Reviewerly/exHarmony huggingface dataset:
THE RUNTIME ANALYSIS OF COMPUTATION OF MODULAR EXPONENTIATION

Context. Providing the problem of fast calculation of the modular exponentiation requires the development of effective algorithmic methods using the latest information technologies. Fast computations of the modular exponentiation are extremely necessary for efficient computations in theoretical-numerical transforms, for provide high crypto capability of information data and in many other applications.&#x0D; Objective – the runtime analysis of software functions for computation of modular exponentiation of the developed programs based on parallel organization of computation with using multithreading.&#x0D; Method. Modular exponentiation is implemented using a 2k-ary sliding window algorithm, where k is chosen according to the size of the exponent. Parallelization of computation consists in using the calculation of the remainders of numbers raised to the power of 2i modulo, and their further parallel multiplications modulo.&#x0D; Results. Comparison of the runtimes of three variants of functions for computing the modular exponentiation is performed. In the algorithm of parallel organization of computation with using multithreading provide faster computation of modular exponentiation for exponent values larger than 1K binary digits compared to the function of modular exponentiation of the MPIR library. The MPIR library with an integer data type with the number of binary digits from 256 to 2048 bits is used to develop an algorithm for computing the modular exponentiation with using multithreading.&#x0D; Conclusions. In the work has been considered and analysed the developed software implementation of the computation of modular exponentiation on universal computer systems. One of the ways to implement the speedup of computing modular exponentiation is developing algorithms that can use multithreading technology on multi-cores microprocessors. The multithreading software implementation of modular exponentiation with increasing from 1024 the number of binary digit of exponent shows an improvement of computation time with comparison with the function of modular exponentiation of the MPIR library.
Semantically Distributed Robust Optimization for Vision-and-Language Inference

Analysis of vision-and-language models has revealed their brittleness under linguistic phenomena such as paraphrasing, negation, textual entailment, and word substitutions with synonyms or antonyms.While data augmentation techniques have been designed to mitigate against these failure modes, methods that can integrate this knowledge into the training pipeline remain under-explored.In this paper, we present SDRO † , a model-agnostic method that utilizes a set linguistic transformations in a distributed robust optimization setting, along with an ensembling technique to leverage these transformations during inference.Experiments on benchmark datasets with images (NLVR 2 ) and video (VIOLIN) demonstrate performance improvements as well as robustness to adversarial attacks.Experiments on binary VQA explore the generalizability of this method to other V&L tasks.
Integration of Natural and Artificial Light on Energy Efficiency of the Mega Bank Makassar Tower Building

One of the largest energy consumers in the world is buildings. The energy consumption comes from the lighting system. Energy use in&#x0D; buildings is generally 25% for lighting systems. The strategy used in building design is to reduce energy consumption while maintaining the&#x0D; best comfort in a building. The application of energy-saving concepts from the building sector is optimizing the lighting system by integrating&#x0D; natural and artificial lighting systems. This study aims to determine the light intensity in the integrated lighting system of natural and artificial&#x0D; manually and also to find out how much energy can be saved with the integrated lighting system manually. The research location is at the&#x0D; Mega Bank Makassar Tower Building. The research sample was selected by purposive sampling and the sixth floor was chosen as the&#x0D; research location. In this study, simulations were carried out using the DIAlux 4.13 program to integrate natural and artificial light and to&#x0D; calculate the amount of energy efficiency in the workspace. To obtain optimal light intensity and energy savings, a simulation was carried&#x0D; out by turning off half the light points in the workspace, especially the light points around the building openings. The simulation results show&#x0D; that the average integrated lighting quality meets the minimum lighting requirements and can save energy usage by up to 50%.&#x0D; Keywords: energy efficiency; integration lighting; workspace
PF : Website Fingerprinting Attack Using Probabilistic Topic Model

Website fingerprinting (WFP) attack enables identifying the websites a user is browsing even under the protection of privacy-enhancing technologies (PETs). Previous studies demonstrate that most machine-learning attacks need multiple types of features as input, thus inducing tremendous feature engineering work. However, we show the other alternative. That is, we present Probabilistic Fingerprinting (PF), a new website fingerprinting attack that merely leverages one type of features. They are produced by using a mathematical model PWFP that combines a probabilistic topic model with WFP for the first time, due to a finding that a plain text and the sequence file generated from a traffic instance are essentially the same. Experimental results show that the proposed new features are more distinguishing than the existing features. In a closed-world setting, PF attains a better accuracy performance (99.79% at most) than prior attacks on various datasets gathered in the scenarios of Shadowsocks, SSH, and TLS, respectively. Besides, even when the number of training instances drops to as few as 4, PF still reaches an accuracy of above 90%. In the more realistic open-world setting, PF attains a high true positive rate (TPR) and Bayes detection rate (BDR), and a low false positive rate (FPR) in all evaluations, which outperforms the other attacks. These results highlight that it is meaningful and possible to explore new features to improve the accuracy of WFP attacks.
Improving the Quality Trade-Off for Neural Machine Translation Multi-Domain Adaptation

