Reviewerly/exHarmony huggingface dataset:
A majorization penalty method for SVM with sparse constraint

Support vector machine (SVM) is an important and fundamental technique in machine learning. Soft-margin SVM models have stronger generalization performance compared with the hard-margin SVM. Most existing works use the hinge-loss function which can be regarded as an upper bound of the 0–1 loss function. However, it cannot explicitly control the number of misclassified samples. In this paper, we use the idea of soft-margin SVM and propose a new SVM model with a sparse constraint. Our model can strictly limit the number of misclassified samples, expressing the soft-margin constraint as a sparse constraint. By constructing a majorization function, a majorization penalty method can be used to solve the sparse-constrained optimization problem. We apply Conjugate-Gradient (CG) method to solve the resulting subproblem. Extensive numerical results demonstrate the impressive performance of the proposed majorization penalty method.
The forgotten preconditions for a well-functioning internet

For decades, proponents of the Internet have promised that it would one day provide a seamless way for everyone in the world to communicate with each other, without introducing new boundaries, gatekeepers, or power structures. What happened? This article explores the system-level characteristics of a key design feature of the Internet that helped it to achieve widespread adoption, as well as the system-level implications of certain patterns of use that have emerged over the years as a result of that feature. Such patterns include the system-level acceptance of particular authorities, mechanisms that promote and enforce the concentration of power, and network effects that implicitly penalise those who do not comply with decisions taken by privileged actors. We provide examples of these patterns and offer some key observations, toward the development of a general theory of why they emerged despite our best efforts, and we conclude with some suggestions on how we might mitigate the worst outcomes and avoid similar experiences in the future.
Weibull Regression Model on Hospitalization Time Data of COVID-19 Patients at Abdul Wahab Sjahranie Hospital Samarinda

The Weibull regression model is a Weibull distribution that is directly influenced by covariates. The Weibull regression model discussed in this study was the Weibull survival and the Weibull hazard regression model. The Weibull regression model in this study was applied to the hospitalization time data of COVID-19 patients from May to September 2021 at the RSUD Abdul Wahab Sjahranie Samarinda. The event of the study is recovery of patient. This study aims to obtain Weibull survival and hazard regression model to the hospitalization time data of Covid-19 patients, to obtain the factors that affect the chance of not recovering (survive) and the recovery rate of Covid-19 patients, and also to interpret Weibull survival and hazard regression models based on the obtained model. In this study, the Maximum Likelihood Estimation (MLE) was used as the parameter estimation method. The closed form of the Maximum Likelihood (ML) estimator cannot be found analytically, and the approximation of ML estimator was found using Newton-Raphson iterative method. Based on the test results, the factors that influence the chance of not recovering and the recovery rate of COVID-19 patients were comorbidities history. The chance of not not recovering (survive) for patients who have a history of comorbidities is greater than the chance of not recovering (survive) for patients who have no history of comorbidities. The recovery rate for COVID-19 patients who have a history of comorbidities is 0,5358 times the recovery rate for patients without a history of comorbidities.
Pemanfaatan Teknologi Smartphone dalam Pembuatan Media Pembelajaran pada Pembelajaran Daring Seni Budaya di Masa Pandemi

The phenomenon of the COVID-19 pandemic that has occurred over the past 2 years has entered every line of human life around the world.No exception, the line of education has changed the face-to-face learning process into online learning.Simultaneously a new teaching situation occurred, the teachers tried to make remote teaching emergency at every level of education.It challenges Cultural Arts teachers to change their teaching practice by leveraging technology to teach and reach students remotely.Arts and Culture teachers can take advantage of technological support in schools so that learning runs effectively and efficiently.It needs to be supported by the ability of Cultural Arts teachers to create learning media to support interesting and fun learning during the pandemic.Learning media innovation can be done by building a multimedia-based application by utilizing existing smartphone technology.Various applications are offered to make it easier for students to understand cultural arts learning messages in a practical way and save time in online learning during the covid-19 pandemic.
Learning Sentence-Level Representations with Predictive Coding

