Reviewerly/exHarmony huggingface dataset:
Practical Federated Learning Infrastructure for Privacy-Preserving Scientific Computing

Federated learning (FL) is deemed a promising paradigm for privacy-preserving data analytics in collaborative scientific computing. However, there lacks an effective and easy-to-use FL infrastructure for scientific computing and high-performance computing (HPC) environments. The objective of this position paper is two-fold. Firstly, we identify three missing pieces of a scientific FL infrastructure: (i) a native MPI programming interface that can be seamlessly integrated into existing scientific applications, (ii) an independent data layer for the FL system such that the user can pick the persistent medium for her own choice, such as parallel file systems and multidimensional databases, and (iii) efficient encryption protocols that are optimized for scientific workflows. The second objective of this paper is to present a work-in-progress FL infrastructure, namely MPI-FL, which is implemented with PyTorch and MPI4py. We deploy MPI-FL on 1,000 CPU cores and evaluate it with four standard benchmarks: MNIST, Fashion-MNIST, CIFAR-10, and SVHN-extra. It is our hope that the scientific computing and HPC community would find MPI-FL useful for their FL-related projects.
Step-by-Step Process of Building Voices for Under Resourced Languages using MARY TTS Platform

This paper presents a comprehensive guide for creating synthetic voices to support under resourced languages for the MaryTTS platform. Although researchers have extensively contributed in the domain of speech synthesis, the lack of a thorough documentation hinders the voice building process for languages not yet supported by MaryTTS, complicating the implementation process for users with inadequate knowledge in the field of Text-to-Speech (TTS). The step-by-step process discussed in this study is further demonstrated with the creation of a synthetic voice for the Sinhala language, with unit selection as the voice building approach. A Sinhalese voice was generated with an intelligibility score of 91.7% upon evaluation with Diagnostic Rhyme Test (DRT). Comparison with ground truth data proved a close approximation to human speech where the intelligibility score was identified as 97.9%, when tested with the same participants. The Mean Opinion Score (MOS) revealed a naturalness level of 2.993, indicating a moderately high speech quality for the proposed system in comparison with the ideal score of 4.972.
Improving Kullback-Leibler based legal document summarization using enhanced text representation

Text summarization has a very high applicability in legal domain due to their complex nature. There are several classical algorithms that can show promising results on legal documents. One such algorithm is Kullback-Leibler based summarization (KLSumm), where documents and candidate summaries are represented through unigram probability distributions and summary sentences are chosen based on minimizing KL-divergence between documents and candidate summaries. The choice of probability distribution has a great impact on choosing the summary sentences from the document. In this work, two approaches are explored for improving the formation of probability distributions, viz. NgramKLSumm and BertKLSumm. From the experimental results, we find that, NgramKLSumm approach performs better on the BillSum dataset in terms of both ROUGE and BERTScore metrics; whereas BertKLSumm performs better on the FIRE dataset in terms of ROUGE metric. In the case of BillSum dataset, the improvement is around 5-13% for BertKLSumm approach; whereas nearly 10-16% improvement is seen for NgramKLSumm approach in terms of ROUGE metrics. In terms of BertScore, the improvement is around 2% F1-score for BertKLSumm approach, while 6% F1-score improvement is seen in the case of NgramKLSumm approach. In the case of FIRE dataset, 2-5% improvement is seen for the ROUGE metrics while no improvement is seen in the case of BertScore. From these results it is clear that with an enhanced representation of documents and candidate summaries, it is possible to obtain great improvements across different datasets as well as evaluation metrics, thereby improving the baseline KLSumm approach for legal document summarization.
A Synthetic Hybrid Approach for Class Imbalance

One of the significant challenges in Data Mining and Machine Learning is class imbalance during data processing. It refers to the situation when the samples belonging to one particular class in a dataset are much more than the samples of other classes. It causes misclassification chaos and lessens the performance of the algorithms to build real-world applications. As a result, any models that are trained on an imbalanced dataset are likely to be biased. In this paper, we have reported a hybrid approach where, we have generated a synthetic dataset based on the original dataset and merged the datasets to make a master dataset. The main objective is to leverage accuracy and improve model performance. The effectiveness of our work are shown in the terms of precision, recall and accuracy. Better results have been achieved in contrast to using the original dataset.
JOWTS: A differential evolution based approach for Microblog Summarization with advanced population enhancement techniques

