Reviewerly/exHarmony huggingface dataset:
Fuzzy Classifier for Speech Assessment in Speech Rehabilitation

The article describes the use of a fuzzy classifier as a mechanism for combining the values calculated from three metrics to obtain a quantitative assessment of the intelligibility of the syllable pronunciation. The resulting assessment is used to assess speech in speech rehabilitation after the treatment of the oral cavity and oropharynx oncology. The evaluation is based on the calculation of the distance as the similarity degree between the syllable pronunciation in the estimated recording (the patient’s speech in the process of rehabilitation) and the syllable pronunciation in the reference recording (the patient’s speech before surgery). It is proposed to use a fuzzy classifier with the genetic optimization algorithm NSGA II. The training datasets are quantitative and expert estimates of a set of syllable pronunciation recordings provided by the Cancer Research Institute Tomsk NRMC. Three datasets were used: 1020 recordings for each of the problematic phonemes [k], [s], [t]. Separate fuzzy classifiers were trained for each of the datasets. Classification accuracies are shown on the initial datasets and rebalanced datasets. The NeighborhoodCleaningRule and SMOTEENN algorithms were used for rebalancing the data. It was concluded that it is possible to use a fuzzy classifier as a combination mechanism to obtain the intelligibility assessment of the syllable pronunciation.
Deep Mining Covid-19 Literature

In this paper we investigate how scientific and medical papers about Covid-19 can be effectively mined. For this purpose we use the CORD19 dataset which is a huge collection of all papers published about and around the SARS-CoV2 virus and the pandemic it caused. We discuss how classical text mining algorithms like Latent Semantic Analysis (LSA) or its modern version Latent Drichlet Allocation (LDA) can be used for this purpose and also touch more modern variant of these algorithms like word2vec which came with deep learning wave and show their advantages and disadvantages each. We finish the paper with showing some topic examples from the corpus and answer questions such as which topics are the most prominent for the corpus or how many percentage of the corpus is dedicated to them. We also give a discussion of how topics around RNA research in connection with Covid-19 can be examined.
Ask It Right! Identifying Low-Quality questions on Community Question Answering Services

Stack Overflow is a Community Question Answering service that attracts millions of users to seek answers to their questions. Maintaining high-quality content is necessary for relevant question retrieval, question recommendation, and enhancing the user experience. Manually removing low-quality content from the platform is time-consuming and challenging for site moderators. Thus, it is imperative to assess the content quality by automatically detecting and ‘closing’ the low-quality questions. Previous works have explored lexical, community-based, vote-based, and style-based features to detect low-quality questions. These approaches are limited to writing styles, textual, and handcrafted features. However, these features fall short in understanding semantic features and capturing the implicit relationships between tags and questions. In contrast, we propose LQuaD (Low-Quality Question Detection), a multi-tier hybrid framework that, a) incorporates semantic information of questions associated with each post using transformers, b) includes the question and tag information that enables learning via a graph convolutional network. LQuaD outperforms the state-of-the-art methods by a 21% higher F1-score on the dataset of 2.8 million questions. Furthermore, we apply survival analysis which acts as a proactive intervention to reduce the number of questions closed by informing users to take appropriate action. We find that the timeframe between the stages from the question's creation till it gets ‘closed’ varies significantly for tags and different ‘closing’ reasons for these questions.
Implementation of Unusual Human Activity Detection in Warehouse Using SSD

In this emerging world, technology and crimes are increasing correspondingly. Unusual activity may be anything that seems illegal or abnormal which is not performed by every normal person. Unusual activities that can happen in warehouse are theft and damaging of warehouse products, unnecessary fights among employees and accidents that occur during work which cause heavy loss of products and wealth. It is a tedious task to control those unusual activities since the working area is very large which involves many number of workers. Even though implementation of CCTV cameras helps in security, it requires a well-trained professional to handle, which can be further automated by machine learning and deep learning models. It will reduce the use of manpower and improve the security by using modern solutions. To provide security to warehouse, we developed an unusual human activity detection (UHAD) model which is trained through SSD model that detects unusual activities.
The effects of school climate on students’ reading achievement

