Reviewerly/exHarmony huggingface dataset:
Simple Mitigation Strategy for a Systematic Gate Error in IBMQ

In this paper, we report the observation and characterisation of a systematic error in the implementation of U 3 gates in the IBM quantum computers.By measuring the effect of this gate for various rotation angles the error appears as an over-rotation, whose magnitude does not correlate with IBM's cited errors calculated using Clifford randomized benchmarking.We propose a simple mitigation procedure to limit the effects of this error.We show that using a simple mitigation strategy one can obtain improved results in the observed value for the CHSH inequality, measured in a cloud-based quantum computer.This work highlights the utility of simple mitigation strategies for short-depth quantum circuits.
Implementation of Kanban Techniques in Software Development Process: An Empirical Study Based on Benefits and Challenges

It has been observed that there is an increasing trend of implementing Lean Techniques as a whole in general and Kanban in particular in the software development process all across the globe to reap the proclaimed advantages of these techniques as documented in the available literature and verified by the Lean practitioners. The main goal of this paper is to highlight the present state of implementation and usage of Kanban techniques in the software development process, its obtained and to-be-obtained benefits, challenges that are being faced by the software development companies and the reason(s) of motivation that compels the organization to adopt Kanban technique. Furthermore, the solutions for challenges identified during the study will also be suggested. In order to achieve this, an empirical study has been conducted encompassing a survey followed by complementing thematic interview sessions with 241 employees of 67 software companies of Pakistan. The companies involved in the empirical study have reasonable years of experience in practicing Agile and Lean techniques. Generally, the obtained results depict that the usage of Kanban techniques is rather positive and appreciative; however, the challenges that are being faced in Kanban adoption are lack of specialized training and experience pertaining to its usage and a too conventional organizational culture.
ReadTwice: Reading Very Large Documents with Memories

Knowledge-intensive tasks such as question answering often require assimilating information from different sections of large inputs such as books or article collections.We propose READTWICE 1 , a simple and effective technique that combines several strengths of prior approaches to model long-range dependencies with Transformers.The main idea is to read text in small segments, in parallel, summarizing each segment into a memory table to be used in a second read of the text.We show that the method outperforms models of comparable size on several question answering (QA) datasets and sets a new state of the art on the challenging NarrativeQA task, with questions about entire books.
Learning Neural Network Subspaces

Recent observations have advanced our understanding of the neural network optimization landscape, revealing the existence of (1) paths of high accuracy containing diverse solutions and (2) wider minima offering improved performance. Previous methods observing diverse paths require multiple training runs. In contrast we aim to leverage both property (1) and (2) with a single method and in a single training run. With a similar computational cost as training one model, we learn lines, curves, and simplexes of high-accuracy neural networks. These neural network subspaces contain diverse solutions that can be ensembled, approaching the ensemble performance of independently trained networks without the training cost. Moreover, using the subspace midpoint boosts accuracy, calibration, and robustness to label noise, outperforming Stochastic Weight Averaging.
TPACK of Prospective Mathematics Teachers at an Early Stage of Training

The objective of this work is to present the results of an investigation on the technological, pedagogical and content knowledge evidenced by mathematics teachers in the second year of initial training (preservice) at the Universidad Nacional, from the TPACK model perspective, on the subject of functions. The research has a qualitative approach with an interpretive hermeneutical stance. A sample of 27 teachers in training who were enrolled in courses related to the three base domains of the TPACK model (pedagogical, technological and content of quadratic function) during the first semester (I cycle) of 2020 was used. Three instruments with situations involving the quadratic function were created to determine students’ knowledge. They were also asked to design their own situation to teach an element linked to this topic, which was then shared and evaluated by their peers. The results show that participants possess instrumental dominance over the basic forms of knowledge underlying the model. It is concluded that, although participants have already experimented in courses related to these forms of knowledge, there is insufficient evidence to ascertain that their current knowledge would allow them to integrate technologies as a didactic resource within the teaching of the subject of quadratic function.
Technical Question Answering across Tasks and Domains

