Reviewerly/exHarmony huggingface dataset:
Detection of Human Faces Covered with disguise and Makeup

Face detection is kind of the identification. When we look at someone's face, we can get information like his or her gender and age. Face detection research has exploded in popularity during the last few decades. Starting with algorithms that can detect faces in constrained environments, today's face detection systems can attain extremely great accuracies at the large scale unconstrained facial datasets. While new algorithms continue to increase performance, the majority of face detection systems are vulnerable to failure when subjected to disguise and cosmetics alterations, which is one of the most difficult covariates to overcome. In this article, the database of disguised and makeup faces (DMFD) is employed. In order to address this issue, we detected the location and size of the facial in the image by using Histogram of Oriented Gradients (HOG) + Linear SVM Machine Learning detector on the Disguise and makeup face database (DMFD).This approach is effective and can detect any disguise and makeup faces in the complex background and illumination variation. The results shows the effectiveness of the face detection system on a database (DMFD) and it provided better results of (99.3%).
Understanding Variational Inference in Function-Space

Recent work has attempted to directly approximate the `function-space' or predictive posterior distribution of Bayesian models, without approximating the posterior distribution over the parameters. This is appealing in e.g. Bayesian neural networks, where we only need the former, and the latter is hard to represent. In this work, we highlight some advantages and limitations of employing the Kullback-Leibler divergence in this setting. For example, we show that minimizing the KL divergence between a wide class of parametric distributions and the posterior induced by a (non-degenerate) Gaussian process prior leads to an ill-defined objective function. Then, we propose (featurized) Bayesian linear regression as a benchmark for `function-space' inference methods that directly measures approximation quality. We apply this methodology to assess aspects of the objective function and inference scheme considered in Sun, Zhang, Shi, and Grosse (2018), emphasizing the quality of approximation to Bayesian inference as opposed to predictive performance.
Changes and Challenges in the Teaching of Grammar in the Age of Disruption

The teaching of grammar during classroom meetings is entirely different from the grammar instruction in the new normal. The learning and teaching atmosphere has been more challenging in the contemporary time because people want to be safe and secure as they do transactions. The widespread threats of the pandemic forced humanity to observe mandatory policies and protocols in work, school, and home. Development of language skills is a long time thrust by which ESL teachers carry among their schools. In the Philippine setting, both private and public school teachers share common problems in a different context and situation. The importance of grammar skills can be reflected on the various tasks and activities performed by people in the worksite, academe, and industry. As such, language teachers confront the issues and challenges in the actual ESL classroom where students are expected to learn and master grammar skills among others. Not all students have a good language facility as shown by poor performance in the English language assessments. Some students dislike inclusion of grammar lessons in the curriculum for fear of being unable to participate in the class and produce outputs. This paper aims to identify and describe the challenges (in the time of pandemic) experienced by language teachers on the teaching of grammar in the local Philippine context. Before the study culminated, the researcher has decided to get the views of experts and experienced ESL teachers about this problem and/or issue. It is a fact that has been observed and proved by many teachers not just ESL area but other majors that having grammar competence can help both students and professionals to achieve their goals and perform communicative tasks in the workplace. Results of the study provided insights and tips on how language teachers can make grammar teaching effective and relevant despite changes in setting and environment.
Sparse mixture of von Mises-Fisher distribution

Mixtures of von Mises-Fisher distributions can be used to cluster data on the unit hypersphere.This is particularly adapted for high-dimensional directional data such as texts.We propose in this article to estimate a von Mises mixture using a l1 penalized likelihood.This leads to sparse prototypes that improve both clustering quality and interpretability.We introduce an expectation-maximisation (EM) algorithm for this estimation and show the advantages of the approach on real data benchmark.We propose to explore the trade-off between the sparsity term and the likelihood one with a simple path following algorithm.
Urgensi Pertemuan dan Presentasi Dalam Organisasi Bisnis

&#x0D; &#x0D; &#x0D; The purpose of this article is to identify how to important it is to implement meeting and presentation management in improving quality and effectiveness in business organization. In the business world, there two important things in a running a business. First, a meeting or a meeting where is more of an activity that becomes a means for exchanging data and information, a place to dialogue ideas and concepts, and a place for the exchange of ideas through productive discussions. Any business organization will need fresh and creative new ideas and important information exchange. Second, in every meeting or meeting requires the delivery of information or a good presentation in general, the presentation is done to persuade, inform, motivate or inspire other. Presentations and meetings is an important thing that must be prepared if you are in the business world. The success or failure of a business is influenced by the way we prepare meetings and presentations. So, don’t ever underestimate the issue of this meetings and presentations because this is where all members of business organization in the discussion voice their opinion and ideas. Therefore, it is important for the author to convey two things that are very meaningful in the business world to be developed in you. &#x0D; &#x0D;
An Interpretable Approach to Lexical Semantic Change Detection with Lexical Substitution