Despite the widely acknowledged importance of reading literacy to the success of modern societies, the performance of Indonesian children in reading remains comparatively poor. With reference to interdependence theory by Harold Kelley and John thibaut (1959), which states that social interaction highly influences student outcomes, this study aims to investigate the effects of school climate on Indonesian students' reading achievements. The sample used in this study was the results of 12,098 Indonesian students' participation in PISA 2018. Data were analysed using SPSS and AMOS software, with several data analyses conducted including a t-test, ANOVA, CFA, and SEM. The results of this study showed that Indonesian female students are significantly better at reading compared to their male counterparts. Based on year levels, students in Grade 11 achieved the highest scores, while surprisingly students in Grade 12 achieved lower scores than their counterparts in Grades 10 and 11. An important finding of this study was related to the effects of disruptive student behavior, which among the variables analysed had the strongest negative effects on reading achievements. Surprisingly, teaching practices, which were hypothesised to have a positive effect on reading performance, instead showed negative direct effects on student performance. However, the tenets of interdependence theory were supported by the finding that strong school communities have significant positive effects on student reading scores.
An asynchronous call graph for JavaScript

Asynchronous JavaScript has become omnipresent, yet is inherently difficult to reason about. While many recent debugging tools are trying to address this issue with (semi-)automatic methods, interactive analysis tools are few and far between. To this date, developers are required to build mental models of complex concurrent control flows with little to no tool support. Thus, asynchrony is making life hard for novices and catches even seasoned developers off-guard, especially when dealing with unfamiliar code. That is why we propose the Asynchronous Call Graph. It is the first approach to capture and visualize concurrent control flow between call graph roots. It is also the first concurrency analysis tool for JavaScript that is fully interactive and integrated with an omniscient debugger in a popular IDE. First tests show that the ACG works successfully on real-world codebases. This approach has the potential to set a new standard for how developers can analyze asynchrony.
Towards Accurate Open-Set Recognition via Background-Class Regularization

In open-set recognition (OSR), classifiers should be able to reject unknown-class samples while maintaining high closed-set classification accuracy. To effectively solve the OSR problem, previous studies attempted to limit latent feature space and reject data located outside the limited space via offline analyses, e.g., distance-based feature analyses, or complicated network architectures. To conduct OSR via a simple inference process (without offline analyses) in standard classifier architectures, we use distance-based classifiers instead of conventional Softmax classifiers. Afterwards, we design a background-class regularization strategy, which uses background-class data as surrogates of unknown-class ones during training phase. Specifically, we formulate a novel regularization loss suitable for distance-based classifiers, which reserves sufficiently large class-wise latent feature spaces for known classes and forces background-class samples to be located far away from the limited spaces. Through our extensive experiments, we show that the proposed method provides robust OSR results, while maintaining high closed-set classification accuracy.
The task of consistency of the logical-temporal sequence of the educational process of the university

The task of higher education is the prompt and complete transfer of the achievements of science and technology to the widest possible stratum of the population, introducing it into the general consciousness, as well as the rapid use of the results of education in the national economy. Improving the efficiency of higher education management is associated with the introduction of automated information systems in the educational process of the university, which is associated with the solution of a number of tasks that are difficult to formalize. Among them, it is advisable to include the tasks of ensuring the consistency of the logical-temporal sequence of training, optimizing the distribution of educational material between academic disciplines, as well as the task of developing curricula for reduced training or wartime plans in certain areas of professional activity. The urgency of the listed tasks increases in the context of the introduction of a new generation of federal state educational standards of higher education, expanding the powers of educational organizations. The widespread introduction of information technology creates favorable conditions for automating the processes of managing the educational process, formulating and solving problems of planning and managing learning in terms of cybernetics, in particular, combinatorics and integer programming. A cybernetic approach to the study of the problems of improving the quality of planning and organization of the educational process of a higher educational institution is proposed. The task of assessing the degree of consistency of documents of the educational process is formalized, methods for its solution are proposed. Ways of realization of the received results in perspective automated information systems of high schools are shown.
The Use of Multiple Criteria Decision Aiding Methods in Recommender Systems: A Literature Review

