Reviewerly/exHarmony huggingface dataset:
Face Synthesis via User Manipulation of Disentangled Latent Representation

In this paper, we propose a novel face synthesis method whose process can be conditioned not only on labels but also on latent variables as “unknown” labels corresponding to unrevealed factors. We extend Variational Autoencoder (VAE) without any additional networks to introduce conditional generation, disentangled representation, and adversarial learning into one autoencoder. Since previous conditional generative models require the annotation of labels to condition them on, disentanglement, i.e., the unsupervised discovery of generative factors enables users to generate face images more flexibly and more efficiently. Moreover, although generative adversarial networks (GANs) have problems of mode collapse and instability of the learning process, adversarial learning on VAE in an introspective way achieves both the variation of results and the stability of generation. Evaluations on the CelebFaces Attributes Dataset (CelebA) show that our method can generate face images following users' conditioning both on the known and the “unknown” labels.
Intelligent Distributed Web Crawler Based on Attention Mechanism

With the rapid development of the Internet, webpages' content has become the central platform for people to publish and retrieve information. Recently, web crawlers could quickly and accurately find the information users need from the massive network information resources. There have been many different types of web crawlers in the literature, developed for data retrieval. However, most of the existing web crawlers have significant limitations. For example, they focus on the effective overall architecture instead of paying attention to the actual data's complexity. Moreover, the advertising links in the news and the public platform's promotional content have become ubiquitous noise. The existing web crawler collection strategy lacks sufficient identification of advertising information. The degree of automation to detect advertisements is low, so it isn't easy to form a complete and deployable large-scale distributed data crawling system. Therefore, the research and improvement of distributed web crawlers that intelligently distinguish advertisements is a work of practical significance. The distributed intelligent web crawler system designed and implemented in this paper solves low manual crawler efficiency and poor data quality. The crawler system can effectively identify and eliminate advertising information and significantly improve the automatically extracted data in the distributed crawler system from the experimental results.
Software implementation for diagnostic decision-making based on multiparametric ultrasonography parameters of breast masses

Purpose . To build a system for making diagnostic decisions based on multiparametric ultrasound examination of the mammary gland. Material and Methods . A total of 277 women with various complaints of breast diseases were examined using a wide range of ultrasound technologies, including elastography and contrast enhancement. To verify the lesions, the patients underwent histological or cytological examination. The results of ultrasound examination, histological and cytological conclusions were entered into the database. The system was trained with 219 patient samples selected from the database. The patient’s samples were grouped according to the visual ultrasound characteristics. The data groups’ analysis allowed the compilation of a table with relative frequencies (probability) of symptoms for this diagnosis. Based on the convolution and the Bayesian method, the system for support of the medical decision by age, clinical picture, and ultrasound diagnostics results were built. Sensitivity and specificity were determined for software implementation in the initial database. Conclusion . The proposed decision support system allows us to determine the probability of malignancy and standardize decision-making in the differential diagnosis of breast masses.
Fusion Techniques for the Breast Cancer Detection

Breast cancer is a considerable prime cause which affects the lives of women’s and leads to death all around the world. A tumor is said to be malignancy if a number of cancerous cells can spread to other organs. There has been great impact on the field of medical imaging by the advancement of the computer technology as new and improved techniques of data acquisition, analysis, processing and visualization has evolved. A Digital Breast Tomosynthesis is used for the detection of breast cancer. This is also called as three-dimensional mammography, which eliminates the overlapping tissue problem. Digital Breast Tomosynthesis (DBT) provides information about potential abnormal tissues necessary for medical follow up and diagnosis. DBT gets additional importance in medical science as it is the only preliminary method of diagnosing a breast cancer with the dense breast. Early and accurate diagnosis can be sufficient in resolving various complications and guiding the patient with timely and proper treatment. By considering the above factors there is a great requirement to explore for DBT. The proposed work develops a computer aided methodology for automatic tumor detection and diagnosing in tomosynthesis Patient’s image. This method is incredibly helpful for doctors or the radiologist automatically locates the tumor space within the breast image for further surgery.
Quantum Amplitude Arithmetic

Quantum algorithm involves the manipulation of amplitudes and computational basis, of which manipulating basis is largely a quantum analogue of classical computing that is always a major contributor to the complexity. In order to make full use of quantum mechanical speedup, more transformation should be implemented on amplitudes. Here we propose the notion of quantum amplitude arithmetic (QAA) that intent to evolve the quantum state by performing arithmetic operations on amplitude. Based on the basic design of multiplication and addition operations, QAA can be applied to solve the black-box quantum state preparation problem and the quantum linear system problem with fairly low complexity, and evaluate nonlinear functions on amplitudes directly. QAA is expected to find applications in a variety of quantum algorithms.
Strengths and Weaknesses of Self-Regulated Learning through YouTube: Indonesian EFL Students’ Perceptions