Learning sentence representations is an essential and challenging topic in the deep learning and natural language processing communities. Recent methods pre-train big models on a massive text corpus, focusing mainly on learning the representation of contextualized words. As a result, these models cannot generate informative sentence embeddings since they do not explicitly exploit the structure and discourse relationships existing in contiguous sentences. Drawing inspiration from human language processing, this work explores how to improve sentence-level representations of pre-trained models by borrowing ideas from predictive coding theory. Specifically, we extend BERT-style models with bottom-up and top-down computation to predict future sentences in latent space at each intermediate layer in the networks. We conduct extensive experimentation with various benchmarks for the English and Spanish languages, designed to assess sentence- and discourse-level representations and pragmatics-focused assessments. Our results show that our approach improves sentence representations consistently for both languages. Furthermore, the experiments also indicate that our models capture discourse and pragmatics knowledge. In addition, to validate the proposed method, we carried out an ablation study and a qualitative study with which we verified that the predictive mechanism helps to improve the quality of the representations.
Does the Cryptocurrency Index Provide Diversification Opportunities With MSCI World Index and MSCI Emerging Markets Index?

The study is extending the ongoing discussion on Bitcoin as a diversification asset with the stock market. Some studies analysed cryptocurrencies as a diversification asset, and few challenged the same. During times of turbulence, it is crucial to gauge further diversification opportunities. Henceforth, the study revisits the opportunities of hedging and diversification with the crypto market from a broader perspective. The study captures the spillover from MSCI World Index and MSCI Emerging Markets Index to Bitwise 10 Crypto Index Fund (BITW). The study has contributed methodologically to the existing literature by applying DY with symmetric and asymmetric dynamic conditional volatility models. The results provide in-depth shreds of evidence that BITW is insulated, neither taking volatilities from other countries nor contributing to the volatilities of other countries. The study provides insight to policymakers and investors.
Comprehension Problems

The studies on comprehension problems carried out within the framework of this project (Lehtinen, 2012; Wiklund, 2016; Wiklund & Laakso, 2019, 2020) confirm the results of previous studies (e.g., Geller, 1998; Keen, 2003), according to which children with ASD often experience communication breakdowns in their interactions. Unlike previous studies, however, in these data, the comprehension problems did not lead to problem behaviour (Brady et al., 1995; Keen, 2003; Wetherby et al., 1998). The results are in many respects in line with those of Geller (1998). Indeed, according to Geller (1998), most of the communication breakdowns in the interactions of school-aged children with ASD are caused by a lack of referential clarity, which is also typical of the data studied within the framework of this project (Lehtinen, 2012; Wiklund, 2016). In addition, ‘topical discontinuities’ constitute one of the most common causes of comprehension problems in these data (Lehtinen, 2012; Wiklund, 2016). ‘Ungrammatical utterances’ and ‘disfluencies’ cause a certain number of comprehension problems between the participants as well (Wiklund & Laakso, 2019, 2020). The most common cause of comprehension problems in these data was ‘overly literal understanding of speech’. In 84.1% of instances, there was no eye contact between the speaker producing a trouble-source turn and the person initiating the repair sequence (or making an outright correction). Sometimes the lack of eye contact was associated with overlapping speech (38.1%).
The Listener and the Work as the Dualistic Basis for the Morphological Analysis of Music

In morphological analysis, the musical work and the listener are seen as elements of one communicative duality. Each element of this duality may play the role of either subject or object. Morphological analysis takes sound construction as its main target. This makes possible a more flexible approach to analyzing a musical text. The morpheme, one of the cornerstones of morphological analysis, can be defined as a sound construction with a typical set of characteristic features. The other cornerstone, the morph, transforms a morpheme into a genеriс, stylistic “flesh and blood” of a specific musical text, on the basis of polymorphism. From the point of view of morphological analysis, the musical development in the “Introduction” to Stravinsky’s The Rite of Spring is based on a step-by-step approach to an externally existing object, permitting us to perceive (see or hear) its details. The object itself is polymorphic, i.e., like an embryo, it contains within itself, from the start, every element it needs for further development. The starting point of the “Introduction,” the high-register bassoon melody accompanied by the supporting French horn voice, can be defined as a forest seen in a distance, from where the sound of a shepherd’s horn can be heard. The ten intonational elements of the initial three measures of the ballet are the base from which the form of the “Introduction” is developed.
Facial attribute classification by deep mining inter‐attribute correlations