In this paper we propose a new Word Sense Induction (WSI) method and apply it to construct a solution for the RuShiftEval shared task on Lexical Semantic Change Detection (LSCD) for the Russian language.Our WSI al gorithm based on lexical substitution achieves stateoftheart performance for the Russian language on the RUSSE 2018 dataset.However, our LSCD system based on it has shown poor performance in the shared task.We have studied mathematical properties of the COMPARE score employed in the task for measuring the degree of semantic change, as well as the discrepancies between this score and our WSI predictions.We have found that our method can detect those aspects of semantic change, which the COMPARE metric is not sensitive to, such as appearance or disappearance of a rare word sense.An important property of our method is its interpretability, which we exploit to perform the detailed error analysis.
Aspects of Information Support of the Learning Process

The article discusses the problem of using information technologies in education, provides examples of using information technologies in education. The significance of the introduction and use of information technologies in Russian society and Russian education is analyzed. It was revealed that information technologies in education allow the use of a wider range of forms and methods of teaching and are one of the modern ways of intensifying and optimizing the educational process
Implementation of optical quantum gate for polarization-encoded qubits via acousto-optic diffraction by shear acoustic waves in vitreous media

We demonstrate experimentally that acousto-optic diffraction by a shear acoustic wave propagating in fused silica can transform linear or circular polarization states into their orthogonal counterparts.It is also shown that a corresponding acousto-optic cell with a shear acoustic wave propagating in a vitreous medium can be used as a NOT or CNOT gate.The advantage of our method is a possibility for spatial manipulation by qubits via acousto-optic diffraction under condition of changing frequency of the acoustic wave.
A novel meta-heuristic optimization algorithm based on cell division: Cell Division Optimizer

Abstract A novel meta-heuristic algorithm named as the Cell Division Optimizer (CDO) is proposed. The proposed algorithm is inspired by the reproduction methods at the cellular level, which is formulated by the well-known cell division process known as mitosis and meiosis. In the proposed model Meiosis and Mitosis govern the exploration and exploitation aspects of the optimization algorithm, respectively. In the proposed method, the solutions are updated in two phases to achieve the global optimum solution. The proposed algorithm can be easily adopted to solve the combinatorial optimization method. To evaluate the proposed method, 50 well-known benchmark test functions and also 2 classical engineering optimization problems including 1 mechanical engineering problem and 1 electrical engineering problem are employed. The results of the proposed method are compared with the latest versions of state-of-the-art algorithms like Particle Swarm Optimization, Cuckoo Search, Grey Wolf Optimizer, FruitFly Optimization, Whale Optimizer, Water-Wave Optimizer and recently proposed variants of top-performing algorithms like SHADE (success history-based adaptive differential evolution) and CMAES (Covariance matrix adaptation evolution strategy). Moreover, the convergence speed of the proposed algorithm is better than the considered competitive methods in most cases.
Efficient Bayesian network structure learning via local Markov boundary  search

We analyze the complexity of learning directed acyclic graphical models from observational data in general settings without specific distributional assumptions. Our approach is information-theoretic and uses a local Markov boundary search procedure in order to recursively construct ancestral sets in the underlying graphical model. Perhaps surprisingly, we show that for certain graph ensembles, a simple forward greedy search algorithm (i.e. without a backward pruning phase) suffices to learn the Markov boundary of each node. This substantially improves the sample complexity, which we show is at most polynomial in the number of nodes. This is then applied to learn the entire graph under a novel identifiability condition that generalizes existing conditions from the literature. As a matter of independent interest, we establish finite-sample guarantees for the problem of recovering Markov boundaries from data. Moreover, we apply our results to the special case of polytrees, for which the assumptions simplify, and provide explicit conditions under which polytrees are identifiable and learnable in polynomial time. We further illustrate the performance of the algorithm, which is easy to implement, in a simulation study. Our approach is general, works for discrete or continuous distributions without distributional assumptions, and as such sheds light on the minimal assumptions required to efficiently learn the structure of directed graphical models from data.
Interactive Handwritten and Text-Based Handwritten Arabic CAPTCHA Schemes for Mobile Devices: A Comparative Study

