Reviewerly/exHarmony huggingface dataset:
From Uncertainty to Self-Efficacy-Perspectives of Pre-Service Teacher’s on Their Practical Experience during the ‘Corona Year’

The outbreak of the COVID-19 pandemic in Israel at the middle of March 2020 has disrupted all aspects of life.Schools, colleges and other higher education institutions were closed most of the time.Teaching and learning continued online in an ERT (emergency remote teaching) format.The situation confronted teachers and students with entirely new challenges of uncertainty.The present study focuses on the perspectives of pre-service teachers in Israel regarding their practical experience training during the times of the covid 19 pandemic.The research hypothesis assumed that the complications caused by the pandemic will have a negative impact on how the pre-service teachers assess the quality of their practical training, which would be reflected in difficulties in their integration at work, their sense of efficacy in teaching, their teaching methods and their reluctance to continue teaching.The study examines the correlations between the assessment of their practical training experience and their self-reported self-efficacy, use of various teaching methods, integration at work, and the willingness to remain in the teaching profession.All at the time of the pandemic.Our main findings indicate that they assess their practical training as good (3.69); have a high sense of professional self-efficacy (4.06); use a variety of teaching methods; and express their desire to continue teaching (3.97).Overall, the findings indicate correlations between the interns' positive assessment of their practical training and their high self-efficacy, their use of a variety of teaching methods, and their desire to continue teaching.
Fixing usability smells of mobile apps: A multi-objective early refactoring recommendation approach

Abstract Usability is crucial for mobile app success. Usability evaluation and correction of Mobile User Interface (MUI) is fundamental phase to discover the usability smells and improve its quality. However, it continues to be a hard, expensive, and generally it is ignored. Several existing works depend on a subjective assessment of usability defects which relies on users’ feedback, that leads to time-consuming, human-centric, and error-prone task. Hence, recent works proposed tools for collection and analysis log user interaction when using the mobile application to understand user behavior and identify usability smells. But, these tools treat the defects with the same importance and rarely propose recommendations to help evaluator to improve their MUIs. It still also needed to identify concrete usability defects and fixed riskiest usability smells. In addition, most existing methods proposed to assess the usability of once the applications has been developed, which led to a lot of reworking by the designer because these changes can affect the development phases.In this paper, we propose an automated approach based on correction rules which combine literature evaluation metrics with usability smells to define recommended refactoring of MUI.To this end, we consider this refactoring recommendation as a multi-objective optimization problem. The purpose of this optimization is to maximize the number of refactoring per individual while minimizing the number of features per rule, the number of rules by solution, the misclassification rate, the information gain and improving the MUI quality in terms of guidance and cohesion coverage and minimizing the complexity of MUI. Moreover, we leaded a comparative study of four evolutionary algorithms to solve this optimization problem. The results prove the efficiency of the IBEA in terms of generating the most accurate refactoring rules with an average of 0.92 of AUC.This paper applied an evolutionary algorithm to recommend the best refactoring operations to enhance the quality of MUI. The results are promising. Our search-based algorithm successfully searches the best trade-off between eight objectives and recommending the most accurate refactoring rules compared with other studied algorithms.
Recipe for Fast Large-scale SVM Training: Polishing, Parallelism, and more RAM!

Support vector machines (SVMs) are a standard method in the machine learning toolbox, in particular for tabular data. Non-linear kernel SVMs often deliver highly accurate predictors, however, at the cost of long training times. That problem is aggravated by the exponential growth of data volumes over time. It was tackled in the past mainly by two types of techniques: approximate solvers, and parallel GPU implementations. In this work, we combine both approaches to design an extremely fast dual SVM solver. We fully exploit the capabilities of modern compute servers: many-core architectures, multiple high-end GPUs, and large random access memory. On such a machine, we train a large-margin classifier on the ImageNet data set in 24 minutes.
The fundamental group of the space $\Omega_n(m)$

In the present paper the spaces $\Omega_n(m)$ are considered. The spaces $\Omega_n(m)$, introduced in 2018 by A.M. Pasko and Y.O. Orekhova, are the generalization of the spaces $\Omega_n$ (the space $\Omega_n(2)$ coincides with $\Omega_n$). The investigation of homotopy properties of the spaces $\Omega_n$ has been started by V.I. Ruban in 1985 and followed by V.A. Koshcheev, A.M. Pasko. In particular V.A. Koshcheev has proved that the spaces $\Omega_n$ are simply connected. We generalized this result proving that all the spaces $\Omega_n(m)$ are simply connected. In order to prove the simply connectedness of the space $\Omega_n(m)$ we consider the 1-skeleton of this space. Using 1-cells we form the closed ways that create the fundamental group of the space $\Omega_n(m)$. Using 2-cells we show that all these closed ways are equivalent to the trivial way. So the fundamental group of the space $\Omega_n(m)$ is trivial and the space $\Omega_n(m)$ is simply connected.
Current Status of Pharmacist-run Personal Broadcasting and Pharmacists’ Perceptions on Self-regulation by Korean Pharmaceutical Association

