Reviewerly/exHarmony huggingface dataset:
Geometry Enhanced Generative Adversarial Networks for Random Heterogeneous Material Representation

Abstract The representation of material structure geometry is essential to the reconstruction, physical simulation, and the multiscale structure design with Random Heterogeneous Material (RHM). Traditional approaches to material structure representation often need to balance the trade-off between efficacy and accuracy. Recently, deep learning-based techniques have been adopted to reduce the computational time of RHM reconstruction. However, existing approaches generally lack guarantees over key RHM characteristics, including Minkowski functionals and correlation functions. We propose a novel approach to geometrically enhancing the deep learning-based RHM representation by introducing Minkowski functionals, a set of topological and geometrical characteristics of material structure, into the training of conditional Generative Adversarial Networks (cGAN). This hybrid approach combines the feature learning capability of deep learning with the well-established material structure characteristics, greatly improving the accuracy of the RHM representation while maintaining its efficiency. The effectiveness of the proposed hybrid approach is validated through the reconstruction of a wide range of natural and manmade materials, including Voronoi foam structures, femur, and sandstone. Through computational experiments, we demonstrate that geometrically enhancing the training of cGAN for RHM representation not only significantly decreases the representation error in Minkowski functionals between input sample materials and reconstructed results, but also improves the performance of other material structure characteristics, such as two-point correlation functions.
Online Product Review Analysis to Automate the Extraction of Customer Requirements

Abstract The increasing use of online retail platforms has generated an enormous amount of textual data on the user experiences with these products in online reviews. These reviews provide a rich resource to elicit customer requirements for a category of products. The recent research has explored this possibility to some extent. The study reported here investigates the coding of publically available user reviews to understand customer sentiments on environmentally-friendly products. The manual review typically consists of a qualitative analysis of textual content, which is a resource-intensive process. An automated procedure based on Aspect-Based Sentiment Analysis (ABSA) is proposed and explored in this study. This procedure can be beneficial in analyzing reviews of products that belong to a specific category. As a case study, environmentally-friendly products are used. Manual content analysis and automated ABSA-based analysis are performed on the same review data to extract customer sentiments. The results show that we obtain over a 50% classification accuracy for a multiclass classification NLP task with a very elementary word vector-based model. The drop in accuracy (compared to human annotation) can be offset because an automated system is thousands of times faster than a human. Given enough data, it will perform better than its human counterpart in tasks on customer requirement modeling. We also discuss the future routes that can be taken to extend our system by leveraging more sophisticated paradigms and substantially improving our system’s performance.
Solving Min-Max Optimization with Hidden Structure via Gradient Descent Ascent

Many recent AI architectures are inspired by zero-sum games, however, the behavior of their dynamics is still not well understood. Inspired by this, we study standard gradient descent ascent (GDA) dynamics in a specific class of non-convex non-concave zero-sum games, that we call hidden zero-sum games. In this class, players control the inputs of smooth but possibly non-linear functions whose outputs are being applied as inputs to a convex-concave game. Unlike general min-max games, these games have a well-defined notion of solution; outcomes that implement the von-Neumann equilibrium of the ``hidden convex-concave game. We prove that if the hidden game is strictly convex-concave then vanilla GDA converges not merely to local Nash, but typically to the von-Neumann solution. If the game lacks strict convexity properties, GDA may fail to converge to any equilibrium, however, by applying standard regularization techniques we can prove convergence to a von-Neumann solution of a slightly perturbed min-max game. Our convergence guarantees are non-local, which as far as we know is a first-of-its-kind type of result in non-convex non-concave games. Finally, we discuss connections of our framework with generative adversarial networks.
A Novel Multilevel Lossy Compression Algorithm for Grayscale Images Inspired by the Synthesization of Biological Protein Sequences

Enormous number of images are generated daily in all areas of life, including social media, medical and navigation images. Moreover, the development of smart phones among other specialized media-capturing devices has witnessed great advances during the last decade. Consequently, the storage, transmission, and analysis of images become essential and frequent tasks. Thus, various research efforts tried to address the image compression problem from different computational perspectives. This article presents a novel multilevel lossy compression algorithm for grayscale images, namely Image-as-Protein (IaP), that is inspired by the translation of DNA sequences into protein sequences that occurs inside live beings. Because of its high similarity, the resulting textual protein sequence can then be tackled by general text compression techniques with competitive compression ratios. Various qualitative comparisons and quantitative measures such as BPP, SSIM and PSNR have been carried out on multiple grayscale image benchmark datasets. The experimental results showed that the proposed algorithm is promising compared to the famous JPEG lossy image compression standard.
MDA analysis of translated and non-translated parliamentary discourse