CAPTCHA tests (Completely Automated Public Turing test to tell Computer and Humans Apart) are used by many services and websites. Recently, researchers have proposed interactive handwritten and text-based handwritten Arabic CAPTCHA schemes. The former scheme presents a handwritten CAPTCHA image, then requests users to select the joints between Arabic letters. In the latter scheme, a new generator of Arabic handwritten CAPTCHA images is developed, once the image is generated, the user is asked to type the letters shown in the image. Although both of them have shown promising results, this experimental study compares them in terms of security and usability for mobile device applications. The results demonstrated that the interactive scheme performs better than the text-based handwritten scheme in both usability and security.
Learning a self‐supervised tone mapping operator via feature contrast masking loss

Abstract High Dynamic Range (HDR) content is becoming ubiquitous due to the rapid development of capture technologies. Nevertheless, the dynamic range of common display devices is still limited, therefore tone mapping (TM) remains a key challenge for image visualization. Recent work has demonstrated that neural networks can achieve remarkable performance in this task when compared to traditional methods, however, the quality of the results of these learning‐based methods is limited by the training data. Most existing works use as training set a curated selection of best‐performing results from existing traditional tone mapping operators (often guided by a quality metric), therefore, the quality of newly generated results is fundamentally limited by the performance of such operators. This quality might be even further limited by the pool of HDR content that is used for training. In this work we propose a learning‐based self‐supervised tone mapping operator that is trained at test time specifically for each HDR image and does not need any data labeling. The key novelty of our approach is a carefully designed loss function built upon fundamental knowledge on contrast perception that allows for directly comparing the content in the HDR and tone mapped images. We achieve this goal by reformulating classic VGG feature maps into feature contrast maps that normalize local feature differences by their average magnitude in a local neighborhood, allowing our loss to account for contrast masking effects. We perform extensive ablation studies and exploration of parameters and demonstrate that our solution outperforms existing approaches with a single set of fixed parameters, as confirmed by both objective and subjective metrics.
Sintering temperature influence on grains function distribution by neural network application

Artificial neural networks application in science and techonology begun during 20th century. This biophysical and biomimetic phenomena is based on extensive research which have led to understanding how neural as a living organism nerve system basic element processes signals by a simple algorithm. The input signals are massively parallel processed, and the output presents the superposition of all parallel processed signals. Artificial neural networks which are based on these principles are useful for solving various problems as pattern recognition, clustering, functional optimization. This research analyzed thermophysical parameters at samples based on Murata powders and consolidated by sintering process. Among different physical properties we applied out neural network approach on grain sizes distribution as a function of sintering temperature, T, (from 1190-1370?C). In this paper, we continue to apply neural networks to prognose structural and thermophysical parameters. For consolidation sintering process is very important to prognose and design many parameters but especially thermal like temperature, to avoid long and even wrong experiments which are wasting the time and materials and energy as well. By this artificial neural networks method we indeed provide the most efficient procedure in projecting the mentioned parameters and provide successful ceramics samples production. This is very helpful in prediction and designing the micro-structure parameters important for advance microelectronic further miniaturization development. This is a quite original novelty for real micro-structure projecting especially on the phenomena within the thin films coating around the grains what opens new prospective in advance fractal microelectronics.
Quantifying tripartite spatial and energy-time entanglement in nonlinear optics

In this work, we provide a means to quantify genuine tripartite entanglement in arbitrary (pure and mixed) continuous-variable states as measured by the tripartite entanglement of formation---a resource-based measure quantifying genuine multipartite entanglement in units of elementary Greenberger-Horne-Zeilinger (GHZ) states called gebits. Furthermore, we predict its effectiveness in quantifying the tripartite spatial and energy-time entanglement in photon triplets generated in cascaded spontaneous parametric down-conversion (SPDC), and find that ordinary nonlinear optics can be a substantial resource of tripartite entanglement.
One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them

We consider distributed statistical optimization in one-shot setting, where there are $m$ machines each observing $n$ i.i.d. samples. Based on its observed samples, each machine sends a $B$-bit-long message to a server. The server then collects messages from all machines, and estimates a parameter that minimizes an expected convex loss function. We investigate the impact of communication constraint, $B$, on the expected error and derive a tight lower bound on the error achievable by any algorithm. We then propose an estimator, which we call Multi-Resolution Estimator (MRE), whose expected error (when $B\ge\log mn$) meets the aforementioned lower bound up to poly-logarithmic factors, and is thereby order optimal. We also address the problem of learning under tiny communication budget, and present lower and upper error bounds when $B$ is a constant. The expected error of MRE, unlike existing algorithms, tends to zero as the number of machines ($m$) goes to infinity, even when the number of samples per machine ($n$) remains upper bounded by a constant. This property of the MRE algorithm makes it applicable in new machine learning paradigms where $m$ is much larger than $n$.
Swarm Optimized Opinion Classification Model for Policy Assessment