Abstract Face attribute classification (FAC) has received considerable attention due to its excellent application value in bio‐metric verification and face retrieval. Current FAC methods suffer two typical challenges: complex inter‐attribute correlations and imbalanced learning. Aims at the challenges, presents an end‐to‐end FAC framework with integrated use of multiple strategies, which consists of a convolutional neural network (CNN) and a graph convolutional network (GCN). The GCN is used to model the semantic correlations among attributes and capture inter‐dependency among them. The correlation information learnt via the GCN is used to guide the learning of the inter‐dependent classification features of the FAC network. An adaptive thresholding strategy and a boosting scheme are adopted to alleviate the effect of the class‐imbalance. To deal with the task imbalance problem, a new dynamic weighting scheme is proposed to update the weight of each attribute classification task in the training process. We apply four evaluation metrics to evaluate the proposed method. Experimental results show all the proposed strategies are effective, and our approach outperforms state‐of‐the‐art FAC methods on two challenging datasets CelebA and LFWA.
What is a related work? A typology of relationships in research literature

An important part of research is situating one's work in a body of existing literature, thereby connecting to existing ideas. Despite this, the various kinds of relationships that might exist among academic literature do not appear to have been formally studied. Here I present a graphical representation of academic work in terms of entities and relations, drawing on structure-mapping theory (used in the study of analogies). I then use this representation to present a typology of operations that could relate two pieces of academic work. I illustrate the various types of relationships with examples from medicine, physics, psychology, history and philosophy of science, machine learning, education, and neuroscience. The resulting typology not only gives insights into the relationships that might exist between static publications, but also the rich process whereby an ongoing research project evolves through interactions with the research literature.
Controllable facial attribute editing via Gaussian mixture model disentanglement

Generative adversarial networks (GANs) have made much progress in the field of high-quality and realistic facial image synthesis in recent years. However, compared with their powerful generation ability, it is difficult for users to modify the desired attributes of the resulting image while keeping the others. How to disentangle the latent space of pre-trained GANs is essential and critical for controllable image synthesis. In this paper, a novel controllable facial attribute editing algorithm based on the Gaussian mixture model (GMM) representation is proposed. First, we assume that the latent variables with respect to each facial attribute lie in a subspace of the whole latent manifold composed of a fixed number of learned features, and each attribute subspace can be modeled by a GMM. Then, to avoid unintended changes during attribute editing, a coordinate accumulation strategy with orthogonal regularization is introduced to enhance the independence of distinct attribute subspaces which helps improving the controllability of attribute editing. In addition, a resampling strategy is utilized to improve the stability of the model. Through qualitative and quantitative experimental results, the proposed method achieves the state-of-the-art performance on facial attribute editing, and improves the controllability of desired attribute editing.
Feature Fusion Based Parallel Graph Convolutional Neural Network for Image Annotation

Graph neural networks’ application in automatic image annotation is becoming more mature. However, there are still several problems. First, the feature data of the original image obtained by the feature extraction algorithm, such as color features and gradient features, all have the problem of slight intra-class variance and significant inter-class variance. Second, merely utilize the graph convolution neural networks to construct samples or labeled graphs, limiting multimodality’s fusion and expansion. This paper uses a parallel graph convolution network based on feature fusion for automatic image annotation. By fusing the sample features, the inherent defects of the features extracted by a single model are reduced, and the annotation performance under the condition of semi-supervised learning is improved. Experiments on three benchmark image annotation datasets show that this method is superior to the existing methods.
An Adaptive Window Shape-Based DIC Method for Large Torsional Deformation Measurement

Torsional deformation measurement is used to measure the resistance of materials to torque and is one of the basic tests for the mechanical properties of materials. The measurement of large torsional deformation based on the digital image correlation (DIC) method has the advantages of full-field measurement, noncontact measurement, and easy-to-implement, but there are also problems, such as decorrelation and out-of-view. To tackle these problems, we propose an adaptive window shape-based DIC method and a reverse matching method, based on which a large torsional deformation measurement scheme is formed. First, taking advantage of the temporal continuity of object deformation, the window shape adaptively changes with the torsion of the specimen to avoid decorrelation. To solve the problem of tracking points moving out of the camera’s field of view, the deformed subregions are reversely matched in the reference image set, which is preshot from different directions. By this, the proposed method greatly improves the measurement range, and as a tradeoff, the absolute accuracy is acceptably sacrificed. Torsion tests of cylindrical low carbon steel specimen show that the proposed method can measure large torsional deformation objects with a shear angle of 74°, and the relative error is about 0.43%.
RGB-D Face Recognition With Identity-Style Disentanglement and Depth Augmentation

