Reviewerly/exHarmony huggingface dataset:
Experience in Implementing Distance Learning Technologies in an International Context

The relevance of the study is determined by socio-cultural processes, which necessitate the revision of methods for organising the educational process, in particular the transition to distance learning, and the establishment of correlations with international standards. The purpose of the article is to identify the features, advantages, and disadvantages of distance learning, along with the prospects for development in accordance with the comparative analysis of Ukrainian and foreign experience with distance education. Methods and techniques varied depending on the tasks at each research stage, thus, the method of a targeted selection, the comparative method, the generalisation and systematisation of the theoretical material, and others were applied. The article substantiates the prospects for the development and implementation of distance learning technologies. The Ukrainian and foreign studies which focus on the “distance education” are analysed; ways of the distance learning implementation are outlined; the conditions for the appropriate and effective distance learning introduction are indicated; advantages and disadvantages of the distance learning are identified, the urgency of the development of distance education in Ukraine is substantiated. The factors that contributed to the emergence and development of distance learning courses are highlighted, the current problems of introduction of distance learning technologies are specified. The article compares the experience of introducing distance learning in different countries, which is based on statistics for 2020-2021. It is stated that the student of the distance learning course must have great will and motivation, be able to properly organise their time, since distance learning is directly aimed at self-education and self-development of the learner. The practical value of the obtained results lies in the application of distance technologies in the educational activities of higher education institutions and in accelerating the modernisation and adaptation of the current education system in Ukraine, in accordance with the requirements and trends of the global education sphere
Rotation-fused Consistency Semi-supervised Learning for Object Detection

In recent years, the neural network has been widely used in the object detection field. Most methods are supervised learning which need lots of labeled data. However, the production of the labeled data set is a costly work, especially in the object detection task. The utilization of unlabeled data has attracted much attention from the academic and industrial areas. Semi-supervised learning is one of the effective ways to simultaneously use both labeled and unlabeled data in the training process which has achieved great success in the field of image classification. Unfortunately, these methods used in image classification are not suitable for object detection completely. In this paper, we propose the Rotation-fused Consistency Semi-supervised Learning for Object Detection(RCSD), which uses rotation fusion, applies the consistency regularization to object detection, and takes into account both the classification and location of objects. The effectiveness of our method in the data set PASCAL VOC is verified. The results show that the performance of the detector is enhanced by using rotation consistency and it is further improved by multi-rotation fusion.
String Covers of a Tree

We consider covering labeled trees by a collection of paths with the same string label, called a (string) cover of a tree. We show how to compute all covers of a directed (rooted) labeled tree in \({\mathcal {O}}(n \log n/\log \log n)\) time and all covers of an undirected labeled tree in \({\mathcal {O}}(n^2)\) time and space or in \({\mathcal {O}}(n^2 \log n)\) time and \({\mathcal {O}}(n)\) space. We also show several essential differences between covers in standard strings and covers in trees.
Adaptive Semiparametric Bayesian Differential Equations Via Sequential Monte Carlo

Nonlinear differential equations (DEs) are used in a wide range of scientific problems to model complex dynamic systems. The differential equations often contain unknown parameters that are of scientific interest, which have to be estimated from noisy measurements of the dynamic system. Generally, there is no closed-form solution for nonlinear DEs, and the likelihood surface for the parameter of interest is multi-modal and very sensitive to different parameter values. We propose a Bayesian framework for nonlinear DE systems. A flexible nonparametric function is used to represent the dynamic process such that expensive numerical solvers can be avoided. A sequential Monte Carlo algorithm in the annealing framework is proposed to conduct Bayesian inference for parameters in DEs. In our numerical experiments, we use examples of ordinary differential equations and delay differential equations to demonstrate the effectiveness of the proposed algorithm. We developed an R package that is available at https://github.com/shijiaw/smcDE. Supplementary files for this article are available online.
Compositional Data Modeling through Dirichlet Innovations

The Dirichlet distribution is a well-known candidate in modeling compositional data sets. However, in the presence of outliers, the Dirichlet distribution fails to model such data sets, making other model extensions necessary. In this paper, the Kummer–Dirichlet distribution and the gamma distribution are coupled, using the beta-generating technique. This development results in the proposal of the Kummer–Dirichlet gamma distribution, which presents greater flexibility in modeling compositional data sets. Some general properties, such as the probability density functions and the moments are presented for this new candidate. The method of maximum likelihood is applied in the estimation of the parameters. The usefulness of this model is demonstrated through the application of synthetic and real data sets, where outliers are present.
Were there ancient professions? - E. Stewart, E. Harris, and D. Lewis, eds. 2020. Skilled Labour and Professionalism in Ancient Greece and Rome. Cambridge: Cambridge University Press. Pp. xviii + 393, 21 black-and-white figs. ISBN 978-1-108-83947-1.