With the rapid penetration of internet-based communication into all industries, YouTube®, a video-sharing platform, has come to be widely used in the healthcare field. This study aims to investigate the current status of pharmacist-run personal broadcast (PRPB) in Korea and explore the underlying perceptions held by pharmacists. YouTube searches made from May to June 2020 and June 2022 were analyzed, followed by a cross-sectional questionnaire survey during July 2020. A 13-item questionnaire was distributed via social media channels to recruit pharmacists using the convenience sampling method. Currently, there are a total of 91 PRPB channels on YouTube, most of which were started after 2019. Of the total 1267 contents, health functional foods (32.4%) and over the counter products (29.3%) had the highest proportions. Among the 218 respondents of the questionnaire survey, 49.1% were working in community pharmacies, and only 2.8% had PRPB experience. They showed higher positive response (42.2%) than negative (33.5%) for the public's exposure to PRPB, and 77.5% of the respondents perceived that social influence of PRPB will grow bigger. Their concerns about PRPB were mainly disclosure of biased information and possibility to lead to product purchase (75.2%) and spreading unverified information (70.2%). In addition, 70.6% of the respondents agreed with the necessity of self-regulation of PRPB by the Korean Pharmaceutical Association and chose the operation of a civil petition counter to report the transmission of false or incorrect information on YouTube (62.8%) and composition of deliberation committee and periodic monitoring (53.2%) as regulatory methods. Perceived necessity of self-regulation was significantly higher in the above-50s age group than in the under-40s group (p&lt;0.05). Pharmacists interested in PRPB should deliver accurate information as drug information providers and abide by professional ethics and public responsibility.
Memory-Based Label-Text Tuning for Few-Shot Class-Incremental Learning

Few-shot class-incremental learning(FSCIL) focuses on designing learning algorithms that can continually learn a sequence of new tasks from a few samples without forgetting old ones. The difficulties are that training on a sequence of limited data from new tasks leads to severe overfitting issues and causes the well-known catastrophic forgetting problem. Existing researches mainly utilize the image information, such as storing the image knowledge of previous tasks or limiting classifiers updating. However, they ignore analyzing the informative and less noisy text information of class labels. In this work, we propose leveraging the label-text information by adopting the memory prompt. The memory prompt can learn new data sequentially, and meanwhile store the previous knowledge. Furthermore, to optimize the memory prompt without undermining the stored knowledge, we propose a stimulation-based training strategy. It optimizes the memory prompt depending on the image embedding stimulation, which is the distribution of the image embedding elements. Experiments show that our proposed method outperforms all prior state-of-the-art approaches, significantly mitigating the catastrophic forgetting and overfitting problems.
Improving Low-Resource Speech Recognition with Pretrained Speech Models: Continued Pretraining vs. Semi-Supervised Training

Self-supervised Transformer based models, such as wav2vec 2.0 and HuBERT, have produced significant improvements over existing approaches to automatic speech recognition (ASR). This is evident in the performance of the wav2vec 2.0 based pretrained XLSR-53 model across many languages when fine-tuned with available labeled data. However, the performance from finetuning these models can be dependent on the amount of in-language or similar-to-in-language data included in the pretraining dataset. In this paper we investigate continued pretraining (CoPT) with unlabeled in-language audio data on the XLSR-53 pretrained model in several low-resource languages. CoPT is more computationally efficient than semi-supervised training (SST), the standard approach of utilizing unlabeled data in ASR, since it omits the need for pseudo-labeling of the unlabeled data. We show CoPT results in word error rates (WERs), equal to or slightly better than using SST. In addition, we show that using the CoPT model for pseudo-labeling, and using these labels in SST, results in further improvements in WER.
Derivatives of Complete Weight Enumerators and New Balance Principle of Binary Self-Dual Codes

Let H be the standard Hadamard matrix of order two and let K=2^{-1/2}H. It is known that the complete weight enumerator $\ W$ of a binary self-dual code of length $n$ is an eigenvector corresponding to an eigenvalue 1 of the Kronecker power $K^{[n]}.$ For every integer $t$ in the interval [0,n] we define the derivative of order $t$, $W_{},$ of $W$ in such a way that $W_{}$ is in the eigenspace of $\ 1$ of the matrix $K^{[n-t]}.$ For large values of $t,$ $W_{}$ contains less information about the code but has smaller length while $W_{&lt;0&gt;}=W$ completely determines the code. We compute the derivative of order $n-5$ for the extended Golay code of length 24, the extended quadratic residue code of length 48, and the putative [72,24,12] code and show that they are in the eigenspace of $\ 1$ of the matrix $% K^{[5]}.$ We use the derivatives to prove a new balance equation which involves the number of code vectors of given weight having 1 in a selected coordinate position. As an example, we use the balance equation to eliminate some candidates for weight enumerators of binary self-dual codes of length eight.
Face Mask Detection using Open-Source Computer Vision Library and Scikit-Learn using Machine Learning