A government policy is a scheme launched by the governing body of a nation for the welfare of a particular section of the society or the entire public in general. The impact of such a policy can hence only be determined by the response from its target group. The evaluation of these schemes is often challenging, due to the inability of the government body or organization to collect unfiltered and unbiased feedback from the entire population. The aforementioned task may require a large amount of effort, considerable time and in-depth knowledge of advanced technology. However, with the advent of the information era, it is possible to analyze the sentiments of the public using negligible resources. The internet is rich in freely available unused and unstructured data that can be exploited efficiently for various purposes. One such application is opinion mining which allows the user to extract data from social media websites and categorize it into pre-defined classes. This paper is an attempt to assess one of the most important and current government initiatives- “Digital India”, through public sentiments. Digital India is a program launched by the Prime Minister of India to transform the country into a technologically advanced and digitally connected nation. This research work corroborates the use of swarm intelligence or nature-inspired algorithms for feature subset selection during opinion mining, as it results in a substantial reduction in the number of features (and consequently a lesser computation time for model training) and increase in the classification accuracy of the model. Therefore, the aim of this study is to analyze public opinion on “Digital India” campaign to ascertain the success (or failure) of the mission, while at the same time, determine the most suited model for automated evaluation of any government policy in the future.
Improving German Image Captions Using Machine Translation and Transfer Learning

Image captioning is a complex artificial intelligence task that involves many fundamental questions of data representation, learning, and natural language processing. In addition, most of the work in this domain addresses the English language because of the high availability of annotated training data compared to other languages. Therefore, we investigate methods for image captioning in German that transfer knowledge from English training data. We explore four different methods for generating image captions in German, two baseline methods and two more advanced ones based on transfer learning. The baseline methods are based on a state-of-the-art model which we train using a translated version of the English MS COCO dataset and the smaller German Multi30K dataset, respectively. Both advanced methods are pre-trained using the translated MS COCO dataset and fine-tuned for German on the Multi30K dataset. One of these methods uses an alternative attention mechanism from the literature that showed a good performance in English image captioning. We compare the performance of all methods for the Multi30K test set in German using common automatic evaluation metrics. We show that our advanced method with the alternative attention mechanism presents a new baseline for German BLEU, ROUGE, CIDEr, and SPICE scores, and achieves a relative improvement of 21.2% in BLEU-4 score compared to the current state-of-the-art in German image captioning.
A Support System for Generating YouTube Titles using Style Transfer

The generation of attractive sentences for consumers is an important issue. In this paper, we propose a new method to generate title sentences that are more popular on YouTube, which is gaining large popularity in recent years. In the proposed method, the automatic generation of titles is regarded as a sentence style transfer task; The title sentence generated by the video creator is transformed into a more attractive one. Specifically, first, the latent variable is obtained by the back translation method. In the back translation method, it translates the original title into another language and retranslates it into Japanese. Then, style transfer is performed based on this latent variable to generate a more attractive title sentence. In addition, transfer learning is employed to address the problem of scarce training data. Subjective evaluation experiments have been conducted to show the effectiveness of the proposed method.
Genz and Mendell-Elston Estimation of the High-Dimensional Multivariate Normal Distribution

Statistical analysis of multinomial data in complex datasets often requires estimation of the multivariate normal (mvn) distribution for models in which the dimensionality can easily reach 10–1000 and higher. Few algorithms for estimating the mvn distribution can offer robust and efficient performance over such a range of dimensions. We report a simulation-based comparison of two algorithms for the mvn that are widely used in statistical genetic applications. The venerable Mendell-Elston approximation is fast but execution time increases rapidly with the number of dimensions, estimates are generally biased, and an error bound is lacking. The correlation between variables significantly affects absolute error but not overall execution time. The Monte Carlo-based approach described by Genz returns unbiased and error-bounded estimates, but execution time is more sensitive to the correlation between variables. For ultra-high-dimensional problems, however, the Genz algorithm exhibits better scale characteristics and greater time-weighted efficiency of estimation.