Reviewerly/exHarmony huggingface dataset:
Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels

Noisy training set usually leads to the degradation of generalization and robustness of neural networks. In this paper, we propose using a theoretically guaranteed noisy label detection framework to detect and remove noisy data for Learning with Noisy Labels (LNL). Specifically, we design a penalized regression to model the linear relation between network features and one-hot labels, where the noisy data are identified by the non-zero mean shift parameters solved in the regression model. To make the framework scalable to datasets that contain a large number of categories and training data, we propose a split algorithm to divide the whole training set into small pieces that can be solved by the penalized regression in parallel, leading to the Scalable Penalized Regression (SPR) framework. We provide the non-asymptotic probabilistic condition for SPR to correctly identify the noisy data. While SPR can be regarded as a sample selection module for standard supervised training pipeline, we further combine it with semi-supervised algorithm to further exploit the support of noisy data as unlabeled data. Experimental results on several benchmark datasets and real-world noisy datasets show the effectiveness of our framework. Our code and pretrained models are released at https://github.com/Yikai-Wang/SPR-LNL.
Automatic Japanese Example Extraction for Flashcard-based Foreign Language Learning

Flashcard systems are effective tools for learning words but have their limitations in teaching word usage. To overcome this problem, we suggest that a flashcard system shows a new example sentence on each repetition. This extension requires high-quality example sentences, automatically extracted from a huge corpus. To do this, we use a Determinantal Point Process which scales well to large data and allows us to naturally represent sentence similarity and quality as features. Our human evaluation experiment on the Japanese language indicates that the proposed method successfully extracted high-quality example sentences.
Object Detection and Tracking Algorithm Study for Football Sports Videos

To further improve the accuracy and robustness of intelligent algorithms in moving target tracking such as football sport, this paper proposes an object detection and tracking strategy based on CamShift and Kalman filtering. First, we adopt CamShift to track players and applies background weighted histogram to improve CamShift algorithm since the tracking defect is easily interrupted by surrounding environment. Then the combination between CamShift and Kalman filter is used to process players&#x2019; occlusion and the paper puts forward occlusion factors based on search windows and the method through occlusion factor to provide weights for the results of Kalman and CamShift. The experiment results show that algorithm is effective for football target tracking and resistant to target occlusion.
Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillation

The powerful learning ability of deep neural networks enables reinforcement learning agents to learn competent control policies directly from continuous environments. In theory, to achieve stable performance, neural networks assume i.i.d. inputs, which unfortunately does no hold in the general reinforcement learning paradigm where the training data is temporally correlated and non-stationary. This issue may lead to the phenomenon of "catastrophic interference" and the collapse in performance. In this paper, we present IQ, i.e., interference-aware deep Q-learning, to mitigate catastrophic interference in single-task deep reinforcement learning. Specifically, we resort to online clustering to achieve on-the-fly context division, together with a multi-head network and a knowledge distillation regularization term for preserving the policy of learned contexts. Built upon deep Q networks, IQ consistently boosts the stability and performance when compared to existing methods, verified with extensive experiments on classic control and Atari tasks. The code is publicly available at: https://github.com/Sweety-dm/Interference-aware-Deep-Q-learning.
A novel evaluation criteria to estimate label

In supervised learning, annotation is hard work because training data must be labeled and a lot of training data is needed. Therefore, we propose a novel label estimation method based on Fisher criterion to estimate label of unlabeled data from a small amount of labeled data. Fisher criterion maximizes between-class variance and minimizes within-class variance. Since the Fisher criterion is only used for linearly separable data, we apply our proposed method to linearly inseparable data. We demonstrate that the proposed method is effective in estimating the labels of linearly inseparable data.
Mining Minority-Class Examples with Uncertainty Estimates

In the real world, the frequency of occurrence of objects is naturally skewed forming long-tail class distributions, which results in poor performance on the statistically rare classes. A promising solution is to mine tail-class examples to balance the training dataset. However, mining tail-class examples is a very challenging task. For instance, most of the otherwise successful uncertainty-based mining approaches struggle due to distortion of class probabilities resulting from skewness in data. In this work, we propose an effective, yet simple, approach to overcome these challenges. Our framework enhances the subdued tail-class activations and, thereafter, uses a one-class data-centric approach to effectively identify tail-class examples. We carry out an exhaustive evaluation of our framework on three datasets spanning over two computer vision tasks. Substantial improvements in the minority-class mining and fine-tuned model’s task performance strongly corroborate the value of our method.
Low-Level Advanced Design of True Random Number Generators Based on Truly Chaotic Digital Nonlinear Oscillators in FPGAs