Social networking services have emerged as the main sources for real-time information about events happening. It has been observed that pertinent information gleaned from tweets during catastrophic events can be helpful in a variety of ways. Therefore, it is necessary to create an automated microblog summarization system. The proposed approach JOWTS, confluence of a wide range of evolutionary computation techniques such as the well-known differential evolutionary algorithm JADE (DE/current-to-pbest/1), Opposition-based Learning (OBL) and Whale Optimization Algorithm (WOA), employs multi-objective optimization for microblog summarization. The summarization task is formulated as a multi-objective optimization problem and combination of objectives such as tweet length & importance of tweets (through tf-idf technique) in a dataset are optimized at the same time. For evaluation, datasets relevant to disaster events are employed and the results are compared to different alternative methodologies utilizing ROUGE measures. When compared against the contemporary evolutionary techniques, it was observed that JOWTS improves ROUGE-1, 2, L scores by 3.86%, 8.53% and 4.69% respectively.
On Preferring Joy

This paper examines how "Joy" is presented in several knowledge organization systems: subject heading lists, thesauri, and one ontology. Some use "Joy" as a preferred term, one combines it with another term in a compound preferred term, and others include "Joy" as a pointer to a different preferred term. The paper describes and compares various understandings of "Joy" as presented by these systems, summarizes the results, and makes some observations about aspects of these systems brought to the fore by the analysis.
LPF-Defense: 3D adversarial defense based on frequency analysis

The 3D point clouds are increasingly being used in various application including safety-critical fields. It has recently been demonstrated that deep neural networks can successfully process 3D point clouds. However, these deep networks can be misclassified via 3D adversarial attacks intentionality designed to perturb some point cloud's features. These misclassifications may be due to the network's overreliance on features with unnecessary information in training sets. As such, identifying the features used by deep classifiers and removing features with unnecessary information from the training data can improve network's robustness against adversarial attacks. In this paper, the LPF-Defense framework is proposed to discard this unnecessary information from the training data by suppressing the high-frequency content in the training phase. Our analysis shows that adversarial perturbations are found in the high-frequency contents of adversarial point clouds. Experiments showed that the proposed defense method achieves the state-of-the-art defense performance against six adversarial attacks on PointNet, PointNet++, and DGCNN models. The findings are practically supported by an expansive evaluation of synthetic (ModelNet40 and ShapeNet) and real datasets (ScanObjectNN). In particular, improvements are achieved with an average increase of classification accuracy by 3.8% on Drop100 attack and 4.26% on Drop200 attack compared to the state-of-the-art methods. The method also improves models' accuracy on the original dataset compared to other available methods. (To facilitate research in this area, an open-source implementation of the method and data is released at https://github.com/kimianoorbakhsh/LPF-Defense.).
An Experiment on the Effects of Modularity on Code Modification and Understanding

Good modularity is seen as an important goal in software design. Achieving this goal is claimed to improve, among other things, the understandability and modifiability of a design. Yet, when teaching software design, we see that students limit the amount of modularity that they introduce into their code and cannot see the benefit of further modularity. This could be because they do not understand the benefits, but it could also be that these benefits are limited for inexperienced developers. In order to teach the benefits of modularity we need to understand what, if any, benefits exist for students. We conducted a controlled experiment where 40 students performed a modification task on two different designs, one with higher modularity than the other. Students were better able to successfully complete the task with the design with higher modularity. However, we found a trend where understanding was lower for the high modularity design. These results suggest modularity is beneficial to students, and that understanding of modularity needs to be better supported when teaching software design.
Application of an approximate geostatistical simulation algorithm to delineate the gold mineralized zones characterized by fractal methodology

The Zarshuran Carlin-type gold deposit is located in the NW part of the Sanandaj-Sirjan metamorphic zone which hosts Neoproterozoic carbonate and black shale rock units. Concentration-Volume (C–V) fractal model and Turning Band Simulation method have been applied to effectively separate gold mineralized stages in the Zarshuran deposit. Three mineralized stages determined by the means of C–V fractal model and Turning Bands Simulation method were correlated with the mineralized stages considering geological studies. The results revealed the presence of enriched (>37 ppm) and Au high grade (5.5–37 ppm) mineralized zones within the deposit. A log ratio matrix was used to correlate the results obtained by the C–V fractal model and turning band simulation for Au and lithological units which showed that the main mineralized zones occur in the black gouges and Jasperoids.
THE CONCEPT OF FORMATION OF PROFESSIONAL COMPETENCE AND SOCIOPSYCHOLOGICAL FACTORS OF PROFESSIONAL EXPECTATIONS OF STUDENTS OF SOCIOLOGISTS