Multiple Criteria Decision Making (MCDA) methods have been increasingly applied to improve recommendations when multiple criteria are considered in Recommender Systems (RSs). This study presents the preliminary results of a systematic literature review, following Kitchenham's guidelines, regarding the application of MCDA methods in RSs over the last two decades. Based on our findings, MCDA methods can be applied in two RS phases: the preference elicitation and the recommendation phases. In the former, RSs usually have a strong interaction with the user, which results in more personalized recommendations, ensuring higher user satisfaction and contributing to address the cold-start challenge in RSs. Regarding the recommendation phase, while most RSs are based on ranking approaches, there is a trend to apply sorting methods in order to avoid an additional step involving a filtering application that selects a subset of alternatives. Future research could focus on applying preference learning combined with MCDA methods for exploring improvements in prediction and recommendation phases, and also in quality and processing time.
Brightness Preserving Generalized Histogram Equalization with High Contrast Enhancement Ability

Histogram equalization (HE) is the one of the simplest and most effective methods for contrast enhancement. It can automatically define the gray-level mapping function based on the distribution of gray-level included in the image. However, since HE does not use a spatial feature included in the input image, HE fails to produce satisfactory results for broad range of low-contrast images. The differential gray-level histogram (DH), which is contained edge information of the input image, was defined and the differential gray-level histogram equalization (DHE) has been proposed. The DHE shows better enhancement results compared to HE for many kinds of images. In this paper, we propose a generalized histogram equalization (GHE) including HE and DHE. In GHE, the histogram is created using the power of the differential gray-level, which includes the spatial features of the image. In HE, the mean brightness of the enhancement image cannot be controlled. On the other hand, GHE can control the mean brightness of the enhancement image by changing the power, thus, the mean brightness of the input image can be perfectly preserved while maintaining good contrast enhancement.
Target Category Agnostic Knowledge Distillation With Frequency-Domain Supervision

Existing knowledge distillation approaches require task-related data to train portable student networks for satisfactory performance. Nevertheless, in real-world applications, a majority of the data is unavailable over concerns about personal privacy, commercial confidentiality, etc. To mitigate the difficulty of acquiring the target dataset, the mainstream knowledge distillation methods generate training samples from the teacher network. However, the generated images still differ from the authentic ones, which limit the student network performance. To solve this issue, we propose a convenient and cost-negligible method to build datasets for training student networks. Specifically, we crawl the data from the web without knowing any category of the target dataset, named irrelevant category crawler data (ICCD). To prevent the performance collapse due to the data distribution gap between our ICCD and the target dataset, we propose a pseudo-classification strategy and frequency-domain supervision (PCFS) for target category agnostic knowledge distillation with ICCD. The pseudo-classification strategy classifies ICCD into different pseudo-categories by the teacher network, and uniformly but randomly samples images from each pseudo-category to construct the pseudo-target dataset. Furthermore, we transform the feature maps from the spatial domain to the frequency domain, and utilize the high- and low-frequency signals of the teacher network to impose strong constraints on the student network. Extensive experiments conducted on various test sets demonstrate the effectiveness of our proposed PCFS, which outperforms existing data-free methods and achieves comparable performance to those using the target training set. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SCUT-BIP-Lab/PCFS-DFKD</uri> .
Pixel-accurate Segmentation of Surgical Tools based on Bounding Box Annotations

Detection and segmentation of surgical instruments is an important problem for laparoscopic surgery. Accurate pixel-wise instrument segmentation is a useful intermediate task for the development of computer-assisted surgery systems, such as pose estimation, surgical phase estimation, enhanced image fusion, video retrieval and others. In this paper we describe a deep learning-based approach to instrument segmentation, which addresses the binary segmentation problem in which every pixel in an image is labeled as instrument or background. The key novelty of our approach relates to the use of training data which is inexpensive and fast to acquire. First, our approach relies on weak annotations provided as bounding boxes of the instruments, which are much faster and cheaper to obtain than a dense pixel-level annotations. Second, to further improve the system’s accuracy we propose a novel approach to generate synthetic training images. Our approach achieves state-of-the-art results, outperforming previously proposed methods for automatic instrument segmentation, based only on weak annotations.
Accelerate Federated Learning of Big Data Analysis Through Distillation and Coupled Gradient Methods

Aiming at the problem that the training time of federated learning (FL) deep neural network is very long or not feasible, this paper presents an efficient federated learning and training framework. The framework consists of two parts: distillation and coupled gradient algorithm, which can reduce the computational cost, speed up the training process of federated learning and save computational resources. Simulation experiments were carried out using an open data set(mnist), and the results show that the framework can guarantee the learning performance and control the training cost well.
Using human gaze in few-shot imitation learning for robot manipulation

