#x0D; Conclusion: In cases of suspicious/ intermediate diagnosis or diagnosis discordant with clinical ones, by FNA; should be followed with the CB to improve the diagnostic yield &amp; to facilitate accurate diagnoses. TCC method is a simple CB method which has higher diagnostic accuracy than FNA.
Machine-Learning-Augmented Analysis of Textual Data: Application in Transit Disruption Management

Despite rapid advances in automated text processing, many related tasks in transit and other transportation agencies are still performed manually. For example, incident management reports are often manually processed and subsequently stored in a standardized format for later use. The information contained in such reports can be valuable for many reasons: identification of issues with response actions, underlying causes of each incident, impacts on the system, etc. In this article, we develop a comprehensive, pragmatic automated framework for analyzing rail incident reports to support a wide range of applications and functions, depending on the constraints of the available data. The objectives are twofold: a) extract information that is required in the standard report forms (automation), and b) extract other useful content and insights from the unstructured text in the original report that would have otherwise been lost/ignored (knowledge discovery). The approach is demonstrated through a case study involving analysis of 23,728 records of general incidents in the London Underground (LU). The results show that it is possible to automatically extract delays, impacts on trains, mitigating strategies, underlying incident causes, and insights related to the potential actions and causes, as well as accurate classification of incidents into predefined categories.
Hierarchy Spatial-Temporal Transformer for Action Recognition in Short Videos

Short videos action recognition based on deep learning has made a series of important progress; most of the proposed methods are based on 3D Convolution neural networks (3D CNN) and Two Stream architecture. However, 3D CNN has a large number of parameters and Two Stream networks cannot learn features well enough. This work aims to build a network to learn better features and reduce the scale of parameters. A Hierarchy Spatial-Temporal Transformer model is proposed, which is based on Two Stream architecture and hierarchy inference. The model is divided into three modules: Hierarchy Residual Reformer, Spatial Attention Module, and Temporal-Spatial Attention Module. In the model, each frame’s image is firstly transformed into a spatial visual feature map. Secondly, spatial feature learning is performed by spatial attention to generating attention spatial feature maps. Finally, the generated attention spatial feature map is incorporated with temporal feature vectors to generate a final representation for classification experiments. Experiment results in the hmdb51 and ucf101 data set showed that the proposed model achieved better accuracy than the state-of-art baseline models
Batu Cave: Prehistoric Occupation Of Meratus Mountains, South Kalimantan

Preliminary studies in the karst hills of the Meratus Mountains in Kotabaru Regency found rock-shelters and caves that were indicated to have traces of prehistoric dwellings. One of them is Batu Cave which is in Batangkulur village, Kelumpang Barat district. This article discusses the results of excavations carried out in Batu Cave in 2018. The problems raised on proof of occupancy and how human life in the past in Batu Cave. Archaeological data were obtained from excavations by using test-pit at two different locations. The excavation findings analyses are quantitative and qualitative. Quantitative analysis was carried out to find out the quantity of findings. The qualitative analysis includes an initial classification, which divides archeological data according to the type, form and style. The results show that Batu Cave are cave dwelling with living activities that rely on the surrounding resources. Exploitation of environmental resources is seen in the use of several types of terrestrial fauna and water as one of the main food sources. Various types of tools were made using rocks, as well as bones and shells.
A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration

One of the primary tasks of morphological parsers is the disambiguation of homographs.Particularly difficult are cases of unbalanced ambiguity, where one of the possible analyses is far more frequent than the others.In such cases, there may not exist sufficient examples of the minority analyses in order to properly evaluate performance, nor to train effective classifiers.In this paper we address the issue of unbalanced morphological ambiguities in Hebrew.We offer a challenge set for Hebrew homographs -the first of its kindcontaining substantial attestation of each analysis of 21 Hebrew homographs.We show that the current SOTA of Hebrew disambiguation performs poorly on cases of unbalanced ambiguity.Leveraging our new dataset, we achieve a new state-of-the-art for all 21 words, improving the overall average F1 score from 0.67 to 0.95.Our resulting annotated datasets are made publicly available for further research.
An Investigation about Entailment and Narrative by AI Techniques (Generative Models)