The concept of forming the professional competence of students of sociologists consists of elements that form their professional competence. Currently, the processes of digitalization of society have a serious impact on the need to develop adaptability and mobility skills, which implies the availability of appropriate knowledge and skills. Proper professional training of a sociologist student will allow you to acquire positive socio-psychological expectations that will determine his field of activity. A distinctive feature of the educational activity of such a student is the need for professional self-determination, which requires the use of certain resources. The formation of a sociologist student as a competent specialist goes through several stages throughout the entire period of study, which allows you to form the necessary skills and abilities to continue your professional activity and career growth after graduation from university.
Self‐supervised non‐rigid structure from motion with improved training of Wasserstein GANs

Abstract This study proposes a self‐supervised method to reconstruct 3D limbic structures from 2D landmarks extracted from a single view. The loss of self‐consistency can be reduced by performing a random orthogonal projection of the reconstructed 3D structure. Thus, the training process can be self‐supervised by using geometric self‐consistency in the reconstruction–projection–reconstruction process. The self‐supervised network mainly consists of graph convolution and Transformer encoders. This network is called the SS‐Graphformer. By adding a discriminator, the SS‐Graphformer is used as a generator to form a Wasserstein Generative Adversarial Network architecture with a Gradient Penalty to improve the accuracy of the reconstruction. It is experimentally demonstrated that the addition of the 2D structure discriminator can significantly improve the accuracy of the reconstruction.
Semantics recalibration and detail enhancement network for real‐time semantic segmentation

Abstract Real‐time semantic segmentation is a crucial technology in automatic driving scenarios, which needs to meet both high precision and real‐time. The authors observe that learning complex correlations between object categories is vital in the real‐time semantic segmentation task. Moreover, image spatial detail information plays an important role in small object segmentation and preserving edges and textures. A Semantics Recalibration and Detail Enhancement Network for real‐time semantic segmentation based on BiSeNet V2 is proposed. On the one hand, a lightweight Semantics Recalibration module is designed to effectively extract global semantic contextual information, which combines pyramid segmentation and adaptive recalibration operations to learn the correlations between object categories. On the other hand, a Detail Enhancement module takes the feature maps of the shallow layers in the semantics branch as input and refines the feature maps to highlight the detail information. Finally, quantitative and qualitative analyses on Cityscapes and CamVid datasets demonstrate the effectiveness and generalisation of the proposed method.
Improved Cascade Correlation Neural Network Model Based on Group Intelligence Optimization Algorithm

The Cascade Correlation learning algorithm is a special supervised learning algorithm for artificial neural network architecture. The optimization algorithm in the traditional neural network has the disadvantages of a single optimization goal, slow convergence speed, and can easily fall into local area, which cannot fully meet the key elements in the cascade correlation learning algorithm. In comparison, the group intelligence optimization algorithm can take into account these key elements in the optimization process at the same time, and obtain better optimization results. In this paper, we propose the single-objective optimization algorithm jDE-B and the multi-objective optimization algorithm MOEA-T, and improve the network expansion mode in the learning process of Cascade Correlation neural networks. We investigate the effect of applying the group intelligent optimization algorithm in the Cascade Correlation learning algorithm. Experimental results show that our improved algorithm is able to enhance the ability of the Cascade Correlation neural network to fit problems, reduce the number of hidden units and the depth of the network, and optimize the network structure.
Generated Image Editing Method Based on Global-Local Jacobi Disentanglement for Machine Learning

Accurate semantic editing of the generated images is extremely important for machine learning and sample enhancement of big data. Aiming at the problem of semantic entanglement in generated image latent space of the StyleGAN2 network, we proposed a generated image editing method based on global-local Jacobi disentanglement. In terms of global disentanglement, we extract the weight matrix of the style layer in the pre-trained StyleGAN2 network; obtain the semantic attribute direction vector by using the weight matrix eigen decomposition method; finally, utilize this direction vector as the initialization vector for the Jacobi orthogonal regularization search algorithm. Our method improves the speed of the Jacobi orthogonal regularization search algorithm with the proportion of effective semantic attribute editing directions. In terms of local disentanglement, we design a local contrast regularized loss function to relax the semantic association local area and non-local area and utilize the Jacobi orthogonal regularization search algorithm to obtain a more accurate semantic attribute editing direction based on the local area prior MASK. The experimental results show that the proposed method achieves SOTA in semantic attribute disentangled metrics and can discover more accurate editing directions compared with the mainstream unsupervised generated image editing methods.
The Entropy of Digital Texts—The Mathematical Background of Correctness