Were there ancient professions? - E. Stewart, E. Harris, and D. Lewis, eds. 2020. Skilled Labour and Professionalism in Ancient Greece and Rome. Cambridge: Cambridge University Press. Pp. xviii + 393, 21 black-and-white figs. ISBN 978-1-108-83947-1. - Volume 34 Issue 2
CAQ: Context-Aware Quantization via Reinforcement Learning

Model quantization is a crucial step for porting Deep Neural Networks (DNNs) on embedded devices to meet the limited computation and storage resources requirement. Traditional methods usually obtain the scaling factor and quantize the weights based on the information of single layer. However, our analysis indicate that these selection methods of scaling factor overlook the differences and dependencies among layers, leading to large truncation errors or zeroing errors, which is the main reason for the performance degradation. To this end, we propose a Context-Aware Quantization (CAQ) scheme, which formalizes the model quantization as a global optimization problem and leverages reinforcement learning to search for the optimal scaling factors based on the entire model. Further, we adopt shift-based scaling factors to narrow the search space to improve the search efficiency, additionally, it reduces the computational complexity during the inference phase, and also provides a simpler and more robust activation calibration solution. We extensively test our scheme on a wide range of Neural Networks, including ResNet 50/101/152, InceptionV3 and MobileNetV2 on ImageNet, the entire search process only takes about 1 hour on a single GeForce RTX 2080 Ti. Compared with the existed methods, Our scheme can get a better performance, which could maintain the post-quantization accuracy loss less than 0.25%, while reducing memory footprint by 5%-8% and multiply accumulate (MAC) operations by 2%-4%. Besides, we further show that the CAQ can be applied on other tasks, such as object detection and segmentation.
Real-Time Face Swapping System using OpenCV

Face swapping has been a thriving genre of work which primarily is associated with the replacement or substitution of one referral face on the face of the other person, be it by means of still images or in real time. This paper exhibits the research on Real-Time Face Swapping algorithm, using the reference image of the user and henceforth considering it as our input image instead of using a data set thereby working on a real time model by the use of the python open-source model of Computer Vision i.e., OpenCV. The input image facial features and attributes are extracted and are replaced to make the final resulting output through our model. The training of the model starts with learning the facial alignments and features to be able to recognize a face and extract its features before swapping it. Now, heading over to the process of image warping by the dissociation and partitioning of the user's face and its background. To increase the precision of the model, in the second part of parsing the face is performed to cancel out other features of the live image, hence the output is obtained. In this research, it was tried to bring accuracy and precision to the model by improving the condition of the image, editing out borders, and training it to a bunch of data for face recognition and correction. On analyzing it delivers the most subtle outputs alongside performing the tasks with the process including the analysis of our image and its output too. The model delivered is to improve the conditions of security, privacy, image capture, and entertainment purposes.
Balancing Expressiveness and Inexpressiveness in View Design

We study the design of data publishing mechanisms that allow a collection of autonomous distributed data sources to collaborate to support queries. A common mechanism for data publishing is via views : functions that expose derived data to users, usually specified as declarative queries. Our autonomy assumption is that the views must be on individual sources, but with the intention of supporting integrated queries. In deciding what data to expose to users, two considerations must be balanced. The views must be sufficiently expressive to support queries that users want to ask—the utility of the publishing mechanism. But there may also be some expressiveness restrictions. Here, we consider two restrictions, a minimal information requirement, saying that the views should reveal as little as possible while supporting the utility query, and a non-disclosure requirement, formalizing the need to prevent external users from computing information that data owners do not want revealed. We investigate the problem of designing views that satisfy both expressiveness and inexpressiveness requirements, for views in a restricted information systems - query languages (conjunctive queries), and for arbitrary views.
A Nonlinear Error Compensator for FDM 3D Printed Part Dimensions Using a Hybrid Algorithm Based on GMDH Neural Network

Following the advances in Computer-Aided Design (CAD) and Additive Manufacturing (AM), with regards to the numerous benefits of the Fused Deposition Modeling (FDM) as a popular AM process, resolving its weaknesses has become increasingly important. A serious problem of the FDM is the dimensional error or size difference between the CAD model and the actual 3D printed part.In this study, the approach is compensating the error regardless of its source. At First, all parameters affecting the dimensional accuracy of FDM are comprehensively identified. Then, multi-input–single-output (MISO) data is prepared by designing experiments using the Taguchi method and obtaining the results from 3D printed samples. Next, a GMDH neural network is applied, which uses a simple nonlinear regression formula in each neuron but can create very complex neuron combinations. So, it is possible to analyze small or even noisy data. Regulatory parameters of the Neural Net have been optimized to increase efficiency. The case study shows a decrease in the RSME for the Nominal CAD Model from 0.377 to 0.033, displaying the compensator's efficiency.
Robot path planning based on improved ant colony algorithm