Recently, a new class of circuits named Digital Nonlinear Oscillators (DNOs) has been proposed for the design of fully digital True Random Number Generators (TRNGs). In this work we discuss the low-level advanced design of TRNGs based on chaotic DNOs, specialized for FPGAs. In detail, starting from a specific DNO topology, we discuss technical solutions to implement these systems exploiting FPGA device primitives. The proposed solutions have been characterized by means of exhaustive measurement campaigns to assess and investigate the impact on the entropy of both chip-to-chip and intra-device variability.
Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm &amp; Analysis

We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO). We introduce Federated Loss SuRface Aggregation (FLoRA), a general FL-HPO solution framework that can address use cases of tabular data and any Machine Learning (ML) model including gradient boosting training algorithms and therefore further expands the scope of FL-HPO. FLoRA enables single-shot FL-HPO: identifying a single set of good hyper-parameters that are subsequently used in a single FL training. Thus, it enables FL-HPO solutions with minimal additional communication overhead compared to FL training without HPO. We theoretically characterize the optimality gap of FL-HPO, which explicitly accounts for the heterogeneous non-IID nature of the parties' local data distributions, a dominant characteristic of FL systems. Our empirical evaluation of FLoRA for multiple ML algorithms on seven OpenML datasets demonstrates significant model accuracy improvements over the considered baseline, and robustness to increasing number of parties involved in FL-HPO training.
Efficient Energy-Aware Resource Management Model (EEARMM) Based Dynamic VM Migration

In cloud environment, an efficient resource management establishes the allocation of computational resources of cloud service providers to the requests of users for meeting the user's demands.The proficient resource management and work allocation determines the accomplishment of the cloud infrastructure.However, it is very difficult to persuade the objectives of the Cloud Service Providers (CSPs) and end users in an impulsive cloud domain with random changes of workloads, huge resource availability and complicated service policies to handle them, With that note, this paper attempts to present an Efficient Energy-Aware Resource Management Model (EEARMM) that works in a decentralized manner.Moreover, the model involves in reducing the number of migrations by definite workload management for efficient resource utilization.That is, it makes an effort to reduce the amount of physical devices utilized for load balancing with certain resource and energy consumption management of every machine.The Estimation Model Algorithm (EMA) is given for determining the virtual machine migration.Further, VM-Selection Algorithm (SA) is also provided for choosing the appropriate VM to migrate for resource management.By the incorporation of these algorithms, overloading of VM instances can be avoided and energy efficiency can be improved considerably.The performance evaluation and comparative analysis, based on the dynamic workloads in different factors provides evidence to the efficiency, feasibility and scalability of the proposed model in cloud domain with high rate of resources and workload management.
Cognitive Computing-Based Mammographic Image Classification on an Internet of Medical

Recently, the Internet of Medical Things (IoMT) has become a research hotspot due to its various applicability in medical field.However, the data analysis and management in IoMT remain challenging owing to the existence of a massive number of devices linked to the server environment, generating a massive quantity of healthcare data.In such cases, cognitive computing can be employed that uses many intelligent technologies-machine learning (ML), deep learning (DL), artificial intelligence (AI), natural language processing (NLP) and others-to comprehend data expansively.Furthermore, breast cancer (BC) has been found to be a major cause of mortality among ladies globally.Earlier detection and classification of BC using digital mammograms can decrease the mortality rate.This paper presents a novel deep learning-enabled multi-objective mayfly optimization algorithm (DL-MOMFO) for BC diagnosis and classification in the IoMT environment.The goal of this paper is to integrate deep learning (DL) and cognitive computing-based techniques for e-healthcare applications as a part of IoMT technology to detect and classify BC.The proposed DL-MOMFO algorithm involved Adaptive Weighted Mean Filter (AWMF)-based noise removal and contrast-limited adaptive histogram equalisation (CLAHE)-based contrast improvement techniques to improve the quality of the digital mammograms.In addition, a U-Net architecture-based segmentation method was utilised to detect diseased regions in the mammograms.Moreover, a SqueezeNet-based feature extraction and a fuzzy support vector machine (FSVM) classifier were used in the presented technique.To enhance the diagnostic performance of the presented method, the MOMFO algorithm was used to effectively tune the parameters of the SqueezeNet and FSVM techniques.The DL-MOMFO technique was tested on the MIAS database, and the experimental outcomes revealed that the DL-MOMFO technique outperformed existing techniques.
Saliency Region Detection in Complex Scenes Based on Multi-scale Cascaded Attention

