Reviewerly/exHarmony huggingface dataset:
L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library

Despite being the third most popular language in India, the Marathi language lacks useful NLP resources. Moreover, popular NLP libraries do not have support for the Marathi language. With L3Cube-MahaNLP, we aim to build resources and a library for Marathi natural language processing. We present datasets and transformer models for supervised tasks like sentiment analysis, named entity recognition, and hate speech detection. We have also published a monolingual Marathi corpus for unsupervised language modeling tasks. Overall we present MahaCorpus, MahaSent, MahaNER, and MahaHate datasets and their corresponding MahaBERT models fine-tuned on these datasets. We aim to move ahead of benchmark datasets and prepare useful resources for Marathi. The resources are available at https://github.com/l3cube-pune/MarathiNLP.
Avoiding barren plateaus via transferability of smooth solutions in Hamiltonian Variational Ansatz

A large ongoing research effort focuses on Variational Quantum Algorithms (VQAs), representing leading candidates to achieve computational speed-ups on current quantum devices. The scalability of VQAs to a large number of qubits, beyond the simulation capabilities of classical computers, is still debated. Two major hurdles are the proliferation of low-quality variational local minima, and the exponential vanishing of gradients in the cost function landscape, a phenomenon referred to as barren plateaus. Here we show that by employing iterative search schemes one can effectively prepare the ground state of paradigmatic quantum many-body models, circumventing also the barren plateau phenomenon. This is accomplished by leveraging the transferability to larger system sizes of iterative solutions, displaying an intrinsic smoothness of the variational parameters, a result that does not extend to other solutions found via random-start local optimization. Our scheme could be directly tested on near-term quantum devices, running a refinement optimization in a favorable local landscape with non-vanishing gradients.
Vietnamese Hate and Offensive Detection using PhoBERT-CNN and Social Media Streaming Data

Society needs to develop a system to detect hate and offense to build a healthy and safe environment. However, current research in this field still faces four major shortcomings, including deficient pre-processing techniques, indifference to data imbalance issues, modest performance models, and lacking practical applications. This paper focused on developing an intelligent system capable of addressing these shortcomings. Firstly, we proposed an efficient pre-processing technique to clean comments collected from Vietnamese social media. Secondly, a novel hate speech detection (HSD) model, which is the combination of a pre-trained PhoBERT model and a Text-CNN model, was proposed for solving tasks in Vietnamese. Thirdly, EDA techniques are applied to deal with imbalanced data to improve the performance of classification models. Besides, various experiments were conducted as baselines to compare and investigate the proposed model's performance against state-of-the-art methods. The experiment results show that the proposed PhoBERT-CNN model outperforms SOTA methods and achieves an F1-score of 67,46% and 98,45% on two benchmark datasets, ViHSD and HSD-VLSP, respectively. Finally, we also built a streaming HSD application to demonstrate the practicality of our proposed system.
Khmer printed character recognition using attention-based Seq2Seq network

This paper presents an end-to-end deep convolutional recurrent neural network solution for Khmer optical character recognition (OCR) task. The proposed solution uses a sequence-to-sequence (Seq2Seq) architecture with attention mechanism. The encoder extracts visual features from an input text-line image via layers of convolutional blocks and a layer of gated recurrent units (GRU). The features are encoded in a single context vector and a sequence of hidden states which are fed to the decoder for decoding one character at a time until a special end-of-sentence (EOS) token is reached. The attention mechanism allows the decoder network to adaptively select relevant parts of the input image while predicting a target character. The Seq2Seq Khmer OCR network is trained on a large collection of computer-generated text-line images for multiple common Khmer fonts. Complex data augmentation is applied on both train and validation dataset. The proposed model’s performance outperforms the state-of-art Tesseract OCR engine for Khmer language on the validation set of 6400 augmented images by achieving a character error rate (CER) of 0.7% vs 35.9%.
Facial expression recognition using HOG and LBP features with convolutional neural network

In computer vision, automatic facial expression recognition (FER) continued a difficult and interesting topic. The majority of extant techniques are based on traditional features descriptors such as local binary pattern (LBP) and histogram of oriented gradient (HOG), in which the classifier's hyperparameters are tailored to produce the best recognition accuracies across a single database or a small set of similar databases. This paper integrates the power of deep learning techniques with the LBP and HOG. The LBP and HOG are estimated from each image in the dataset. The resulting dataset is applied to a convolutional neural network (CNN). The architecture of this CNN constitutes three convolutional layers and three max-pooling layers. The output layers involve BatchNormalization, three dense layers, and two dropout layers. The proposed architecture is validated on the extended cohn-kanade dataset (CK+). We obtain improvement in the accuracy of the CNN model from 0.9593 to 0.967 and 0.975 after using the LBP and HOG respectively.
Learning to Model Editing Processes