Contributing to De Sutter and Lefer’s (2020) new agenda for empirical translation studies (TS), we attempt to both benefit from Biber’s (1988) multidimensional analysis (MDA) and incorporate a translational perspective into Biber’s work. Regarding the former goal, we aim to avoid reductionism in corpus-based translation studies (CTS) (De Sutter & Lefer, 2020) by mainly abiding by the stages set in Biber’s (1988) work. Regarding the latter, the present chapter supplements the relatively limited number of MDA-oriented studies (see Berber-Sardinha & Veirano-Pinto, 2014, 2019), where the translation prism is absolutely underrepresented. The data used for this study come from The European Comparable and Parallel Corpus Archive of Parliamentary Discourse (ECPC). More specifically, we resort to subcorpora of 2005’s translated (i.e., EP-EN-TT-05) and non-translated (i.e., EP-EN-ST-05) speeches from the European Parliament (as published in the Official Journal of European Union) and from the House of Commons (i.e., HC-05, as published in Hansard). Aided by Nini’s Multidimensional Analysis Tagger (MAT) and Brezina’s Lancaster Stats Tool Online (LSTO), subcorpora interventions are processed and compared to Biberian dimensions. The chapter yields tentative exploratory results, which, as in all empirical studies, are to be pursued by further quantitative and qualitative analyses.
Tweeting #RemoveKebab: A Critical Study of the Far Right’s Islamophobic Hate Hashtag on Twitter

Background: To contribute to research on the transnational far right, Islamophobia, and social media platforms, this article interrogates the far right’s practice of using Twitter to produce and circulate a #removekebab hashtag. Analysis: The accounts behind the words and images of 100 #removekebab tweets are analyzed to show how they communicate the transnational far right’s hateful Islamophobic discourse. Conclusion and implications: The far right’s #removekebab tweets dehumanize Muslims, tacitly call for genocide against Muslims, and rationalize this violence by stereotyping Muslims as a collective threat to the West.
Deep discriminant generation-shared feature learning for image-based kinship verification

Kinship verification based on facial images in the wild is an interesting and challenging research topic in the fields of computer vision. It has many potential applications, e.g., missing children search, image annotation, etc. In practice, the biggest obstacle in kinship verification is that there usually exists large divergence between the images of parent and children. How to effectively tackle such challenge is important for improving kinship verification performance. To this end, we proposed a novel Deep Discriminant Generation-Shared Feature Learning for Image-Based Kinship Verification (D2GFL) method for kinship verification, which consists of a two-stream generation-specific feature learning module and a generation-shared discrepancy reducing module. The generation-specific feature learning module can learn parent-specific and child-specific features. The generation-shared module aims to reduce the divergence between facial images of parent and child. In order to further relieve the cross-generation difference and improve the discriminability of learned features, we also design a cross-generation difference loss term and an intra-generation discriminant loss term, which simultaneously make use of the local and holistic features, as well as the family identity information. Experimental results on several widely used kinship datasets are presented to validate the effectiveness of our proposed approach by comparing with the state-of-the-art kinship verification methods. Specially, our approach can improve the average verification accuracy at least by 3.5%, 1.5% and 0.8% on KinFaceW-I, Cornell, and TSKinFace datasets, respectively.
SPIN: Simplifying Polar Invariance for Neural networks Application to vision-based irradiance forecasting

Translational invariance induced by pooling operations is an inherent property of convolutional neural networks, which facilitates numerous computer vision tasks such as classification. Yet to leverage rotational invariant tasks, convolutional architectures require specific rotational invariant layers or extensive data augmentation to learn from diverse rotated versions of a given spatial configuration. Unwrapping the image into its polar coordinates provides a more explicit representation to train a convolutional architecture as the rotational invariance becomes translational, hence the visually distinct but otherwise equivalent rotated versions of a given scene can be learnt from a single image. We show with two common vision-based solar irradiance forecasting challenges (i.e. using ground-taken sky images or satellite images), that this preprocessing step significantly improves prediction results by standardising the scene representation, while decreasing training time by a factor of 4 compared to augmenting data with rotations. In addition, this transformation magnifies the area surrounding the centre of the rotation, leading to more accurate short-term irradiance predictions.
Process Duration Modelling and Concept Drift Detection for Business Process Mining

Customer behaviour within business processes can change over time, making it difficult for market understanding and decision making. Detecting such variations, also referred to as concept drift, can provide insight into the evolution of the business environment, offer opportunities for model refinement and provide target-oriented services to improve customer satisfaction. Concept drift in the control-flow perspective has been extensively studied but there is a research gap in detecting process duration drift. In this paper, we use gamma mixture models (GMMs) with an expectation-maximization (EM) algorithm to fit process durations and then detect variations in their histogram, density and cumulative distributions. Specifically, three metrics: the overall difference in back-to-back histograms, the Kullback-Leibler (KL) divergence and the maximum difference in cumulative distributions are used to evaluate how different the process durations are. Furthermore, three corresponding statistical tests: the multinomial test, log-likelihood ratio (LLR) test and Kolmogorov-Smirnov (KS) test are applied to determine whether, or not, the differences are statistically significant. The approach is applied to a public real-life hospital billing process where two concept drift occurrences are discovered. The main contribution of this paper is the approach aiming for detecting process duration changes.
Development Blended Web Mobile Learning Model on COVID-19 Pandemic