Building neural machine translation systems to perform well on a specific target domain is a well-studied problem.Optimizing system performance for multiple, diverse target domains however remains a challenge.We study this problem in an adaptation setting where the goal is to preserve the existing system quality while incorporating data for domains that were not the focus of the original translation system.We find that we can improve over the performance trade-off offered by Elastic Weight Consolidation with a relatively simple data mixing strategy.At comparable performance on the new domains, catastrophic forgetting is mitigated significantly on strong WMT baselines.Combining both approaches improves the Pareto frontier on this task.
An Automorphic Distance Metric and Its Application to Node Embedding for Role Mining

Role is a fundamental concept in the analysis of the behavior and function of interacting entities in complex networks. Role discovery is the task of uncovering the hidden roles of nodes within a network. Node roles are commonly defined in terms of equivalence classes. Two nodes have the same role if they fall within the same equivalence class. Automorphic equivalence, where two nodes are equivalent when they can swap their labels to form an isomorphic graph, captures this notion of role. The binary concept of equivalence is too restrictive, and nodes in real-world networks rarely belong to the same equivalence class. Instead, a relaxed definition in terms of similarity or distance is commonly used to compute the degree to which two nodes are equivalent. In this paper, we propose a novel distance metric called automorphic distance, which measures how far two nodes are from being automorphically equivalent. We also study its application to node embedding, showing how our metric can be used to generate role-preserving vector representations of nodes. Our experiments confirm that the proposed automorphic distance metric outperforms a state-of-the-art automorphic equivalence-based metric and different state-of-the-art techniques for the generation of node embeddings in different role-related tasks.
Algebraic Fault Analysis of SHA-256 Compression Function and Its Application

Cryptographic hash functions play an essential role in various aspects of cryptography, such as message authentication codes, pseudorandom number generation, digital signatures, and so on. Thus, the security of their hardware implementations is an important research topic. Hao et al. proposed an algebraic fault analysis (AFA) for the SHA-256 compression function in 2014. They showed that one could recover the whole of an unknown input of the SHA-256 compression function by injecting 65 faults and analyzing the outputs under normal and fault injection conditions. They also presented an almost universal forgery attack on HMAC-SHA-256 using this result. In our work, we conducted computer experiments for various fault-injection conditions in the AFA for the SHA-256 compression function. As a result, we found that one can recover the whole of an unknown input of the SHA-256 compression function by injecting an average of only 18 faults on average. We also conducted an AFA for the SHACAL-2 block cipher and an AFA for the SHA-256 compression function, enabling almost universal forgery of the chopMD-MAC function.
Meta-Embeddings in Taxonomy Enrichment Task

In this paper we consider the taxonomy enrichment task based on a recently appeared dataset, called Diachronic wordnets, created on the basis of English and Russian wordnets.We study meta-embeddings approaches, which combine several source embeddings, to the hypernym prediction of novel words and show that meta-embedding approaches obtain the best results for this task if compared to other methods based on different principles.When combining with automatically extracted features from the Wiktionary online dictionary, the joint approach improves the results.
Prediction of Cement Compressive Strength by Combining Dynamic Models of Neural Networks

This study aimed at developing models predicting cement strength based on shallow neural networks (ANN) using exclusively industrial data.The models used physical, chemical, and early strength results to forecast those for 28-and 7-day.Neural networks were trained dynamically for a movable period and then used for a future period of at least one day.The study includes nine types of activation functions.The algorithms use the root mean square errors of testing sets (RMSE Future ) and their robustness as optimization criteria.The RMSE Future of the best models with optimum ANNs was in the range of 1.36 MPa to 1.63 MPa, which is near or within the area of long-term repeatability of a very competent laboratory.Continuous application of the models in actual conditions of a cement plant in the long-term showed a performance at least equivalent to that calculated during the design step.
Self–Training With Quantile Errors for Multivariate Missing Data Imputation for Regression Problems in Electronic Medical Records: Algorithm Development Study