It is easy to arise problems such as early search time is long, get stuck at local optimum easily and convergence speed is slow when traditional ant colony algorithm is uesed to path planning, Therefore, this paper put forwards an improved ant colony algorithm. According to the evaluation function of A* algorithm, the heuristic function is optimized, and the weight coefficient is adjusted dynamically and thus raise the efficiency of search path through algorithm, which is conducive to global optimization. The grid method is applied to create obstacle environment for simulation. The experimental results indicate that the improved algorithm can plan a shorter path and the convergence speed is significantly faster.
A Preprocessing and Feature Extraction Method of Ground-based Cloud Images for Photovoltaic Power Prediction

The photovoltaic system is affected by many factors, so its ultra-short-term prediction faces enormous challenges. Total Sky Imager (TSI) is used for sky monitoring, which is of great significance for photovoltaic power prediction, but its structural characteristics lead to distortion and loss of information. A method of cloud image preprocessing and feature extraction is proposed in this paper. Firstly, the template calibration method is proposed to establish the distortion model, and bilinear interpolation is used for gray reconstruction. Secondly, the method of combining Multi-Scale Retinex (MSR) method with grayscale transformation is proposed to compensate the illumination of cloud image. Then, digital image processing technology is used to extract spectral features, texture features, cloud features, shadow-band features, and other features that affect the photovoltaic power. Finally, the Gradient Boosting Decision Tree (GBDT) method is used to train and build the photovoltaic power regression model according to ground-based cloud images and photovoltaic power. Experiments show that the proposed preprocessing method of cloud image can restore the real information of the sky, and the photovoltaic power regression model constructed from the image features has high accuracy, and the R-Squared(R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) can reach 0.96. It provides an effective method for the ultra-short-term prediction of photovoltaic power.
Voice Assistants for Speech Therapy

Many individuals with speech disorders are unable to access speech therapies due to a lack of trained professionals and available resources. To address this issue, there has been an increased focus on using eHealth technologies for remote therapy delivery. For example, there exists a number of mobile applications that aim to deliver different speech therapy exercises (e.g., listening to an audio prompt and repeating it). However, current approaches often are not able to provide personalized feedback or adapt to users’ unique needs. Such limitations can impede skill development for practical communications. Furthermore, the current approaches might not support therapeutic alliance — a key factor in determining speech therapy outcomes. In this challenge paper, we explore how voice assistants (VAs) can potentially enable remote delivery of speech therapies at scale. We also describe design challenges that need to be addressed before VAs can be used to deliver speech therapies.
Data Mining Techniques in Direct Marketing on Imbalanced Data using Tomek Link Combined with Random Under-sampling

Determining the potential customers is very important in direct marketing. Data mining techniques are one of the most important methods for companies to determine potential customers. However, since the number of potential customers is very low compared to the number of non-potential customers, there is a class imbalance problem that significantly affects the performance of data mining techniques. In this paper, different combinations of basic and advanced resampling techniques such as Synthetic Minority Oversampling Technique (SMOTE), Tomek Link, RUS, and ROS were evaluated to improve the performance of customer classification. Different feature selection techniques are used in order the decrease the number of non-informative features from the data such as Information Gain, Gain Ratio, Chi-squared, and Relief. Classification performance was compared and utilized using several data mining techniques, such as LightGBM, XGBoost, Gradient Boost, Random Forest, AdaBoost, ANN, Logistic Regression, Decision Trees, SVC, Bagging Classifier based on ROC AUC and sensitivity metrics. A combination of Tomek Link and Random Under-Sampling as a resampling technique and Chi-squared method as feature selection algorithm showed superior performance among the other combinations. Detailed performance evaluations demonstrated that with the proposed approach, LightGBM, which is a gradient boosting algorithm based on decision tree, gave the best results among the other classifiers with 0.947 sensitivity and 0.896 ROC AUC value.
A Generalization of Ordered-Pair Semantics