Most existing sequence generation models produce outputs in one pass, usually left-to-right. However, this is in contrast with a more natural approach that humans use in generating content; iterative refinement and editing. Recent work has introduced edit-based models for various tasks (such as neural machine translation and text style transfer), but these generally model a single edit step. In this work, we propose modeling editing processes, modeling the whole process of iteratively generating sequences. We form a conceptual framework to describe the likelihood of multi-step edits, and describe neural models that can learn a generative model of sequences based on these multistep edits. We introduce baseline results and metrics on this task, finding that modeling editing processes improves performance on a variety of axes on both our proposed task and related downstream tasks compared to previous single-step models of edits.
Effectiveness evaluation of machine learning algorithms for breast cancer prediction

Breast cancer is becoming a global epidemic, affecting predominantly women. As a result, the number of people diagnosed with breast cancer is increasing every day. As a result, it is critical to have certain early detection methods in place that can assist patients in recognizing this condition at an early stage. Therefore, they might begin taking their medication to prevent the sickness from killing them. Different prediction approaches for early diagnosis of such diseases have been created in the machine learning fields. Those algorithms employ a variety of computational classifiers and claim to achieve satisfactory results in a few areas. However, no research was reached to determine which computationally sophisticated approach is more effective in detecting breast cancer. As a result, it is necessary to select the most effective strategy from the available options. This paper makes a contribution to the performance evaluation of 12 alternative classification strategies on datasets of breast cancer. The right explanations for the classifiers' dominance were investigated.
Assessing the Impact of Features on Probabilistic Forecasting of Photovoltaic Power Generation

Photovoltaic power generation has high variability and uncertainty because it is affected by uncertain factors such as weather conditions. Therefore, probabilistic forecasting is useful for optimal operation and risk hedging in power systems with large amounts of photovoltaic power generation. However, deterministic forecasting is the mainstay of photovoltaic generation forecasting; there are few studies on probabilistic forecasting and feature selection from weather or time-oriented features in such forecasting. In this study, prediction intervals were generated by the lower upper bound estimation using neural networks with two outputs to make probabilistic predictions. The objective was to improve prediction interval coverage probability (PICP), mean prediction interval width (MPIW), and loss, which is the integration of these two metrics, by removing unnecessary features through feature selection. When features with high gain were selected by random forests (RF), in the forecast of 14.7-kW PV systems, loss improved by 1.57 kW, PICP by 0.057, and MPIW by 0.12 kW on average over two weeks compared to the case where all features were used without feature selection. Therefore, the low gain features from RF act as noise in LUBE and reduce the prediction accuracy.
Discovering the Hidden Vocabulary of DALLE-2

We discover that DALLE-2 seems to have a hidden vocabulary that can be used to generate images with absurd prompts. For example, it seems that \texttt{Apoploe vesrreaitais} means birds and \texttt{Contarra ccetnxniams luryca tanniounons} (sometimes) means bugs or pests. We find that these prompts are often consistent in isolation but also sometimes in combinations. We present our black-box method to discover words that seem random but have some correspondence to visual concepts. This creates important security and interpretability challenges.
CCPIN: Classification and Combine Parallel Interaction Network for CTR Prediction

The study of feature interactions in deep neural network-based recommender systems has been a popular research area in industry and academic circles. However, the vast majority of parallel CTR prediction models do not classify the input features but instead feed them into the model. This way not only reduces the accuracy of the model but also ignores the effectiveness of learning individual feature interactions. In addition, the majority of parallel CTR prediction models only focus on the submodel intersections of their parallel models, ignoring the importance of the external intersection. To address the shortcomings, this paper proposes the CCPIN model on the basis of the XdeepFM model. In the CCPIN model, it can not only learn different category feature interactions but also learn individual feature interactions. Through the classification gate, adaptive features are maximized to improve the performance of the submodel. Through the Combine layer, the interaction of submodel results can be learned while retaining the original output. Through comparison experiments with other models on two datasets, it is demonstrated that the CCPIN model has an average increase of 0.93% in AUC and a decrease of 0.47% in Logloss compared to other models.
Scalable relevance ranking algorithm via semantic similarity assessment improves efficiency of medical chart review