Based on Shannon's communication theory, in the present paper, we provide the theoretical background to finding an objective measurement-the text-entropy-that can describe the quality of digital natural language documents handled with word processors. The text-entropy can be calculated from the formatting, correction, and modification entropy, and based on these values, we are able to tell how correct or how erroneous digital text-based documents are. To present how the theory can be applied to real-world texts, for the present study, three erroneous MS Word documents were selected. With these examples, we can demonstrate how to build their correcting, formatting, and modification algorithms, to calculate the time spent on modification and the entropy of the completed tasks, in both the original erroneous and the corrected documents. In general, it was found that using and modifying properly edited and formatted digital texts requires less or an equal number of knowledge-items. In information theory, it means that less data must be put on the communication channel than in the case of erroneous documents. The analysis also revealed that in the corrected documents not only the quantity of the data is less, but the quality of the data (knowledge pieces) is higher. As the consequence of these two findings, it is proven that the modification time of erroneous documents is severalfold of the correct ones, even in the case of minimal first level actions. It is also proven that to avoid the repetition of the time- and resource-consuming actions, we must correct the documents before their modification.
Analysis of College Students’ Test Scores Based on Two-Component Mixed Generalized Normal Distribution

In order to improve the fitting accuracy of college students' test scores, this paper proposes two-component mixed generalized normal distribution, uses maximum likelihood estimation method and Expectation Conditional Maxinnization (ECM) algorithm to estimate parameters and conduct numerical simulation, and performs fitting analysis on the test scores of Linear Algebra and Advanced Mathematics of F University.The empirical results show that the two-component mixed generalized normal distribution is better than the commonly used two-component mixed normal distribution in fitting college students' test data, and has good application value.
Adversarial Anchor-Guided Feature Refinement for Adversarial Defense

Adversarial training (AT), which is known as a robust training method for defending against adversarial examples, usually loses the performance of models for clean examples due to the feature distribution discrepancy between clean and adversarial. In this paper, we propose a novel Adversarial Anchor-guided Feature Refinement (AAFR) defense method aimed at reducing the discrepancy and delivering reliable performances for both clean and adversarial examples. We devise adversarial anchor that detects whether the feature comes from clean or adversarial example. Then, we use adversarial anchor to refine the feature to reduce the discrepancy. As a result, the proposed method substantially achieves adversarial robustness while preserving the performance for clean examples. The effectiveness of the proposed method is verified with comprehensive experiments on CIFAR-10, CIFAR-100, and Tiny ImageNet datasets.
Weakly Supervised Face Naming with Symmetry-Enhanced Contrastive Loss

We revisit the weakly supervised cross-modal face-name alignment task; that is, given an image and a caption, we label the faces in the image with the names occurring in the caption. Whereas past approaches have learned the latent alignment between names and faces by uncertainty reasoning over a set of images and their respective captions, in this paper, we rely on appropriate loss functions to learn the alignments in a neural network setting and propose SECLA and SECLA-B.SECLA is a Symmetry-Enhanced Contrastive Learning-based Alignment model that can effectively maximize the similarity scores between corresponding faces and names in a weakly supervised fashion. A variation of the model, SECLA-B, learns to align names and faces as humans do, that is, learning from easy to hard cases to further increase the performance of SECLA. More specifically, SECLA-B applies a two-stage learning framework: (1) Training the model on an easy subset with a few names and faces in each image-caption pair. (2) Leveraging the known pairs of names and faces from the easy cases using a bootstrapping strategy with additional loss to prevent forgetting and learning new alignments at the same time. We achieve state-of-the-art results for both the augmented Labeled Faces in the Wild dataset and the Celebrity Together dataset. In addition, we believe that our methods can be adapted to other multimodal news understanding tasks.
ElliPose: Stereoscopic 3D Human Pose Estimation by Fitting Ellipsoids

One of the most relevant tasks for augmented and virtual reality applications is the interaction of virtual objects with real humans which requires accurate 3D human pose predictions. Obtaining accurate 3D human poses requires careful camera calibration which is difficult for nontechnical personal or in a pop-up scenario. Recent markerless motion capture approaches require accurate camera calibration at least for the final triangulation step. Instead, we solve this problem by presenting ElliPose, Stereoscopic 3D Human Pose Estimation by Fitting Ellipsoids, where we jointly estimate the 3D human as well as the camera pose. We exploit the fact that bones do not change in length over the course of a sequence and thus their relative trajectories have to lie on the surface of a sphere which we can utilize to iteratively correct the camera and 3D pose estimation. As another use-case we demonstrate that our approach can be used as replacement for ground-truth 3D poses to train monocular 3D pose estimators. We show that our method produces competitive results even when comparing with state-of-the-art methods that use more cameras or ground-truth camera extrinsics.