This paper proposes a saliency detection method based on multi-scale cascade attention mechanism. It utilizes both channel and spatial weight attention mechanism to effectively learn the salient regions. By generating multi-scale intermediate feature maps, the shallow features are divided into categories of foreground and background. Then, the channel weights are calculated by using the foreground and background feature distribution, and the spatial weights are computed by using the predicted feature map, so that the network is more focused on salient regions and suppresses the interference of background regions. Experimental results show that the model can reliably and accurately detect salient targets and delivers better performance.
dunXai: DO-U-Net for Explainable (Multi-label) Image Classification

Artificial Intelligence (AI) and Machine Learning (ML) are becoming some of the most dominant tools in scientific research. Despite this, little is often understood about the complex decisions taken by the models in predicting their results. This disproportionately affects biomedical and healthcare research where explainability of AI is one of the requirements for its wide adoption. To help answer the question of what the network is looking at when the labels do not correspond to the presence of objects in the image but the context in which they are found, we propose a novel framework for Explainable AI that combines and simultaneously analyses Class Activation and Segmentation Maps for thousands of images. We apply our approach to two distinct, complex examples of real-world biomedical research, and demonstrate how it can be used to provide a global and concise numerical measurement of how distinct classes of objects affect the final classification. We also show how this can be used to inform model selection, architecture design and aid traditional domain researchers in interpreting the model results.
Constrained Density Matching and Modeling for Cross-lingual Alignment of Contextualized Representations

Multilingual representations pre-trained with monolingual data exhibit considerably unequal task performances across languages. Previous studies address this challenge with resource-intensive contextualized alignment, which assumes the availability of large parallel data, thereby leaving under-represented language communities behind. In this work, we attribute the data hungriness of previous alignment techniques to two limitations: (i) the inability to sufficiently leverage data and (ii) these techniques are not trained properly. To address these issues, we introduce supervised and unsupervised density-based approaches named Real-NVP and GAN-Real-NVP, driven by Normalizing Flow, to perform alignment, both dissecting the alignment of multilingual subspaces into density matching and density modeling. We complement these approaches with our validation criteria in order to guide the training process. Our experiments encompass 16 alignments, including our approaches, evaluated across 6 language pairs, synthetic data and 5 NLP tasks. We demonstrate the effectiveness of our approaches in the scenarios of limited and no parallel data. First, our supervised approach trained on 20k parallel data (sentences) mostly surpasses Joint-Align and InfoXLM trained on over 100k parallel sentences. Second, parallel data can be removed without sacrificing performance when integrating our unsupervised approach in our bootstrapping procedure, which is theoretically motivated to enforce equality of multilingual subspaces. Moreover, we demonstrate the advantages of validation criteria over validation data for guiding supervised training.
Extractive summarization of Malayalam documents using latent Dirichlet allocation: An experience

Abstract Automatic text summarization (ATS) extracts information from a source text and presents it to the user in a condensed form while preserving its primary content. Many text summarization approaches have been investigated in the literature for highly resourced languages. At the same time, ATS is a complicated and challenging task for under-resourced languages like Malayalam. The lack of a standard corpus and enough processing tools are challenges when it comes to language processing. In the absence of a standard corpus, we have developed a dataset consisting of Malayalam news articles. This article proposes an extractive topic modeling-based multi-document text summarization approach for Malayalam news documents. We first cluster the contents based on latent topics identified using the latent Dirichlet allocation topic modeling technique. Then by adopting vector space model, the topic vector and sentence vector of the given document are generated. According to the relevant status value, sentences are ranked between the document’s topic and sentence vectors. The summary obtained is optimized for non-redundancy. Evaluation results on Malayalam news articles show that the summary generated by the proposed method is closer to the human-generated summaries than the existing text summarization methods.
Introdução à Programação com Memória Persistente

The book introduced in XXII Escola Regional de Alto Desempenho da Região Sul (ERAD/RS) – event coordinated by Sociedade Brasileira de Computação (SBC) and Comissão Regional de Alto Desempenho do Estado do Rio Grande do Sul (CRAD/RS) with the aim of qualifying professionals from the south region of Brazil – , involves the integration of parallel programing researchers. Written in five chapters, this book is able to provide a grand variety of convergent content, focusing on performance. &nbsp; In its first chapter, entitled “Desenvolvimento de Aplicações Paralelas Adaptativas: Uma Visão de Duas Décadas (2001-2021)”, the authors explain about the evolution of the parallel &nbsp;architectures and the transcription of their uses in dynamic environment. The theme provoques an interesting reflection not only for already-consolidated academics, but also for young students searching for new challenges. In the following two chapters, the main topics are programing languages and technical in parallel computing, named as “Computação de Alto Desempenho em Julia", which refers to modern language, easily used and learn; and "Introdução à Programação com Memória Persistente", which discuss about the motivation and problems related to consistency of processors’ datas with persistent memory (PM) In the fourth chapter, the essential aspects, the evaluation and the differences from the programs aiming to achieve the Gaming section were summarized and discussed. Lastly, the outcome of the book highlights the omnipresent theme from research turned to the processing of high performance (PAD), “Apresentação de Resultados Experimentais para Processamento de Alto Desempenho em R”, which proposes ways to better dispose and reproduc the datas in academic texts and experiments.
Exploring the Impacts from Datasets to Monocular Depth Estimation (Mde) Models with Minenavi