Accurately assigning phenotype information to individual patients via computational phenotyping using Electronic Health Records (EHRs) has been seen as the first step towards enabling EHRs for precision medicine research. Chart review labels annotated by clinical experts, also known as “gold standard” labels, are essential for the development and validation of computational phenotyping algorithms. However, given the complexity of EHR systems, the process of chart review is both labor intensive and time consuming. We propose a fully automated algorithm, referred to as pGUESS, to rank EHR notes according to their relevance to a given phenotype. By identifying the most relevant notes, pGUESS can greatly improve the efficiency and accuracy of chart reviews. pGUESS uses prior guided semantic similarity to measure the informativeness of a clinical note to a given phenotype. We first select candidate clinical concepts from a pool of comprehensive medical concepts using public knowledge sources and then derive the semantic embedding vector (SEV) for a reference article (SEVref) and each note (SEVnote). The algorithm scores the relevance of a note as the cosine similarity between SEVnote and SEVref. The algorithm was validated against four sets of 200 notes that were manually annotated by clinical experts to assess their informativeness to one of three disease phenotypes. pGUESS algorithm substantially outperforms existing unsupervised approaches for classifying the relevance status with respect to both accuracy and scalability across phenotypes. Averaging over the three phenotypes, the rank correlation between the algorithm ranking and gold standard label was 0.64 for pGUESS, but only 0.47 and 0.35 for the next two best performing algorithms. pGUESS is also much more computationally scalable compared to existing algorithms. pGUESS algorithm can substantially reduce the burden of chart review and holds potential in improving the efficiency and accuracy of human annotation.
Detecting Digital Watermarking Image Attacks Using a Convolution Neural Network Approach

In scientific research, one of the most significant problems of recent years has been and continues to be the protection of digital material. The advancement of Internet technology has allowed for the illicit duplication, authentication, and distribution of digital material by unauthorized individuals. For this reason, a variety of watermarking systems have been investigated for a variety of purposes, including broadcast monitoring, intellectual property protection, content authentication, and copy control. There are various types of the watermarking image attacks that impact the quality of the images; therefore, it is critical to ensure that watermarked digital images can withstand these kinds of attacks. Hence, novelty of the proposed research is to develop approaches to detect these attacks which becomes very important to guarantee a sufficient quality of watermarking images. In this paper, a deep learning method based on a convolution neural network (CNN) algorithm was proposed to detect various types of watermarking attacks, namely, median filter, Gaussian filter, salt-and-pepper, average filter, motion blur, and no attack, to improve the watermarking quality. Evaluation metrics such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and the normalization correlation (NC) were employed to examine the invisibility and robustness of the watermarking images. The empirical results of the CNN model show good performance for detecting watermarking attacks with different sizes (256, 128, and 64). The accuracy percentage of the testing process was 98%. A highly efficient CNN approach was developed. Very high performance of NC was found in the detection of the salt-and-pepper attack (99.02%, 99.97%, and 99.49% with respect to watermarking image sizes of 256 × 256, 128 × 128, and 64 × 64, respectively). The study concludes that the CNN model is able to detect watermarking attacks successfully.
Bayesian statistics‐guided label refurbishment mechanism: Mitigating label noise in medical image classification

Abstract Purpose Deep neural networks (DNNs) have been widely applied in medical image classification, benefiting from its powerful mapping capability among medical images. However, these existing deep learning‐based methods depend on an enormous amount of carefully labeled images. Meanwhile, noise is inevitably introduced in the labeling process, degrading the performance of models. Hence, it is significant to devise robust training strategies to mitigate label noise in the medical image classification tasks. Methods In this work, we propose a novel Bayesian statistics‐guided label refurbishment mechanism (BLRM) for DNNs to prevent overfitting noisy images. BLRM utilizes maximum a posteriori probability in the Bayesian statistics and the exponentially time‐weighted technique to selectively correct the labels of noisy images. The training images are purified gradually with the training epochs when BLRM is activated, further improving classification performance. Results Comprehensive experiments on both synthetic noisy images (public OCT &amp; Messidor datasets) and real‐world noisy images (ANIMAL‐10N) demonstrate that BLRM refurbishes the noisy labels selectively, curbing the adverse effects of noisy data. Also, the anti‐noise BLRMs integrated with DNNs are effective at different noise ratio and are independent of backbone DNN architectures. In addition, BLRM is superior to state‐of‐the‐art comparative methods of anti‐noise. Conclusions These investigations indicate that the proposed BLRM is well capable of mitigating label noise in medical image classification tasks.
Prediksi Jumlah Pengunjung Semarang Zoo dengan Metode Fuzzy Time Series

Forecasting is a very important element in making decisions to deal with uncertain situations. The purpose of this study is to determine the results of forecasting using the fuzzy time series method so that it can be used as a basis for the Semarang Zoo management in planning development. This study uses secondary data from PT Taman Satwa Semarang. Data on the number of visitors to the Semarang Zoo from January 2017 to December 2021. The steps in this research are formulating problems, collecting data, analyzing data, and drawing conclusions. The calculation technique is carried out in two ways, namely manually and using the RStudio application. The result of this research is that the prediction results for January 2022 are 34,640 for manual calculations and 34,430 for calculations using the Rstudio application. While the MAPE test results obtained 14.15% for manual calculations and 15.082% for calculations using the Rstudio application.&#x0D; .&#x0D; Keywords: forecasting, fuzzy time series, number of visitors
Evaluating binaural beats as a therapeutic tool for intervening visually-induced motion sickness in virtual reality environments