Building automatic technical support system is an important yet challenge task.Conceptually, to answer a user question on a technical forum, a human expert has to first retrieve relevant documents, and then read them carefully to identify the answer snippet.Despite huge success the researchers have achieved in coping with general domain question answering (QA), much less attentions have been paid for investigating technical QA.Specifically, existing methods suffer from several unique challenges (i) the question and answer rarely overlaps substantially and (ii) very limited data size.In this paper, we propose a novel framework of deep transfer learning to effectively address technical QA across tasks and domains.To this end, we present an adjustable joint learning approach for document retrieval and reading comprehension tasks.Our experiments on the TechQA demonstrates superior performance compared with state-of-the-art methods.How many provinces did the Ottoman empire contain in 17th century?... … At the beginning of the 17th century the Ottoman empire contained 32 provinces.Some of these were later absorbed into the Ottoman Empire, while others … … How can uninstall Data Studio 3.1.1where Control Panel uninstall process gets an error?Question We are able to install Data Studio (DS) 4.1.2successfully but unable to uninstall the existing Data Studio 3.1.1.When uninstall Data Studio 3.1.1from Control Panel, it raises an error message pop-up window and can not uninstall it.Here is the message: |Java Virtual Machine Launcher| X Could not find the main class: com.zerog.lax.LAX.Program will exit.How can uninstall Data Studio 3.1.1where Control Panel process gets an error?Cause It is an known behavior/limitation.Answer It may be happened where two versions Data Studio 3.1.1and 4.1.2installed machine.Here is an workaround.Please try to uninstall all products including Install Manager (IM) then reinstall IM and Data Studio 4.1.2.
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization

It is well-known that stochastic gradient noise (SGN) acts as implicit regularization for deep learning and is essentially important for both optimization and generalization of deep networks. Some works attempted to artificially simulate SGN by injecting random noise to improve deep learning. However, it turned out that the injected simple random noise cannot work as well as SGN, which is anisotropic and parameter-dependent. For simulating SGN at low computational costs and without changing the learning rate or batch size, we propose the Positive-Negative Momentum (PNM) approach that is a powerful alternative to conventional Momentum in classic optimizers. The introduced PNM method maintains two approximate independent momentum terms. Then, we can control the magnitude of SGN explicitly by adjusting the momentum difference. We theoretically prove the convergence guarantee and the generalization advantage of PNM over Stochastic Gradient Descent (SGD). By incorporating PNM into the two conventional optimizers, SGD with Momentum and Adam, our extensive experiments empirically verified the significant advantage of the PNM-based variants over the corresponding conventional Momentum-based optimizers.
Conceptual Review on Digital Transformation of Higher Education

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Modeling Perceptual Learning of Abstract Invariants

We present the beginnings of a model of the human capacity to learn abstract invariants, such as square. The model is founded on four primary assumptions, which we believe to be neurally plausible and generic: Metric space, Topology, Comparison operations (subtraction, greater-than/less-than), and Extraction of vertices. The model successfully learns to discriminate simple planar quadrilaterals, and generalizes that learning across variations in viewpoint and modest variations in shape.
A Sharp Leap from Quantified Boolean Formula to Stochastic Boolean Satisfiability Solving

Stochastic Boolean Satisfiability (SSAT) is a powerful representation for the concise encoding of quantified decision problems with uncertainty. While it shares commonalities with quantified Boolean formula (QBF) satisfiability and has the same PSPACE-complete complexity, SSAT solving tends to be more challenging as it involves expensive model counting, a.k.a. Sharp-SAT. To date, SSAT solvers, especially those imposing no restrictions on quantification levels, remain much lacking. In this paper, we present a new SSAT solver based on the framework of clause selection and cube distribution previously proposed for QBF solving. With model counting integrated and learning techniques strengthened, our solver is general and effective. Experimental results demonstrate the overall superiority of the proposed algorithm in both solving performance and memory usage compared to the state-of-the-art solvers on a number of benchmark formulas.
Benign Overfitting of Constant-Stepsize SGD for Linear Regression

There is an increasing realization that algorithmic inductive biases are central in preventing overfitting; empirically, we often see a benign overfitting phenomenon in overparameterized settings for natural learning algorithms, such as stochastic gradient descent (SGD), where little to no explicit regularization has been employed. This work considers this issue in arguably the most basic setting: constant-stepsize SGD (with iterate averaging) for linear regression in the overparameterized regime. Our main result provides a sharp excess risk bound, stated in terms of the full eigenspectrum of the data covariance matrix, that reveals a bias-variance decomposition characterizing when generalization is possible: (i) the variance bound is characterized in terms of an effective dimension and (ii) the bias bound provides a sharp geometric characterization in terms of the location of the initial iterate (and how it aligns with the data covariance matrix). We reflect on a number of notable differences between the algorithmic regularization afforded by (unregularized) SGD in comparison to ordinary least squares (minimum-norm interpolation) and ridge regression.
Sentiment Classification of Bank Clients’ Reviews Written in the Polish Language

It is estimated that approximately 80% of all data gathered by companies are text documents. This article is devoted to one of the most common problems in text mining, i.e. text classification in sentiment analysis, which focuses on determining the sentiment of a document. A lack of defined structure of the text makes this problem more challenging. This has led to the development of various techniques used in determining the sentiment of a document. In this paper, a comparative analysis of two methods in sentiment classification, a naive Bayes classifier and logistic regression, was conducted. Analysed texts are written in the Polish language and come from banks. The classification was conducted by means of a bag‑of‑n‑grams approach, where a text document is presented as a set of terms and each term consists of n words. The results show that logistic regression performed better.
Multi-class medical image segmentation using one-vs-rest graph cuts and majority voting