In this pandemic has rapidly affected our day to-day life disrupting the world trade and movements. Wearing a protective face mask has become anormal. In the near future, many public service providers will ask the customers to wear masks correctly to avail of their services. Therefore, face mask detection has become a crucial task to help global security. This project presents a simplified approach to achieve this purpose using some basic Machine Learning packages like OpenCV and Scikit-Learn. The proposed method detects the face from the image correctly and then identifies it has a mask on it or not. As a surveillance task performer, it can also detect a face along with the mask in motion. This method attains accuracy almost up to 90% on two data sets. We explore optimized values of parameters using the Viola Jones detection framework used to detect the presence of masks correctly without causing over-fitting.
Convolutional layers are equivariant to discrete shifts but not continuous translations

The purpose of this short and simple note is to clarify a common misconception about convolutional neural networks (CNNs). CNNs are made up of convolutional layers which are shift equivariant due to weight sharing. However, convolutional layers are not translation equivariant, even when boundary effects are ignored and when pooling and subsampling are absent. This is because shift equivariance is a discrete symmetry while translation equivariance is a continuous symmetry. This fact is well known among researchers in equivariant machine learning, but is usually overlooked among non-experts. To minimize confusion, we suggest using the term `shift equivariance' to refer to discrete shifts in pixels and `translation equivariance' to refer to continuous translations.
A Biomedical Pipeline to Detect Clinical and Non-Clinical Named Entities

There are a few challenges related to the task of biomedical named entity recognition, which are: the existing methods consider a fewer number of biomedical entities (e.g., disease, symptom, proteins, genes); and these methods do not consider the social determinants of health (age, gender, employment, race), which are the non-medical factors related to patients' health. We propose a machine learning pipeline that improves on previous efforts in the following ways: first, it recognizes many biomedical entity types other than the standard ones; second, it considers non-clinical factors related to patient's health. This pipeline also consists of stages, such as preprocessing, tokenization, mapping embedding lookup and named entity recognition task to extract biomedical named entities from the free texts. We present a new dataset that we prepare by curating the COVID-19 case reports. The proposed approach outperforms the baseline methods on five benchmark datasets with macro-and micro-average F1 scores around 90, as well as our dataset with a macro-and micro-average F1 score of 95.25 and 93.18 respectively.
Learning to Increase the Power of Conditional Randomization Tests

The model-X conditional randomization test is a generic framework for conditional independence testing, unlocking new possibilities to discover features that are conditionally associated with a response of interest while controlling type-I error rates. An appealing advantage of this test is that it can work with any machine learning model to design powerful test statistics. In turn, the common practice in the model-X literature is to form a test statistic using machine learning models, trained to maximize predictive accuracy with the hope to attain a test with good power. However, the ideal goal here is to drive the model (during training) to maximize the power of the test, not merely the predictive accuracy. In this paper, we bridge this gap by introducing, for the first time, novel model-fitting schemes that are designed to explicitly improve the power of model-X tests. This is done by introducing a new cost function that aims at maximizing the test statistic used to measure violations of conditional independence. Using synthetic and real data sets, we demonstrate that the combination of our proposed loss function with various base predictive models (lasso, elastic net, and deep neural networks) consistently increases the number of correct discoveries obtained, while maintaining type-I error rates under control.
ANEC: An Amharic Named Entity Corpus and Transformer Based Recognizer

Named Entity Recognition is an information extraction task that serves as a preprocessing step for other natural language processing tasks, such as machine translation, information retrieval, and question answering. Named entity recognition enables the identification of proper names as well as temporal and numeric expressions in an open domain text. For Semitic languages such as Arabic, Amharic, and Hebrew, the named entity recognition task is more challenging due to the heavily inflected structure of these languages. In this paper, we present an Amharic named entity recognition system based on bidirectional long short-term memory with a conditional random fields layer. We annotate a new Amharic named entity recognition dataset (8,070 sentences, which has 182,691 tokens) and apply Synthetic Minority Over-sampling Technique to our dataset to mitigate the imbalanced classification problem. Our named entity recognition system achieves an F_1 score of 93%, which is the new state-of-the-art result for Amharic named entity recognition.
review on fuzzy multi-criteria decision-making methods in medical diagnosis