The recent advancements in technology are growing rapidly, because of that the demands of Internet are increasing rapidly. Include the use of website and application. Technology has brought various potentials for language learning. Therefore, in this research the aims are to determine the perceptions of EFL students and the strengths and weaknesses of self-regulated learning using YouTube. To achieve the objective of the research, the method used in this research is a case study. The data in this research were collected through in-depth interviews. The subjects in this research were four undergraduate students who are majoring in English education from university in Indonesia and they were purposively selected, so that they can provide sufficient information. The results in this research showed the positive perceptions of students towards EFL students self-regulated learning using YouTube. They declared that the use of YouTube could facilitate them in regulating their learning English, brings joy, pleasure and excitement during the learning process. In addition, in this research found out that the strengths of self-regulated learning using YouTube; YouTube provides a lot of learning materials, the ease to use YouTube, flexibility of time and place when using YouTube in regulating their learning. Meanwhile the weaknesses are financial constraints and problem of Internet connectivity.
UPAYA PENINGKATAN HASIL BELAJAR SISWA PADA MASA PANDEMI COVID-19 DI SDMU AHMAD DAHLAN BRAJA SELEBAH

This article aims to describe the efforts made by SDMU Ahmad Dahlan Braja Selebah in improving student learning outcomes during the Covid 19 Pandemic. The learning system carried out during the Covid19 pandemic currently requires teachers to be more active and creative by utilizing social media as a medium of learning. The research method used in this research is descriptive qualitative data collection methods through observation, interviews and documentation. The results showed that the teacher chose the right learning method and the school supported it by facilitating the use of existing media as a means of delivering distance learning material as well as conducting regular evaluation of learning. The positive contribution in this study is intended to inform the managers of educational institutions that learning activities during the Covid-19 period can be improved by fulfilling distance learning sarpras and choosing appropriate learning methods and material presentation.
Synthesis and Visualization of Photorealistic Textures for 3D Face Reconstruction of Prehistoric Human

Reconstruction of face 3D shape and its texture is a challenging task in the modern anthropology. While a skilled anthropologist could reconstruct an appearance of a prehistoric human from its skull, there are no automated methods to date for automatic anthropological face 3D reconstruction and texturing. We propose a deep learning framework for synthesis and visualization of photorealistic textures for 3D face reconstruction of prehistoric human. Our framework leverages a joint face-skull model based on generative adversarial networks. Specifically, we train two image-to-image translation models to separate 3D face reconstruction and texturing. The first model translates an input depth map of a human skull to a possible depth map of its face and its semantic parts labeling. The second model, performs a multimodal translation of the generated semantic labeling to multiple photorealistic textures. We generate a dataset consisting of 3D models of human faces and skulls to train our 3D reconstruction model. The dataset includes paired samples obtained from computed tomography and unpaired samples representing 3D models of skulls of prehistoric human. We train our texture synthesis model on the CelebAMask-HQ dataset. We evaluate our model qualitatively and quantitatively to demonstrate that it provides robust 3D face reconstruction of prehistoric human with multimodal photorealistic texturing.
Handwritten Arabic Digit Recognition Using Convolutional Neural Network

In Computer vision systems, computer vision works by imitating humans in their vision way which is known as the human vision system (HVS). In HVS, humans use their eyes and brains in order to see and classify any object around them. Hence, computer vision systems imitate HSV by developing several algorithms for classifying images and objects. The main goal of this paper is to propose a model for identifying and classifying the Arabic handwritten digits with high accuracy.  The concept of deep learning via the convolutional neural network (CNN) with the ADBase database is used to achieve the goal. The training is done by having a 3*3 and 5*5 filters. Basically, while the classification phase distinct learning rates are used to train the network. The obtained results are encouraging and promising.
Biased Models Have Biased Explanations

We study fairness in Machine Learning (FairML) through the lens of attribute-based explanations generated for machine learning models. Our hypothesis is: Biased Models have Biased Explanations. To establish that, we first translate existing statistical notions of group fairness and define these notions in terms of explanations given by the model. Then, we propose a novel way of detecting (un)fairness for any black box model. We further look at post-processing techniques for fairness and reason how explanations can be used to make a bias mitigation technique more individually fair. We also introduce a novel post-processing mitigation technique which increases individual fairness in recourse while maintaining group level fairness.
The authorship of the poem ‘Ushshaq-nama from the prospect of academic orientalist studies and modern computer technologies