Purpose: Semi-automatic image segmentation is still a valuable tool in clinical applications since it retains the expert oversights legally required. However, semi-automatic methods for simultaneous multi-class segmentation are difficult to be clinically implemented due to the complexity of underlining algorithms. We purpose an efficient one-vs-rest graph cut approach of which the complexity only grows linearly as the number of classes increases. Approach: Given an image slice, we construct multiple one-vs-rest graphs, each for a tissue class, for inference of a conditional random field (CRF). The one-vs-rest graph cut is to minimize the CRF energy derived from regional and boundary class probabilities estimated from random forests to obtain a one-vs-rest segmentation. The final segmentation is obtained by fusing from those one-vs-rest segmentations based on majority voting. We compare our method to a well-used multi-class graph cut method, alpha-beta swap, and a fully connected CRF (FCCRF) method, in brain tumor segmentation of 20 high-grade tumor cases in 2013 MICCAI dataset. Results: Our method achieved mean Dice score of 0.83 for whole tumor, compared to 0.80 by alpha-beta swap and 0.79 by FCCRF. There was a performance improvement over alpha-beta swap by a factor of five. Conclusions: Our method utilizes the probabilistic-based CRF which can be estimated from any machine learning technique. Comparing to traditional multi-class graph cut, the purposed one-vs-rest approach has complexity that grows only linearly as the number of classes increases, therefore, our method can be applicable for both online semi-automatic and offline automatic segmentation in clinical applications.
Population and Global Search Improved Squirrel Search Algorithm for Feature Selection in Big Data Classification

The analytics techniques in Big Data are extensively employed as an alternative to generalized for data mining due to the huge volumes of large-scale high dimensional data.Feature selection techniques eradicate the redundant and inappropriate features to decrease the data dimensionality and increase the classifiers' efficiency.However, the traditional feature selection strategies are dearth of scalability to handle the unique characteristics of large-scale data and extract the valuable features within the restricted time.This article proposed a feature selection algorithm centered on the Population and Global Search Improved Squirrel Search Algorithm (PGS-ISSA) that tackles the problem of local optimum and reduce convergence rate in standard Squirrel Search Algorithm (SSA).The novelty of this proposed PGS-ISSA is the introduction of chaos theory to improve population initialization so that the search space is increased.Then the acceleration coefficients are used in the position update equations to improve the convergence rate in local search process while inertia weight is also applied to optimally balance the exploration and exploitation in SSA.PGS-ISSA employs the fitness function based on the minimum error rate for ensuring the selection of best features that improve the classification accuracy.The proposed PGS-ISSA based feature section algorithm is evaluated by using Support Vector Machine (SVM) classifier implemented in MATLAB tool to address the big data classification problem.The experiments performed on both small and large-scale datasets illustrated that the suggested PGS-ISSA enhances the classification accuracy by 1.7% to 5.4% better than other compared models through effective handling of the big data problems.The results obtained for the bigger Higgs dataset shows that the proposed PGS-ISSA achieved high performance than the standard SSA, existing ISSA models and other prominent optimizationbased feature selection algorithms with 64.72% accuracy, 67.3194% precision, 62.1528% recall, 62.3026% f-measure, 82.7226% specificity and consumed less time of 140.1366 seconds.PGS-ISSA also achieved comparatively better results for the other benchmark datasets with 0.3% to 6% improvement on statistical metrics and 10% to 25% reduction in execution time.
Technology for the development of the qualities of pedagogical competence in future teachers

The problem of creative and humanistic renewal of vocational pedagogical education in the Republic is considered. The current state of the theory and practice of professional pedagogical training of teachers is analyzed. The conceptual foundations of the formation and development of the creative competence of the future teacher are substantiated, taking into account the trends of modern socio-pedagogical reality, the prospects and strategies for the development of pedagogical science.
The Effectiveness of Alert Sounds for Electric Vehicles Based on Pedestrians’ Perception