In a real-world computing environment, the information is not complete, precise, and certain, making it very difficult to derive an actual decision. Uncertainty is a factor that makes the decision-making process more difficult in medical diagnosis. Everyone is different from the other at the physical level as well as mental level, symptoms of a disease expressed by patients are linguistic in nature and vary from person to person. That is why Decision making in medical diagnosis is becoming a vast research field in medical science. In medical diagnosis, generally, uncertainty (vagueness or imprecision) arises due to patients’ vague linguistic expression of their problems to medical experts. The field of medicine and decision-making are the most fruitful and interesting areas of applications of fuzzy set theory. In the present paper, we study the importance of different fuzzy sets and fuzzy multi-criteria decision-making methods in the field of medical diagnosis.
HISTORICAL ANALYSIS AND MANAGEMENT ON FLOODS DURING PANDEMIC COVID-19 IN DKI JAKARTA INDONESIA

A flood is an event that occurs due to an accumulation of water that falls and cannot be accommodated by the ground. Thestudy aims to find out the history of floods in DKI Jakarta and the handling of floods amid the current Covid-19 pandemic. Natural events, such as floods, are not new in an urban area. The study discusses whyfloods that occur in urban areas, especially the DKI Jakarta area, are an annual cycle?.But there is a difference in 2020 and 2021 because of the flood, this time amid the Covid-19 pandemic, so extra handling is needed because if there is a crowd in the refugee camp it can spread the virus more widely. Thus, the government is doing everything it can to handle floods so that the community remains in a healthy body condition. The method used in the study is descriptive with a literature study approach, namely collecting data from reliable sources.Floods can be caused by two factors, namely natural causes and unnatural causes (from human activities).&#x0D;
Facial Expression Recognition System Using Haar Cascades Classifier

Abstract: Facial expression conveys non-verbal cues, which play a crucial role in social relations. Facial Expression Recognition is a significant yet challenging task, as we can use it to identify the emotions and the mental state of an individual. In this system, using image processing and machine learning, we compare the captured image with the trained dataset and then display the emotional state of the image. To design a robust facial feature recognition system, Local Binary Pattern(LBP) is used. We then assess the performance of the suggested system by using a database that is trained, with the help of Neural Networks. The results show the competitive classification accuracy of various emotions.
Large-scale Robustness Analysis of Video Action Recognition Models

We have seen a great progress in video action recognition in recent years. There are several models based on convolutional neural network (CNN) and some recent transformer based approaches which provide top performance on existing benchmarks. In this work, we perform a large-scale robustness analysis of these existing models for video action recognition. We focus on robustness against real-world distribution shift perturbations instead of adversarial perturbations. We propose four different benchmark datasets, HMDB51-P, UCF101-P, Kinetics400-P, and SSv2-P to perform this analysis. We study robustness of six state-of-the-art action recognition models against 90 different perturbations. The study reveals some interesting findings, 1) transformer based models are consistently more robust compared to CNN based models, 2) Pretraining improves robustness for Transformer based models more than CNN based models, and 3) All of the studied models are robust to temporal perturbations for all datasets but SSv2; suggesting the importance of temporal information for action recognition varies based on the dataset and activities. Next, we study the role of augmentations in model robustness and present a real-world dataset, UCF101-DS, which contains realistic distribution shifts, to further validate some of these findings. We believe this study will serve as a benchmark for future research in robust video action recognition.
Determination of Discounts Using K-Means Clustering with RFM Models in Retail Business

Intense competition in the business sector motivates every company to manage services to regular consumers to the fullest. Increase customer loyalty can be done by grouping customers into several groups and determine appropriate and effective marketing strategies for each group. This study aims to propose the right targeting of discounts that can increase customer loyalty in the retail business. Customer grouping uses data mining techniques with the Cross-Industry Standard Process for Data Mining (CRISP-DM) method, which is divided into six phases, namely business understanding, data understanding, data preparation, modelling, evaluation, and deployment. The formation of this cluster uses k-means clustering method and is based on RFM (recency, frequency, monetary) analysis. From the results of the silhouette test on 2734 transaction data from 210 customers of PT. XYZ from October 2019 to March 2020, three customer clusters were formed. From these three clusters, one cluster that has the best frequency and monetary values is chosen so that it is considered the worthiest group to be given a discount in order to maintain its loyalty.
Neural Networks for Path Planning

The scientific community is able to present a new set of solutions to practical problems that substantially improve the performance of modern technology in terms of efficiency and speed of computation due to the advancement in neural networks architectures. We present the latest works considering the utilization of neural networks in robot path planning. Our survey shows the contrast between different formulations of the problems that consider different inputs, outputs, and environments and how different neural networks architectures are able to provide solutions to all of the presented problems.