Imitation learning has attracted attention as a method for realizing complex robot control without programmed robot behavior. Meta-imitation learning has been proposed to solve the high cost of data collection and low generalizability to new tasks that imitation learning suffers from. Meta-imitation can learn new tasks involving unknown objects from a small amount of data by learning multiple tasks during training. However, meta-imitation learning, especially using images, is still vulnerable to changes in the background, which occupies a large portion of the input image. This study introduces a human gaze into meta-imitation learning-based robot control. We created a model with model-agnostic meta-learning to predict the gaze position from the image by measuring the gaze with an eye tracker in the head-mounted display. Using images around the predicted gaze position as an input makes the model robust to changes in visual information. We experimentally verified the performance of the proposed method through picking tasks using a simulated robot. The results indicate that our proposed method has a greater ability than the conventional method to learn a new task from only 9 demonstrations even if the object's color or the background pattern changes between the training and test.
Blockchain in a Nutshell

Blockchain enables a digital society where people can contribute, collaborate, and transact without having to second-guess trust and transparency. It is the technology behind the success of Bitcoin, Ethereum, and many disruptive applications and platforms that have positive impact in numerous sectors, including finance, education, health care, environment, transportation, and philanthropy, to name a few. This chapter provides a friendly description of essential concepts, mathematics, and algorithms that lay the foundation for blockchain technology.
Fries

A computing job in a big data system can take a long time to run, especially for pipelined executions on data streams. Developers often need to change the computing logic of the job such as fixing a loophole in an operator or changing the machine learning model in an operator with a cheaper model to handle a sudden increase of the data-ingestion rate. Recently many systems have started supporting runtime reconfigurations to allow this type of change on the fly without killing and restarting the execution. While the delay in reconfiguration is critical to performance, existing systems use epochs to do runtime reconfigurations, which can cause a long delay. In this paper we develop a new technique called Fries that leverages the emerging availability of fast control messages in many systems, since these messages can be sent without being blocked by data messages. We formally define consistency in runtime reconfigurations, and develop a Fries scheduler with consistency guarantees. The technique not only works for different classes of dataflows, but also works for parallel executions and supports fault tolerance. Our extensive experimental evaluation on clusters show the advantages of this technique compared to epoch-based schedulers.
Multi-Grained Interpre table Network for Image Recognition

Given a classification problem with a large number of classes, humans often compare features at different granularities from coarse to fine to gradually recognize an object. However, current deep models are generally trained to directly make the final prediction, focusing on improving the ability of the network to extract features without considering the interpretability of the model. In this paper, we propose a multi-grained interpretable network to imitate the reasoning process of humans. The proposed network is equipped with techniques to assign images with multi-grained labels, so as to train a tree-structured classifier that learns features at different levels of granularity. The proposed method can hierarchically classify objects in images at different granularities, while providing a decision pathway with multi-grained explanations for practitioners. Experimental results demonstrate that our method achieves competitive prediction accuracy on CUB-200-2011 and Stanford Cars datasets, and simultaneously produces high-quality explanations of its decisions. Moreover, our method shows higher robustness of the learned features to adversarial examples generated by the FGSM and PGD attacks.
Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions

The paradigm of machine intelligence moves from purely supervised learning to a more practical scenario when many loosely related unlabeled data are available and labeled data is scarce. Most existing algorithms assume that the underlying task distribution is stationary. Here we consider a more realistic and challenging setting in that task distributions evolve over time. We name this problem as Semi-supervised meta-learning with Evolving Task diStributions, abbreviated as SETS. Two key challenges arise in this more realistic setting: (i) how to use unlabeled data in the presence of a large amount of unlabeled out-of-distribution (OOD) data; and (ii) how to prevent catastrophic forgetting on previously learned task distributions due to the task distribution shift. We propose an OOD Robust and knowleDge presErved semi-supeRvised meta-learning approach (ORDER) (we use ORDER to denote the task distributions sequentially arrive with some ORDER), to tackle these two major challenges. Specifically, our ORDER introduces a novel mutual information regularization to robustify the model with unlabeled OOD data and adopts an optimal transport regularization to remember previously learned knowledge in feature space. In addition, we test our method on a very challenging dataset: SETS on large-scale non-stationary semi-supervised task distributions consisting of (at least) 72K tasks. With extensive experiments, we demonstrate the proposed ORDER alleviates forgetting on evolving task distributions and is more robust to OOD data than related strong baselines.