Deep learning approaches achieve highly accurate face recognition by training the models with huge face image datasets. Unlike 2D face image datasets, there is a lack of large 3D face datasets available to the public. Existing public 3D face datasets were usually collected with few subjects, leading to the over-fitting problem. This paper proposes two CNN models to improve the RGB-D face recognition task. The first is a segmentation-aware depth estimation network, called DepthNet, which estimates depth maps from RGB face images by exploiting semantic segmentation for more accurate face region localization. The other is a novel segmentation-guided RGB-D face recognition model that contains an RGB recognition branch, a depth map recognition branch, and an auxiliary segmentation mask branch. In our multi-modality face recognition model, a feature disentanglement scheme is employed to factorize the feature representation into identity-related and style-related components. DepthNet is applied to augment a large 2D face image dataset to a large RGB-D face dataset, which is used for training our RGB-D face recognition model. Our experimental results show that DepthNet can produce more reliable depth maps from face images with the segmentation mask. Our multi-modality face recognition model fully exploits the depth map and outperforms state-of-the-art methods on several public 3D face datasets with challenging variations.
Dynamic Group Difference Coding Based on Thermal Infrared Face Image for Fever Screening

Recently, noncontact temperature measurement methods based on infrared face perception have received widely attentions since fever screening plays an important role in the early prediction of respiratory infections, such as SARS, H1N1, and COVID-19. However, the performance of these methods always significantly degrades when facing the changes of environment. Thus, the majority of these methods leverage the block-body and sensors to reduce the influence of environment changes. It is a pity that the increased instrument complexity leads to higher costs and failure rate. To address the aforementioned issues, this article presents a novel fever screening method, named dynamic group difference coding (DGDC), which is based on the analysis about the influencing factors. The key idea of DGDC is to compute the temperature differences between the target person and the recently passed crowd (dynamic group). Specifically, we develop the face temperature encoder (FTE) to describe the face temperature and thus construct the difference matrix of the embedding feature between the target person and the dynamic group. Multilayer perceptions (MLP) are employed to capture the intrinsic information by characterizing the difference matrix in vertical and horizontal directions, respectively. Finally, we provide a dataset of thermal infrared face (TIF) images and conduct extensive experiments to demonstrate the advantages of the proposed method over the competing methods.
Explainable Federated Learning: A Lifecycle Dashboard for Industrial Settings

As the adoption of federated learning (FL) in the manufacturing industry grows and systems get increasingly complex, a need to inspect their behavior arises. Stakeholders of the FL process want a more transparent system to understand the current state and analyze how its performance changed over time. However, current representation approaches are often not designed for industrial applications and do not cover the entire FL model lifecycle. We propose the lifecycle dashboard, which considers the different requirements and perspectives of industrial stakeholders by visualizing information from the FL server. In addition, our representation approach is generic enough to be applied to different use cases and industries. We evaluate the lifecycle dashboard in a semistructured expert interview, show improvements in the understandability of FL systems, and discuss possible use cases in the industry.
Cross Apprenticeship Learning Framework: Properties and Solution Approaches

Apprenticeship learning is a framework in which an agent learns a policy to perform a given task in an environment using example trajectories provided by an expert. In the real world, one might have access to expert trajectories in different environments where system dynamics is different while the learning task is the same. For such scenarios, two types of learning objectives can be defined. One where the learned policy performs very well in one specific environment and another when it performs well across all environments. To balance these two objectives in a principled way, our work presents the cross apprenticeship learning (CAL) framework. This consists of an optimization problem where an optimal policy for each environment is sought while ensuring that all policies remain close to each other. This nearness is facilitated by one tuning parameter in the optimization problem. We derive properties of the optimizers of the problem as the tuning parameter varies. We identify conditions under which an agent prefers using the policy obtained from CAL over the traditional apprenticeship learning. Since the CAL problem is nonconvex, we provide a convex outer approximation. Finally, we demonstrate the attributes of our framework in the context of a navigation task in a windy gridworld environment.
Robust lockwire segmentation with multiscale boundary-driven regional stability

Lockwire segmentation plays a vital role in ensuring mechanical safety in industrial fields. Aiming at the missed detection problem encountered in blurred and low-contrast situations, we propose a robust lockwire segmentation method based on multiscale boundary-driven regional stability. We first design a novel multiscale boundary-driven stability criterion to generate a blur-robustness stability map. Then, the curvilinear structure enhancement metric and linearity measurement function are defined to compute the likeliness of stable regions to belong to lockwires. Finally, the closed boundaries of lockwires are determined to achieve accurate segmentation. Experimental results demonstrate that our proposed method outperforms state-of-the-art object segmentation methods.