Background: Visually-induced motion sickness (VISM) is an emerging problem in the application of virtual reality (VR) technology. Binaural beats are proven to be effective in reducing anxiety levels and enhancing pain management in VR environments. They may have the potential to be a remedy for symptoms of visually-induced motion sickness. Objective: To investigate whether binaural beats can effectively ease the symptoms of VIMS. Methods: Motion sickness susceptible individuals (n=35) were assigned to wear VR headsets and watch a 15-min VR video to evoke VIMS. Whenever motion sickness was self-reported by subjects, they were randomized into three groups receiving three types of intervention methods: Subjects in the experimental group listened to binaural beats; The control group was treated with beats of the same frequency at two soundtracks. And the blank group recovered without any sound played in the earphones. Subjects filled in the Simulator Sickness Questionnaires (SSQ) at three stages—before watching the VR video (baseline), during the watching experience (induction), and after watching (recovery). To increase the credibility, their physiological data were collected by a Biofeedback device during the whole process. Results: Participants’ subjective SSQ scores were analyzed, which did not reveal any significant difference between recovery and induction (p&gt;0.05). The physiological data were following the result obtained from analyzing SSQ scores. Conclusion: The binaural beats did not show a significant effect of alleviating VISM.
Sentiment Analysis on Covid-19 Vaccination in Indonesia Using Support Vector Machine and Random Forest

World Health Organization (WHO) stated Covid-19 as a global pandemic in March, 2020. This pandemic has influenced people’s life in many sectors such as the economy, health, tourism, and many more. One way to end this pandemic is to make herd immunity obtained through the vaccination program. This program still raises pros and cons at the beginning of its implementation in Indonesia. Many people doubt the safety and side effects of the vaccine. There are also pros and cons to vaccination programs in social media such as Twitter. This platform generates a huge amount of text data containing people's perceptions about vaccines. This research aims to predict sentiment using supervised learning such as support vector machine (SVM) and random forest and capture sentiment about vaccines in Indonesia in the first two weeks of the program. The result shows SVM was a better model than random forest based on the precision and F1-score metrics. The SVM approach produces a precision value of 0.50, a recall of 0.64, and an F1-score of 0.52. In the study, it was also found that tweets with neutral sentiment dominated the twitter user sentiment in the study period. Tweets with negative sentiment decreased after the first week of the COVID-19 vaccination program.
Real-Time Face Recognition and Detection Using Python

Abstract: While humans can recognize faces without much effort, facial recognition is a challenging pattern recognition problem in computing. Facial recognition systems attempt to identify a human face, which is three-dimensional and changes in appearance with lighting and facial expression, based on its two-dimensional image. To accomplish this computational task, facial recognition systems perform four steps. First face detection is used to segment the face from the image background. In the second step the segmented face image is aligned to account for face pose, image size and photographic properties, such as illumination and grayscale. The purpose of the alignment process is to enable the accurate localization of facial features in the third step, the facial nature extraction. Features such as eyes, nose and mouth are pinpointed and measured in the image to represent the face. The so established feature vector of the face is then, in the fourth step, matched against a database of faces. Keywords: face, detection, recognition, system, OpenCV
Pendampingan Blended Learning Berorientasi Pada Kemampuan Literasi dan Numerasi Pasca Pandemi Covid-19 di SDN 2 Tanjungpura

The Covid-19 pandemic has several impacts on the world of education including dropout rates, decreased learning achievement, and violence in children, as well as external risks. Based on some data, the impact of Covid-19 also causes loss generation, so it is necessary to hold a mentoring program to overcome motivation and participant loss in face-to-face and online learning that must be implemented by some schools. Indramayu is one of the areas that are at level 3 and according to The Ministry of Home Affairs, No.30 of 2021 has been able to carry out face-to-face learning. The implementation of face-to-face learning provides several solutions ranging from reflection to follow-up plans, in addition, face-to-face learning also allows for blended learning. The steps in this program include: (1) technical guidance activities and coordination of activities; (2) the preparation of work plans and the development of numeracy literacy instruments and psychosocial instruments; (3) preparation of guidebooks and modules and coordination with the school. The results of the initial and final assessment analysis for students, teachers, and parents at SDN 2 Tanjungpura showed that the program was carried out well with data as many as 98% of students felt excited and enjoy participating in mentoring activities through the establishment of progressive matching environmental.&#x0D; &#x0D; Keywords: Covid-19 pandemic, blended learning, literacy skill, numeracy skill, primary school