Current computer vision tasks based on deep learning require a huge amount of data with annotations for model training or testing, especially in some dense estimation tasks, such as optical flow segmentation and depth estimation. In practice, manual labeling for dense estimation tasks is very difficult or even impossible, and the scenes of the dataset are often restricted to a small range, which dramatically limits the development of the community. To overcome this deficiency, we propose a synthetic dataset generation method to obtain the expandable dataset without burdensome manual workforce. By this method, we construct a dataset called MineNavi containing video footages from first-perspective-view of the aircraft matched with accurate ground truth for depth estimation in aircraft navigation application. We also provide quantitative experiments to prove that pre-training via our MineNavi dataset can improve the performance of depth estimation model and speed up the convergence of the model on real scene data. Since the synthetic dataset has a similar effect to the real-world dataset in the training process of deep model, we also provide additional experiments with monocular depth estimation method to demonstrate the impact of various factors in our dataset such as lighting conditions and motion mode.
PixelFolder: An Efficient Progressive Pixel Synthesis Network for Image Generation

Pixel synthesis is a promising research paradigm for image generation, which can well exploit pixel-wise prior knowledge for generation. However, existing methods still suffer from excessive memory footprint and computation overhead. In this paper, we propose a progressive pixel synthesis network towards efficient image generation, coined as PixelFolder. Specifically, PixelFolder formulates image generation as a progressive pixel regression problem and synthesizes images by a multi-stage paradigm, which can greatly reduce the overhead caused by large tensor transformations. In addition, we introduce novel pixel folding operations to further improve model efficiency while maintaining pixel-wise prior knowledge for end-to-end regression. With these innovative designs, we greatly reduce the expenditure of pixel synthesis, e.g., reducing $$89\%$$ computation and $$53\%$$ parameters compared to the latest pixel synthesis method called CIPS. To validate our approach, we conduct extensive experiments on two benchmark datasets, namely FFHQ and LSUN Church. The experimental results show that with much less expenditure, PixelFolder obtains new state-of-the-art (SOTA) performance on two benchmark datasets, i.e., 3.77 FID and 2.45 FID on FFHQ and LSUN Church, respectively. Meanwhile, PixelFolder is also more efficient than the SOTA methods like StyleGAN2, reducing about $$72\%$$ computation and $$31\%$$ parameters, respectively. These results greatly validate the effectiveness of the proposed PixelFolder. Our source code is available at https://github.com/BlingHe/PixelFolder .
Modified Gabor Filter with Enhanced Naïve Bayes Algorithm for Facial Expression Recognition in Image Processing

Face recognition is a subdomain of computational biometrics, which is an automated technique for verifying an identity of a human based on some parameters like the pattern in iris and face dimensions. It is a multi-faceted process as there are only a limited images available for the purpose of training, and different images are obtained through various conditions and surroundings. There is a constant challenge that researchers face which is as hoe to efficiently handle the sensitivity of the classifier under illumination, expressions, and performing under low resolution images. Conventional methods of face recognition have a lower accuracy on the classification, and hence, this paper proposes a modified Gabor filtering technique based on NB classifier (MGF + ENBA) for overcoming the issue. The MGF technique extracts only the most important features from the available data in an efficient manner. The ENBA algorithm is then used for the classification of facial expression in a more accurate way with the emotion detection such as sad, happy, angry, fear, and neutral emotional features. The experiments were performed using the Cohn-Kanade data and the JAFFE database. The results prove that the proposed technique performs better than the other methods in terms of precision, recall, and F-measure which prove improved accuracy.
A Study on Senior High School English Reading Teaching Pointing to the Cultivation of Thinking Quality

The English Curriculum Standards (2017 edition) for the first time clearly listed the quality of thinking as one of the core &#x0D; competencies of the discipline. However, the current problem facing high school English teaching is how to effectively cultivate &#x0D; students’ quality of thinking. Therefore, teachers need to actively implement the cultivation of core competence, strive to combine &#x0D; English reading teaching and the cultivation of thinking quality, and cultivate the thinking quality of students through English &#x0D; reading teaching.