The poem ‘Ushshaq-nama by Fakhr ad-Din ‘Iraqi (610–688 / 1213–1289) is the first poetical writing on the subject of mystical love in Persian literature. ‘Iraqi’s authorship of this work has never been questioned by researchers. However, the English orientalist J. Baldick 1973 cast a doubt on ‘Iraqi’s authorship of this poem. The article examines in detail the arguments of J. Baldick both from the point of view of the context of the creation of the poem, as well as the methods of the latest computational methods, in particular stylometry. Boldik's arguments concern both the historical and religious context in which ‘Iraqi lived and the peculiarities of his works, primarily the poem‘Ushshaq-name. The paper demonstrates that this computational method may be used for the study of the features of the style of Persian poetry and the confirmation of the authorship of doubtful Persian writings. Further expert decision full-field by Artjoms Šeļa based on stylometric methods for the establishment of the poem ‘Ushshaq- nama is expounded. While the results of the analysis of the historical and religious contexts of the poem may serve as confirmation of the authorship of ‘Iraqi and the refutation of the Baldick hypothesis, the results of the stylometric analysis do not give an unambiguous answer to the question of its authorship.
Fixed-Parameter Tractability of the Weighted Edge Clique Partition Problem

We develop an FPT algorithm and a compression for the Weighted Edge Clique Partition (WECP) problem, where a graph with n vertices and integer edge weights is given together with an integer k, and the aim is to find k cliques, such that every edge appears in exactly as many cliques as its weight. The problem has been previously only studied in the unweighted version called Edge Clique Partition (ECP), where the edges need to be partitioned into k cliques. It was shown that ECP admits a kernel with k² vertices [Mujuni and Rosamond, 2008], but this kernel does not extend to WECP. The previously fastest algorithm known for ECP has a runtime of 2^𝒪(k²)n^O(1) [Issac, 2019]. For WECP we develop a compression (to a slightly more general problem) with 4^k vertices, and an algorithm with runtime 2^𝒪(k^(3/2)w^(1/2)log(k/w))n^O(1), where w is the maximum edge weight. The latter in particular improves the runtime for ECP to 2^𝒪(k^(3/2)log k)n^O(1).
Text Classification on Large Scale Chinese News Corpus using Character-level Convolutional Neural Network

Abstract In view of the current problems in the field of Chinese news, such as chaotic news data and lack of automatic and efficient management, most of the existing Chinese text classification methods take word characteristics as the basic unit of text representation, but ignore the beneficial performance of character characteristics. In this paper, an automatic classification method of Chinese news based on character-level convolutional neural networks(char-CNN) is proposed. In order to compare with traditional models and deep learning models, we constructed a large scale Chinese news corpus to show that character-level convolutional neural networks could achieve the most advanced or competitive results.
Corporate Social Responsibility For Employee’s Commitment: Does Corporate Reputation Count?

Corporate social responsibility (CSR) has attracted the attention of scholars and companies.To keep employees committed and to sustain in a competitive business environment, companies need to ensure the retention of its talent and corporate responsible behaviour.Aiming at examining the effect of CSR on corporate reputation and employee's commitment, and the mediating role of corporate reputation in influencing the impact of CSR on employee's commitment in Malaysian banking sector, the study gathered data from randomly selected samples among selected banks' employees through self-administered survey.Using SmartPLS to analyse the formative higher order structural model, this study revealed that for employees to be committed, companies need to focus on behaving more responsibly to the society and environment, rather than other dimensions of CSR.Organization's responsible behaviour also contributed significantly to its reputation development.Surprisingly, being perceived as a reputable organization did not show a significant role in mediating the impact of CSR practices on employee's commitment.Thus, this study confirmed that to gain commitment from its employees, looking good is insufficient.The banks, however, need to be worthy by doing good to its stakeholders
On the Power of Localized Perceptron for Label-Optimal Learning of Halfspaces with Adversarial Noise

We study {\em online} active learning of homogeneous halfspaces in $\mathbb{R}^d$ with adversarial noise where the overall probability of a noisy label is constrained to be at most $ν$. Our main contribution is a Perceptron-like online active learning algorithm that runs in polynomial time, and under the conditions that the marginal distribution is isotropic log-concave and $ν= Ω(ε)$, where $ε\in (0, 1)$ is the target error rate, our algorithm PAC learns the underlying halfspace with near-optimal label complexity of $\tilde{O}\big(d \cdot polylog(\frac{1}ε)\big)$ and sample complexity of $\tilde{O}\big(\frac{d}ε \big)$. Prior to this work, existing online algorithms designed for tolerating the adversarial noise are subject to either label complexity polynomial in $\frac{1}ε$, or suboptimal noise tolerance, or restrictive marginal distributions. With the additional prior knowledge that the underlying halfspace is $s$-sparse, we obtain attribute-efficient label complexity of $\tilde{O}\big( s \cdot polylog(d, \frac{1}ε) \big)$ and sample complexity of $\tilde{O}\big(\frac{s}ε \cdot polylog(d) \big)$. As an immediate corollary, we show that under the agnostic model where no assumption is made on the noise rate $ν$, our active learner achieves an error rate of $O(OPT) + ε$ with the same running time and label and sample complexity, where $OPT$ is the best possible error rate achievable by any homogeneous halfspace.
Faktor Pembentuk dari Kemandirian Belajar Siswa

