Reviewerly/exHarmony huggingface dataset:
MGGAN: Solving Mode Collapse Using Manifold-Guided Training

Mode collapse is a critical problem in training generative adversarial networks. To alleviate mode collapse, several recent studies have introduced new objective functions, network architectures, or alternative training schemes. However, their achievement is often the result of sacrificing the image quality. In this paper, we propose a new algorithm, namely, the manifold-guided generative adversarial network (MGGAN), which leverages a guidance network on existing GAN architecture to induce the generator to learn the overall modes of a data distribution. The guidance network transforms an image into a learned manifold space, which is effective in representing the coverage of the overall modes. The characteristics of this guidance network helps penalize mode imbalance. Results of the experimental comparisons using various baseline GANs showed that MGGAN can be easily extended to existing GANs and resolve mode collapse without losing the image quality. Moreover, we extend the idea of manifold-guided GAN training to increase the original diversity of a data distribution. From the experiment, we confirmed that a GAN model guided by a joint manifold can sample data distribution with greater diversity. Results of the experimental analysis confirmed that MGGAN is an effective and efficient tool for improving the diversity of GANs.
Occluded Person Re-Identification with Single-scale Global Representations

Occluded person re-identification (ReID) aims at re-identifying occluded pedestrians from occluded or holistic images taken across multiple cameras. Current state-of-the-art (SOTA) occluded ReID models rely on some auxiliary modules, including pose estimation, feature pyramid and graph matching modules, to learn multi-scale and/or part-level features to tackle the occlusion challenges. This unfortunately leads to complex ReID models that (i) fail to generalize to challenging occlusions of diverse appearance, shape or size, and (ii) become ineffective in handling non-occluded pedestrians. However, real-world ReID applications typically have highly diverse occlusions and involve a hybrid of occluded and non-occluded pedestrians. To address these two issues, we introduce a novel ReID model that learns discriminative single-scale global-level pedestrian features by enforcing a novel exponentially sensitive yet bounded distance loss on occlusion-based augmented data. We show for the first time that learning single-scale global features without using these auxiliary modules is able to outperform the SOTA multi-scale and/or part-level feature-based models. Further, our simple model can achieve new SOTA performance in both occluded and non-occluded ReID, as shown by extensive results on three occluded and two general ReID benchmarks. Additionally, we create a large-scale occluded person ReID dataset with various occlusions in different scenes, which is significantly larger and contains more diverse occlusions and pedestrian dressings than existing occluded ReID datasets, providing a more faithful occluded ReID benchmark. The dataset is available at: https://git.io/OPReID
Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization

Multimodal abstractive summarization (MAS) models that summarize videos (vision modality) and their corresponding transcripts (text modality) are able to extract the essential information from massive multimodal data on the Internet.Recently, large-scale generative pretrained language models (GPLMs) have been shown to be effective in text generation tasks.However, existing MAS models cannot leverage GPLMs' powerful generation ability.To fill this research gap, we aim to study two research questions: 1) how to inject visual information into GPLMs without hurting their generation ability; and 2) where is the optimal place in GPLMs to inject the visual information?In this paper, we present a simple yet effective method to construct vision guided (VG) GPLMs for the MAS task using attention-based add-on layers to incorporate visual information while maintaining their original text generation ability.Results show that our best model significantly surpasses the prior state-of-the-art model by 5.7 ROUGE-1, 5.3 ROUGE-2, and 5.1 ROUGE-L scores on the How2 dataset (Sanabria et al., 2018), and our visual guidance method contributes 83.6% of the overall improvement.Furthermore, we conduct thorough ablation studies to analyze the effectiveness of various modality fusion methods and fusion locations.* * The two authors contribute equally.The code is available at:
SEGA: Semantic Guided Attention on Visual Prototype for Few-Shot Learning

Teaching machines to recognize a new category based on few training samples especially only one remains challenging owing to the incomprehensive understanding of the novel category caused by the lack of data. However, human can learn new classes quickly even given few samples since human can tell what discriminative features should be focused on about each category based on both the visual and semantic prior knowledge. To better utilize those prior knowledge, we propose the SEmantic Guided Attention (SEGA) mechanism where the semantic knowledge is used to guide the visual perception in a top-down manner about what visual features should be paid attention to when distinguishing a category from the others. As a result, the embedding of the novel class even with few samples can be more discriminative. Concretely, a feature extractor is trained to embed few images of each novel class into a visual prototype with the help of transferring visual prior knowledge from base classes. Then we learn a network that maps semantic knowledge to category-specific attention vectors which will be used to perform feature selection to enhance the visual prototypes. Extensive experiments on miniImageNet, tieredImageNet, CIFAR-FS, and CUB indicate that our semantic guided attention realizes anticipated function and outperforms state-of-the-art results.
A survey on mutual information based medical image registration algorithms