When using machine learning in the real world, the missing value problem is the first problem encountered. Methods to impute this missing value include statistical methods such as mean, expectation-maximization, and multiple imputations by chained equations (MICE) as well as machine learning methods such as multilayer perceptron, k-nearest neighbor, and decision tree.The objective of this study was to impute numeric medical data such as physical data and laboratory data. We aimed to effectively impute data using a progressive method called self-training in the medical field where training data are scarce.In this paper, we propose a self-training method that gradually increases the available data. Models trained with complete data predict the missing values in incomplete data. Among the incomplete data, the data in which the missing value is validly predicted are incorporated into the complete data. Using the predicted value as the actual value is called pseudolabeling. This process is repeated until the condition is satisfied. The most important part of this process is how to evaluate the accuracy of pseudolabels. They can be evaluated by observing the effect of the pseudolabeled data on the performance of the model.In self-training using random forest (RF), mean squared error was up to 12% lower than pure RF, and the Pearson correlation coefficient was 0.1% higher. This difference was confirmed statistically. In the Friedman test performed on MICE and RF, self-training showed a P value between .003 and .02. A Wilcoxon signed-rank test performed on the mean imputation showed the lowest possible P value, 3.05e-5, in all situations.Self-training showed significant results in comparing the predicted values and actual values, but it needs to be verified in an actual machine learning system. And self-training has the potential to improve performance according to the pseudolabel evaluation method, which will be the main subject of our future research.
Tricks for Training Sparse Translation Models

Multi-task learning with an unbalanced data distribution skews learning towards high resource tasks, especially when model capacity is fixed and fully shared across all tasks.Sparse scaling architectures, such as BASE-Layers, provide flexible mechanisms for tasks to have a variable number of parameters, which can be useful to counterbalance skewed data distributions.However, we find that that BASELayers sparse model for multilingual machine translation can perform poorly out of the box, and propose two straightforward techniques to mitigate this -a temperature heating mechanism and dense pre-training.Overall, these methods improve performance on two multilingual translation benchmarks compared to standard BASELayers and dense scaling baselines, and in combination, more than 2x model convergence speed.
Anomaly Detection for Diagnosing Failures in a Centrifugal Compressor Train

Predicting machine failures is of the utmost importance in industrial systems as it can turn expensive crashes and repair costs into affordable maintenance costs. To this end, this paper presents preliminary work for detecting failures in a centrifugal compressor train based on sensorial data. We show the detection capabilities of a two-step process consisting of: (1) a preprocessing step to reduce the dimensionality of the input data using Principal Component Analysis, and (2) an anomaly detection step using the Mahalanobis distance to detect anomalous observations on the sensors’ data. The experiments using real-world data demonstrate the feasibility of our approach and the ability of the method to detect the failures eight days in advance.
Progressive Kernel Extreme Learning Machine for Food Image Analysis via Optimal Features from Quality Resilient CNN

Recently, food recognition has received more research attention for mHealth applications that use automated visual-based methods to assess dietary intake. The goal is to improve the food diaries by addressing the challenges faced by existing methodologies. In addition to the classical challenge of the absence of rigid food structure and intra-class variations, food diaries employing deep networks trained with pristine images are susceptible to quality variations in real-world conditions of image acquisition and transmission. Similarly, existing progressive classifiers that use visual features via a convolutional neural network (CNN) classify food categories and cannot detect food ingredients. We aim to provide a system that selects the optimal subset of features from quality resilient CNNs and subsequently incorporates the parallel type of classification to tackle such challenges. The first progressive classifier recognizes food categories, and its multilabel extension detects food ingredients. Following this idea, after extracting features from the quality resilient category and ingredient CNN models by fine-tuning it on synthetic images generated using the novel online data augmentation method random iterative mixup. Our feature selection strategy uses the Shapley additive explanation (SHAP) values from the gradient explainer to select the best features. Then, novel progressive kernel extreme learning machine (PKELM) is exploited to cater to domain variations due to quality distortions, intra-class variations, and so forth, by remodeling the network structure based on activity value with the nodes. PKELM extension for multilabel classification detects ingredients by employing a bipolar step function to process test output and then selecting the column labels of the resulting matrix with a value of one. Moreover, during online learning, the PKELM novelty detection mechanism can label unlabeled instances and detect noisy samples. Experimental results showed superior performance on an integrated set of measures for seven publicly available food datasets.
Agnostic Dolinar receiver for coherent-state classification