In this paper, we generalize the ordered-pair semantics advanced by Matthew Clemens for the Logic of Paradox (LP) to n-tuple semantics, for each fixed n. Moreover, we show that the resulting semantics can accommodate not only LP, but also classical logic as well as strong Kleene logic depending on the set of designated (or distinguished) values that one chooses. Building on the technical observations, we offer intuitively plausible readings for the semantics, and we also discuss some weaknesses of the original intuitive reading advanced by Clemens.
A Multi-perspective Narrative-Based Geovisualization Dashboard for the 2020 US Presidential Election

In this paper, we design and implement a map dashboard that combines spatio-temporal visualization and interactive narrative to comprehensively illustrate the 2020 US presidential election. Specifically, our dashboard takes campaign rallies and major events as narrative clues and integrates multi-perspective factors (e.g., the spatial spread of COVID-19, social distancing adherence, poll results) for visualization and statistical analysis. Compared with traditional methods and products, our integrated multi-perspective solution better balances the narrative property and the geovisualization property of a dashboard, making it suitable for illustrating social or political events that happened on a large geographic scale. The result shows that our narrative-based geovisualization dashboard may be used for demonstrating and associating multiple factors with partisanship and has the potential to help users explore the interaction between policies controlling COVID-19, social distancing, and partisanship across the country during the 2020 US presidential election.
Distribution of Internet Banking Credentials Using Visual Cryptography and Watermarking Techniques

Distribution of Internet banking credentials like user ID, login password, and transactional password is the most critical step and prone to forgery and hacking. We proposed a methodology by which banks can distribute the same in a much safer way. This methodology uses visual cryptography and digital watermarking techniques to encrypt the user login password and transactional password to be embedded into the user login ID. One part of the secret is being digitally transmitted and another trough the traditional postal services. This system also shows that reconstructed image quality is very good in respect to other visual cryptography schemes.
Genetic Programming using Cooperative Coevolution and Problem Decomposition for Solving Large-scale Symbolic Regression Problems

Symbolic regression using genetic programming (SRGP) is one of the most popular machine learning approaches for building human-readable interpretable models. At the same time, SRGP usually fails in solving high-dimensional problems. Large-scale problems lead to rapid bloating of trees and require special techniques for preventing always-destructive genetic operations and the loss of variables in trees. In the study, we have proposed a novel approach, which performs random decomposition of large-scale symbolic regression problems into sub-problems with less number of variables and uses cooperative coevolution for merging sub-solutions at the fitness evaluation stage. We have discussed the general conception and some alternative realizations. The results of numerical experiments using some large-scale artificial and real-world benchmark problems have demonstrated that the proposed approach outperforms the standard SRGP algorithm and some of its variations.
Teachable moments in functional audio processing

The atomic entity of digital audio processing systems is a digital audio signal, i.e. a sequence of sound samples that represent the amplitude of a sound wave at discrete time intervals. Such signals are transformed additively, by combining them into more complex signals, and subtractively, by subjecting them to digital filters. In order to cover digital audio processing in a classroom from first principles, we need to form collections of samples in streams or arrays, and define operations on these collections in accordance with the constraints of digitization. In this work, we pursue an alternative approach, where the atomic entity is a continuous wave function. We present additive synthesis operations, including wave envelopes and musical abstractions in a purely functional setting. The final continuous wave function is digitized in order to make the sound audible. We report our experiences with what we call functional audio processing as an example domain for teaching functional programming to first-year students, where simplicity and conceptual elegance outweighs the inherent limitation to additive synthesis. We describe a sequence of teachable moments that highlight the potential of functional audio processing at an early stage in the learning process, before streams or arrays are introduced.
Adversarial Learning of Cancer Tissue Representations

Deep learning based analysis of histopathology images shows promise in advancing the understanding of tumor progression, tumor micro-environment, and their underpinning biological processes. So far, these approaches have focused on extracting information associated with annotations. In this work, we ask how much information can be learned from the tissue architecture itself.We present an adversarial learning model to extract feature representations of cancer tissue, without the need for manual annotations. We show that these representations are able to identify a variety of morphological characteristics across three cancer types: Breast, colon, and lung. This is supported by 1) the separation of morphologic characteristics in the latent space; 2) the ability to classify tissue type with logistic regression using latent representations, with an AUC of 0.97 and \(85\%\) accuracy, comparable to supervised deep models; 3) the ability to predict the presence of tumor in Whole Slide Images (WSIs) using multiple instance learning (MIL), achieving an AUC of 0.98 and \(94\%\) accuracy.Our results show that our model captures distinct phenotypic characteristics of real tissue samples, paving the way for further understanding of tumor progression and tumor micro-environment, and ultimately refining histopathological classification for diagnosis and treatment (The code and pretrained models are available at: https://github.com/AdalbertoCq/Adversarial-learning-of-cancer-tissue-representations).