Many storytelling generation problems concern the difficulty to model the sequence of sentences. Language models are generally able to assign high scores to well-formed text, especially in the cases of short texts, failing when they try to simulate human textual inference. Although in some cases output text automatically generated sounds as bland, incoherent, repetitive and unrelated to the context, in other cases the process reveals capability to surprise the reader, avoiding to be boring/predictable, even if the generated text satisfies entailment task requirements. The lyric tradition often does not proceed towards a real logical inference, but takes into account alternatives like the unexpectedness, useful for predicting when a narrative story will be perceived as interesting. To achieve a best comprehension of narrative variety, we propose a novel measure based on two components: inference and unexpectedness, whose different weights can modify the opportunity for readers to have different experiences about the functionality of a generated story. We propose a supervised validation treatment, in order to compare the authorial original text, learned by the model, with the generated one.
Generalized Quantum Arthur--Merlin Games

This paper investigates the role of interaction and coins in quantum Arthur--Merlin games (also called public-coin quantum interactive proof systems). While the existing model restricts the messages from the verifier to be classical even in the quantum setting, the present work introduces a generalized version of quantum Arthur--Merlin games where the messages from the verifier can be quantum as well: the verifier can send not only random bits, but also halves of EPR pairs (to share with the prover). This generalization turns out to provide several novel characterizations of quantum interactive proof systems with a constant number of turns. First, it is proved that the complexity class corresponding to two-turn quantum Arthur--Merlin games where both of the two messages are quantum (denoted qq-QAM in this paper) does not change by adding a constant number of turns of classical interaction prior to the communications of qq-QAM proof systems. This can be viewed as a quantum analogue of the celebrated collapse theorem for AM due to Babai. To prove this collapse theorem, this paper presents a natural complete problem for qq-QAM: deciding whether the output of a given quantum circuit is close to a totally mixed state. This complete problem is on the very line of the previous studies investigating the hardness of checking properties related to quantum circuits, and thus qq-QAM may provide a good measure in computational complexity theory. It is further proved that the class ${qq-QAM}_1$, the perfect-completeness variant of qq-QAM, gives new bounds for standard well-studied classes of two-turn quantum interactive proof systems. Finally, the collapse theorem above is extended to comprehensively classify the role of classical and quantum interactions in quantum Arthur--Merlin games: it is proved that, for any constant $m\geq2$, the class of problems having $m$-turn quantum Arthur--Merlin proof systems is either equal to PSPACE or equal to the class of problems having two-turn quantum Arthur--Merlin proof systems of a specific type, which provides a complete set of quantum analogues of Babai's collapse theorem.
Can Pre-training help VQA with Lexical Variations?

Rephrasings or paraphrases are sentences with similar meanings expressed in different ways.Visual Question Answering (VQA) models are closing the gap with the oracle performance for datasets like VQA2.0.However, these models fail to perform well on rephrasings of a question, which raises some important questions like Are these models robust towards linguistic variations?Is it the architecture or the dataset that we need to optimize?In this paper, we analyzed VQA models in the space of paraphrasing.We explored the role of language & cross-modal pre-training to investigate the robustness of VQA models towards lexical variations.Our experiments find that pre-trained language encoders generate efficient representations of question rephrasings, which help VQA models correctly infer these samples.We empirically determine why pre-training language encoders improve lexical robustness.Finally, we observe that although pre-training all VQA components obtain state-of-the-art results on the VQA-Rephrasings dataset, it still fails to completely close the performance gap between original and rephrasing validation splits.
The Significant of Fashion Ethics Education on Young Fashion Designers and Entrepreneurs

The research aimed to examine the relationship and understand the significance of ethical fashion education to young fashion designers and entrepreneurs’ value systems and ethical behavior. The research applied a descriptive qualitative method based on focus group discussion using purposive sampling. Data were collected from six young fashion designers and entrepreneurs, who graduated from Fashion Design and Business at Universitas Ciputra Surabaya and had ever attended Fashion Ethics class on their seventh semester. The research result shows that Fashion Ethics education is important and gives a positive impact on participants’ ethical values and behavior. Furthermore, some existing personal traits that came from family, as well as previous formal education, significantly influence people’s value system and ethical behavior, which cause different impacts and results of the program among participants. Meanwhile, the religious background does not have a significant influence on the result. In conclusion, the Fashion Ethics program has a positive effect on young fashion designers and entrepreneurs’ ethical values and behavior.
ANALISIS URGENSI PENGEMBANGAN KOMIK DIGITAL DENGAN MENGINTEGRASIKAN NILAI-NILAI KEISLAMAN

Abstract: In the era of modern industrial technology 4.0, educators are required to be able to develop skills in making learning media that will be used with the help of technology. Comics are popular among teenagers where high school students are included. One type of comics, digital comics, has recently become a global trend, which is useful for use as an effective learning medium. To take advantage of this great opportunity, this study aims to analyze the importance of developing digital comics.The making of this digital comic uses the 4D model which is a development model that consists of 4 stages.