Education Design Research (EDR) focuses on producing a Blended Web Mobile Learning (BWML) to increase the students’ HOTs of senior high school. The BWML model in each phase is supported by using the MoLearn application (web and mobile version). The results indicate that the BWML model is proven to be valid, practical and effective in improving HOTs of senior high school students. Research implication such as: (1) The BWML model can be used as an alternative to digital-based learning, (2) The BWML model can be a reference for developing a hybrid learning model in COVID-19 pandemic, (3) The research result can be used as empirical data that web and mobile integration in learning has proven successful.
Implementation of Optimal CNN Accelerators for Mobile Devices: Algorithm, Architecture, and Memory System Co-Design

Recently, many attempts have been made to use convolutional neural network (CNN) applications on mobile devices. This paper presents the overall process of designing optimal CNN accelerators for mobile devices based on algorithm, architecture, and memory system co-design, and introduces various existing studies related to each research field.
Physical and Digital Accessibility in Museums in the New Reality

Accessibility is a multidisciplinary field and requires innovation. People with disabilities are often excluded from the cultural life, especially blind people. None of the 55 Bulgarian museums included in the study offers them adequate visualization and interaction. The research is conducted with blind and physically challenged people as participants and combines physical and online tests. The results are collected through online questionnaire
Single-Server Individually-Private Information Retrieval: A Combinatorial Approach

This paper considers the problem of single-server Individually-Private Information Retrieval (IPIR). In this problem, a user wants to retrieve D messages belonging to a dataset of K messages stored on a single server. Initially, the user knows M other messages belonging to the dataset as side information, where the identities of these M messages are unknown to the server. The goal is to minimize the total amount of information that the user must download from the server while keeping the identity of each of the D desired messages individually private, i.e., the identity of every individual message wanted by the user must be protected. The capacity of IPIR, which is defined as the supremum of all achievable download rates, was previously characterized for D = 2, M = 1. However, the capacity was left open for all other values of D, M. In this work, we present a technique for the proof of converse, based on a novel combinatorial approach. Using this technique, we establish an upper bound on the capacity of IPIR for D = 2, M = 2. For this setting, we also propose a new IPIR scheme—based on a probabilistic partitioning of the messages, that achieves the capacity upper bound. We believe that our approach can be employed for proving the converse and designing optimal schemes for the general cases of the problem.
Self-slimmed Vision Transformer

Vision transformers (ViTs) have become the popular structures and outperformed convolutional neural networks (CNNs) on various vision tasks. However, such powerful transformers bring a huge computation burden, because of the exhausting token-to-token comparison. The previous works focus on dropping insignificant tokens to reduce the computational cost of ViTs. But when the dropping ratio increases, this hard manner will inevitably discard the vital tokens, which limits its efficiency. To solve the issue, we propose a generic self-slimmed learning approach for vanilla ViTs, namely SiT. Specifically, we first design a novel Token Slimming Module (TSM), which can boost the inference efficiency of ViTs by dynamic token aggregation. As a general method of token hard dropping, our TSM softly integrates redundant tokens into fewer informative ones. It can dynamically zoom visual attention without cutting off discriminative token relations in the images, even with a high slimming ratio. Furthermore, we introduce a concise Feature Recalibration Distillation (FRD) framework, wherein we design a reverse version of TSM (RTSM) to recalibrate the unstructured token in a flexible auto-encoder manner. Due to the similar structure between teacher and student, our FRD can effectively leverage structure knowledge for better convergence. Finally, we conduct extensive experiments to evaluate our SiT. It demonstrates that our method can speed up ViTs by $$\mathbf {1.7}\times $$ with negligible accuracy drop, and even speed up ViTs by $$\mathbf {3.6}\times $$ while maintaining $$\textbf{97}\%$$ of their performance. Surprisingly, by simply arming LV-ViT with our SiT, we achieve new state-of-the-art performance on ImageNet. Code is available at https://github.com/Sense-X/SiT .
Diversified development of national vocal music teaching based on online teaching system

research-article Diversified development of national vocal music teaching based on online teaching system Share on Author: Bo Tang Hunan University of Humanities, China Hunan University of Humanities, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 269–273https://doi.org/10.1145/3482632.3482688Online:22 November 2021Publication History 0citation9DownloadsMetricsTotal Citations0Total Downloads9Last 12 Months9Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Standard (3, 5)-threshold quantum secret sharing by maximally entangled 6-qubit states