Self-Regulated Learner is a student who can stand alone, not dependent on others. His or her behavior and attitude do not come in sudden but through a process since their childhood. Among individuals are different because of many factors. Factors that greatly influence student's self-regulated attitude categories into two factors from internal and external. Internal factors, namely, physiological factors; including the physical condition of students, healthy or unhealthy, and psychological factors; including talents, interests, attitudes, motivation, intelligence, and others. Meanwhile, external factors; including family, school, and society. This study discussed how good student self-regulated learning, its characteristics, its benefits, and its process.
Multi-Camera Panoramic Imaging System Based on Adaptive Brightness Uniformization For The Truck

Abstract With the development of science technology and logistics industry, the transportation of large trucks is also indispensable. It is also accompanied by many traffic accidents. The paper mainly focuses on the problem of blind spots in the parking of large trucks. A panoramic imaging system with six cameras is implemented to improve its safety performance. It mainly includes the realization of modules included simultaneous acquisition of images by multiple cameras, image calibration and correction, top view image generation, image stitching and image fusion. In view of the brightness difference of images acquired by different cameras, we proposed an adaptive brightness uniformization method based on γ-transformation. Meanwhile, we compared the proposed method with various image enhancement methods included histogram equalization, Laplace operator and logarithm transformation. The experimental results in the system demonstrate the superiority of our proposed method compared to others.
Hierarchy-NMS: Merging Candidate Bounding Boxes for Cerebrospinal Fluid Cell Image Segmentation

Abstract In this article, Hierarchy-Non Maximum Suppression (H-NMS) is proposed to fix the defects of general NMS algorithms in cerebrospinal fluid cell image segmentation. To cope with the confusion caused by the hierarchy of candidate bounding boxes in this scenario, a tree structure is built on the top of all bounding boxes and only leaf nodes are saved as the final output. The experimental results showed that H-NMS outperforms some variants of NMS in the cerebrospinal fluid cell image processing in both image segmentation and cell counting.
Application of Blended Learning Model to Computer Fundamentals Courses in an Online Environment

In the online environment, schools should keep up with the pace of development and strengthen the application of hybrid teaching mode, so as to strengthen the cultivation of students' comprehensive computer application ability and computer subject literacy, and improve students' learning efficiency. The author explores the actual situation of teaching basic computer courses at this stage and the requirements of applying hybrid teaching mode to carry out computer basic course teaching under the network environment, and puts forward an effective strategy for the application of hybrid teaching mode in computer basic course under the network environment, hoping to contribute to the improvement of the teaching quality and quality of computer basic course.
O projeto 'Edição Digital dos Vocabulários da Academia das Ciências': o VOLP-1940

This paper presents the Digital Edition of the Vocabularies of the Academy of Sciences project, which aims to digitise the spelling vocabularies of the Lisbon Academy of Sciences (ACL) in order to create a digital lexicographic corpus bringing together the printed versions of all these lexicographical reference works – the 1940, 1947, 1970, and finally the 2012 editions. The first stage started with the Vocabulário Ortográfico da Língua Portuguesa [Orthographic Vocabulary of the Portuguese Language] (VOLP-1940), our case study. After digitising this vocabulary, the work described here focuses on the linguistic annotation of VOLP-1940 using eXtensible Markup Language (XML), an annotation metalanguage, and following the annotation directives of the Text Encoding Initiative (TEI), more specifically the application of TEI Lex-0, a new TEI sub-format. We aim to highlight the need for rigorous linguistic data processing in the creation of new lexical resources to increase the quality of their description and applicability.
Distribution Testing On The Average Room Occupancy Rate Of Hotels By Province In 2013-2017

Abstract Data on Average Room Occupancy Rate of Hotels by Province in 2013-2017 obtained from the Central Statistics Agency. The Average Data of Hotel Room Occupancy Rate by Province in 2013-2017 is continuous data. This data will be tested using 4 types of continuous distribution data, normal, exponential, lognormal, and gamma distribution. This data processing uses Minitab software to determine the opportunity value of BKA and BKB. This distribution test uses a hypothesis where if there is no data in accordance with the distribution used, further data processing will be carried out to determine what type of distribution is in accordance with the continuous data using EasyFit Software. Meanwhile, if the data matches more than 1 distribution, the coefficient of determination testing will be done to find out which distribution is the closest to the distribution test results.