We consider the problem of discriminating quantum states, where the task is to distinguish two different quantum states with a complete classical knowledge about them, and the problem of classifying quantum states, where the task is to distinguish two classes of quantum states where no prior classical information is available but a finite number of physical copies of each class is given. In the case the quantum states are represented by coherent states of light, we identify intermediate scenarios where partial prior information is available. We evaluate an analytical expression for the minimum error when the quantum states are opposite and a prior on the amplitudes is known. Such a threshold is attained by complex positive operator-valued measurements that involve a highly nonlinear optical procedure. A suboptimal procedure that can be implemented with current technology is presented that is based on a modification of the conventional Dolinar receiver. We study and compare the performance of the scheme under different assumptions on the prior information available.
Search Engine Optimization (SEO) As A Promotional Media On Google Search

the current era, people's interest in using the internet is getting higher, this is because internet media has become an informative tool that can be accessed at any time. In searching for information, internet users usually use search engines. One of them is Google, the most widely used search engine. To find information in the Google search engine, we must use keywords. With SEO technique the website will be easy to find. A website optimization based on keywords, its implementation using On-page SEO and Off-page SEO mechanisms. On-page SEO is changing web pages according to the target keywords. By paying attention to important elements such as meta title, meta description, heading tags, text format, alt text, content, robot.txt, and sitemap.xml. Off-page SEO is a method that is carried out outside the website, creating relevant backlinks to the targeted website. This method can make the website as an organic promotional medium that brings traffic. In this research applying the concept of SEO both On-page and Off-page, the results obtained that there was a significant increase in traffic and based on the results of using the User Acceptance Test (UAT) with five aspects of component quality, this SEO support system can help in analyzing the website and as many as 82.3% of respondents said that the system built was very good
Performance Analysis of K-Nearest Neighbor Classification Algorithms for Bank Loan Sectors

An attempt has been made to develop an algorithm for banks to check the credibility of borrowers to avoid nonperformance assets. People move towards different banks for loan purpose to fulfil their financial needs. Approaching bank for loan is increasing day by day mainly for child marriage, education, agriculture, business, home loan etc. Some people take the loan and they won’t pay back in time or some will move out of the country without any intimation, so that bank will go in loss. Even now in covid-19 pandemic many industries were closed but they need to give salary to the employees, need to pay rent and electricity bills too for that they will approach bank for loan. For all these cases bank first need to analyse their Credit Information Bureau India Limited score and check whether they had done loan repayments in appropriate time or not. In the present work the effectiveness of K nearest neighbor algorithm were analysed. This research were carried out using python. The accuracy of this classifier is analysed using following metrics such as Jaccard index, F1-score and LogLoss. This helps to find the potential of the customer which is much higher than the data mining classification algorithm and thus it helps in sanctioning loans.
Colaizzi's Methods in Descriptive Phenomenology: Basis of A Filipino Novice Researcher

Descriptive phenomenology is a common methodology employed in social science research to investigate and describe people's lived experiences. It is both a philosophy and a scientific technique, and it has undergone several modifications as it expanded from the original European movement to encompass the American movement. This paper discussed and explained the process in applying Colaizzi’s method in descriptive phenomenological research under the field of education. This paper used a published research study, which the process of Colaizzi was utilized to give enough help in sorting, organizing, analyzing and presenting the narrative dataset. The main objective of using Colaizzi's descriptive phenomenology method was to generate an exhaustive description of the phenomena addressing the challenges of student moms in the midst of pandemic. Descriptive phenomenology is particularly beneficial for correctly describing the problems of student moms, and the result may be applied as the voice of this group of students during pandemic. This can be a basis of School Institutions in crafting policies as well the National Government.
Language Modelling via Learning to Rank

We consider language modelling (LM) as a multi-label structured prediction task by re-framing training from solely predicting a single ground-truth word to ranking a set of words which could continue a given context. To avoid annotating top-k ranks, we generate them using pre-trained LMs: GPT-2, BERT, and Born-Again models. This leads to a rank-based form of knowledge distillation (KD). We also develop a method using N-grams to create a non-probabilistic teacher which generates the ranks without the need of a pre-trained LM. We confirm the hypotheses: that we can treat LMing as a ranking task and that we can do so without the use of a pre-trained LM. We show that rank-based KD generally gives a modest improvement to perplexity (PPL) -- though often with statistical significance -- when compared to Kullback–Leibler-based KD. Surprisingly, given the naivety of the method, the N-grams act as competitive teachers and achieve similar performance as using either BERT or a Born-Again model teachers. Unsurprisingly, GPT-2 always acts as the best teacher. Using it and a Transformer-XL student on Wiki-02, rank-based KD reduces a cross-entropy baseline from 65.27 to 55.94 and against a KL-based KD of 56.70.