One of the largest problems with electric vehicles is that they often go unnoticed by pedestrians due to the absence of noticeable noise generated by electric motors, which is a potential cause of accidents and collisions.Surprisingly, this positive property in terms of reducing the noise pollution is in fact becoming a road safety problem.In addition, with the promotion of electric traction vehicles due to new environmental policies and the current proliferation of personal mobility vehicles, this problem could even be increased in the coming years.Therefore, the future global road regulation has included aspects on noise and warning sounds that electric vehicles must emit in the years to come.However, despite the requirements, no specific signal type or many other features have been established.Only the emission levels have been set (56-75 dB).Consequently, within the framework of this problem, this article evaluates the acoustic characteristics of the sound that should be emitted by electric vehicles so that pedestrians can easily detect them and the optimal sound pressure level they should emit to not unnecessarily raise noise pollution levels, concluding that the emission limits established are excessive in certain scenarios and that optimal warning sounds must be focused on electronically imitating combustion engine noises.
Facial Expressions Recognition Via CNNCraft-net for Static RGB Images

Facial Expression Recognition (FER) is one of the most important research problems in computer vision and Artificial Intelligence (AI) due to its potential applications, many studies were proposed for the FER, whether based on using handcrafted (Craft) features with traditional machine learning techniques or using end to end convolution neural network (CNN).In this paper, we proposed a new model called CNNCraft-net based on combining the advantages of CNN and traditional models by concatenating features outputs from CNN, autoencoder, and handcrafted features such as scale-invariant feature transform (SIFT), speed up robust feature (SURF) and Oriented Fast Rotated Brief (ORB), computed by the bag of visual words (BOVW) to recognize eight facial expressions for static RGB images.For the comparative analysis, multiple metrics were used such as Accuracy, Loss, F-measure, precision, and recall.The high imbalanced AffectNet and FER2013 datasets were used to evaluate the proposed model where the proposed model achieves accuracy 61.9% for eight expressions and 65% for seven expressions for AffectNet and 69% for FER2013.
On Orthogonality Constraints for Transformers

Orthogonality constraints encourage matrices to be orthogonal for numerical stability.These plug-and-play constraints, which can be conveniently incorporated into model training, have been studied for popular architectures in natural language processing, such as convolutional neural networks and recurrent neural networks.However, a dedicated study on such constraints for transformers has been absent.To fill this gap, this paper studies orthogonality constraints for transformers, showing the effectiveness with empirical evidence from ten machine translation tasks and two dialogue generation tasks.For example, on the large-scale WMT'16 En→De benchmark, simply plugging-and-playing orthogonality constraints on the original transformer model (Vaswani et al., 2017) increases the BLEU from 28.4 to 29.6, coming close to the 29.7 BLEU achieved by the very competitive dynamic convolution (Wu et al., 2019).
Sentiment Analysis and Topic Modeling of Indonesian Public Conversation about COVID-19 Epidemics on Twitter

The World Health Organization (WHO) declared the COVID-19 outbreak has resulted in more than six million confirmed cases and more than 371,000 deaths globally on June 1, 2020. The incident sparked a flood of scientific research to help society deal with the virus, both inside and outside the medical domain. Research related to public health analysis and public conversations about the spread of COVID-19 on social media is one of the highlights of researchers in the world. People can analyze information from social media as supporting data about public health. Analyzing public conversations will help the relevant authorities understand public opinion and information gaps between them and the public, helping them develop appropriate emergency response strategies to address existing problems in the community during the pandemic and provide information on the population's emotions in different contexts. However, research related to the analysis of public health and public conversations was so far conducted only through supervised analysis of textual data. In this study, we aim to analyze specifically the sentiment and topic modeling of Indonesian public conversations about the COVID-19 on Twitter using the NLP technique. We applied some methods to analyze the sentiment to obtain the best classification method. In this study, the topic modeling was carried out unsupervised using Latent Dirichlet Allocation (LDA). The results of this study reveal that the most frequently discussed topic related to the COVID-19 pandemic is economic issues.
ReliefE: feature ranking in high-dimensional spaces via manifold embeddings

Abstract Feature ranking has been widely adopted in machine learning applications such as high-throughput biology and social sciences. The approaches of the popular Relief family of algorithms assign importances to features by iteratively accounting for nearest relevant and irrelevant instances. Despite their high utility, these algorithms can be computationally expensive and not-well suited for high-dimensional sparse input spaces. In contrast, recent embedding-based methods learn compact, low-dimensional representations, potentially facilitating down-stream learning capabilities of conventional learners. This paper explores how the Relief branch of algorithms can be adapted to benefit from (Riemannian) manifold-based embeddings of instance and target spaces, where a given embedding’s dimensionality is intrinsic to the dimensionality of the considered data set. The developed ReliefE algorithm is faster and can result in better feature rankings, as shown by our evaluation on 20 real-life data sets for multi-class and multi-label classification tasks. The utility of ReliefE for high-dimensional data sets is ensured by its implementation that utilizes sparse matrix algebraic operations. Finally, the relation of ReliefE to other ranking algorithms is studied via the Fuzzy Jaccard Index.