In this paper, a standard (3, 5)-threshold quantum secret sharing scheme is presented, in which any three of five participants can resume cooperatively the classical secret from the dealer, but one or two shares contain absolutely no information about the secret. Our scheme can be fulfilled by using the singular properties of maximally entangled 6-qubit states found by Borras. We analyze the scheme's security by several ways, for example, intercept-and-resend attack, entangle-and-measure attack, and so on. Compared with the other standard threshold quantum secret sharing schemes, our scheme needs neither to use d-level multipartite entangled states, nor to produce shares by classical secret splitting techniques, so it is feasible to be realized.
High-quality dense 3D point clouds with active stereo and a miniaturizable interferometric pattern projector

We have built and characterized a compact, simple and flexible 3D camera based on interferometric fringe projection and stereo reconstruction. The camera uses multi-frame active stereo as basis for 3D reconstruction, providing full-field 3D images with 3D measurement standard deviation of 0.09 mm, 12.5 Hz 3D image capture rate and 3D image resolution of 500 × 500 pixels. Interferometric projection enables a compact, low-power projector that consumes &lt; 1 W of electrical power. The key component in the projector, a movable micromirror, has undergone initial vibration, thermal vacuum cycling (TVAC) and radiation testing, with no observed component degradation. The system's low power, small size and component longevity makes it well suitable for space applications.
Real-Time Adversarial Perturbations Against Deep Reinforcement Learning Policies: Attacks and Defenses

Deep reinforcement learning (DRL) is vulnerable to adversarial perturbations. Adversaries can mislead the policies of DRL agents by perturbing the state of the environment observed by the agents. Existing attacks are feasible in principle, but face challenges in practice, either by being too slow to fool DRL policies in real time or by modifying past observations stored in the agent’s memory. We show that Universal Adversarial Perturbations (UAP), independent of the individual inputs to which they are applied, can fool DRL policies effectively and in real time. We introduce three attack variants leveraging UAP. Via an extensive evaluation using three Atari 2600 games, we show that our attacks are effective, as they fully degrade the performance of three different DRL agents (up to 100%, even when the $$l_\infty $$ bound on the perturbation is as small as 0.01). It is faster than the frame rate (60 Hz) of image capture and considerably faster than prior attacks ( $$\approx $$ 1.8 ms). Our attack technique is also efficient, incurring an online computational cost of $$\approx $$ 0.027 ms. Using two tasks involving robotic movement, we confirm that our results generalize to complex DRL tasks. Furthermore, we demonstrate that the effectiveness of known defenses diminishes against universal perturbations. We introduce an effective technique that detects all known adversarial perturbations against DRL policies, including all universal perturbations presented in this paper.
Non-Reference Image Quality Assessment Based on Super-Pixel Segmentation and Information Entropy

Because the existing Blind/Non-reference Image Spatial Quality Evaluator (BRISQUE) algorithm does not take the factor of image information into consideration when evaluating image quality. Therefore, in order to improve the stability and accuracy of the BRISQUE algorithm, a superpixel segmentation blind/non-reference image spatial quality evaluator (S-BRISQUE) based on the information entropy weighting of superpixel segmentation is proposed. First, the image is segmented according to the similarity of the image information, and the information entropy matrix of each part of the sub-image is calculated after the segmentation. Then, the entropy matrix is used to weight the average subtractive contrast normalization (MSCN) parameter matrix, and the MSCN parameter based on the image information is used as the weight. The proposed S-BRISQUE algorithm was verified on the Live benchmark database. Compared with the BRISQUE algorithm and other classic algorithms, the S-BRISQUE algorithm has a higher prediction accuracy rate, also better than some evaluation algorithms.
The Effect of Regularization on Deep Learning Methods For Detection of Malaria Infection

Malaria is an infectious disease caused by peripheral blood parasites of the genus Plasmodium. The estimated global malaria cases reached 229 million cases in 2019, of which 250,644 cases occurred in Indonesia. The large number of cases makes the early and accurate diagnosis of malaria very important because it can reduce the severity and prevent death. The most widely used method of diagnosis by far is examining a thin blood smear under a microscope and looking for infected cells. This research examines the effect of regularization applied to several Convolutional Neural Network (CNN) architectures to obtain the best accuracy of malaria parasite detection on thin blood smear images. The regularization techniques used are dropout layer, L2 regularization, and data augmentation. The results show that the use of BaselineNet without regularization achieved 94.92% accuracy. The use of regularization on ResNet-50, MicroVGGNet, BaselineNet-1 obtained 97.12%, 95.64% and 96,28% accuracy respectively.