The process of aligning one image, in the coordinate system of another is called registration. Image registration is vital in the medical domain and is used for diagnosis, therapy planning, and treatment of diseases. Due to its huge applicability and importance, medical image registration has emerged to be a separate domain of research. Beginning from the invasive landmark based registration, innumerable algorithms have been proposed to register two images of the human physiology. However, a breakthrough in the literature occurred when an information theory based measure, mutual information was used to register two images. Since then a large number of new algorithms have developed, which use mutual information for fully automatic registration of medical images. This paper is a survey of these algorithms. Beginning from its development, it discusses about some of the major works done on mutual information based image registration. Some comparative studies with other algorithms have also been discussed. The paper ends with a discussion of the developments in medical image registration algorithms post mutual information, which primarily include deep neural network (DNN) based algorithms.
Principal component analysis and K-means clustering as tools during exploration for Zn skarn deposits and industrial carbonates, Sala area, Sweden

This contribution presents an application of principal component analysis (PCA) and K-means clustering as tools for data dimension reduction and grouping of multivariate, whole-rock lithogeochemical data. The study dataset consists of 64 geochemical variables and measurements of spectrophotometric brightness determined from 181 dolomite marble samples, collected at various distance from two contrasting types of mineral deposits, 1) stratabound, dolomite marble- and skarn-hosted Zn-Pb-Ag sulphide deposits and 2) industrial dolomite deposits. Clustering and PCA outputs are assessed based on spatial distribution relative to known mineral deposits and interpretability using geological domain knowledge, to test if the methods can provide a non-biased classification of dolomite samples which is useful for exploration vectoring. The PCA illustrate that three principle components derived from centered log-ratio transformed data can account for 79.69% of the dataset variance. K-means clustering provide unsupervised division of samples into different groups reflecting relative contents of detrital (siliciclastic-volcaniclastic), biogenic and hydrothermal components in the marble protoliths. Spatial analysis of principal components and K-means clusters reveal systematic distribution patterns relative to known deposits, thus providing an exploration guide. The samples most prospective for Zn-Pb-Ag deposits are divided into groups of ‘halo dolomite’ exhibiting elevated Fe and Mn, and an ‘ore dolomite’ also showing elevated Zn, Pb, Ag, Sb, Hg. This can be reconciled with magnetite and Mn-bearing Mg-silicates and carbonates in hydrothermal alteration haloes, and proximal enrichment in hydrothermal sulphides (galena, pyrrhotite, pyrite, sphalerite). Samples in these groups returned low spectrophotometric brightness, resulting from sulphides and Fe oxides grinding to dark powders during sample preparation, significantly lowering the brightness of powdered dolomite marble, even when occurring in low concentrations. Conversely, a ‘clean dolomite’ group is characterized by low contents of the elements above, high contents of Ca, Mg, Sr and total carbon, low magnetic susceptibility and high spectrophotometric brightness, and spatially coincide with known industrial dolomite deposits. An additional group of ‘detrital-rich dolomite’ is distinct from the other groups in an elevated content of high field strength elements and Al, and intermediate spectrophotometric brightness. This variety represent samples containing a higher content of co-settled volcaniclastic-siliciclastic material in the marble precursor. Assessment of the clustered data in relation to magnetic susceptibility measurements from the same samples show that Halo and Ore dolomite can be differentiated from other dolomite types by geomagnetic methods, hence providing a proxy for their indirect detection during geophysical surveys.
Sustainability and Digital Teaching Competence in Higher Education

This article examines the research that explores the relationship between sustainability and digital teaching competence in the university environment, through a qualitative systematic review, which covers 2011 to 2021. It is intended to identify how sustainability is applied in higher education through teaching experiences linked to the use of ICT, where the digital teaching competence is specified and put into practice. In other words, it is about responding to the following questions: What digital skills are being applied to develop educational sustainability in higher education? In which aspects of educational and pedagogical sustainability are they projected? As a work methodology, the PRISMA protocol is applied as the technique of systematic review, using the Scopus and WOS databases as sources of information. Subsequently, a qualitative analysis of the selected articles is carried out using the ATLAS.ti scientific software, using the DigCompEdu model as the basis for the analysis of the information. The results shed light on the panorama of research on digital competence and sustainability and the evolution of scientific production over ten years, as well as the methodology applied in these studies. The DigCompEdu model is found to be useful for registering the modalities of teaching competencies put into practice, manifesting a primacy of pedagogical digital competences over those of professional development and student empowerment. Sustainability development areas are also identified, linked to teaching digital competence, such as inclusion, educational quality or lifelong learning.
One Glitch to Rule Them All: Fault Injection Attacks Against AMD's Secure Encrypted Virtualization

AMD Secure Encrypted Virtualization (SEV) offers protection mechanisms for virtual machines in untrusted environments through memory and register encryption. To separate security-sensitive operations from software executing on the main x86 cores, SEV leverages the AMD Secure Processor (AMD-SP). This paper introduces a new approach to attack SEV-protected virtual machines (VMs) by targeting the AMD-SP. We present a voltage glitching attack that allows an attacker to execute custom payloads on the AMD-SPs of all microarchitectures that support SEV currently on the market (Zen 1, Zen 2, and Zen 3). The presented methods allow us to deploy a custom SEV firmware on the AMD-SP, which enables an adversary to decrypt a VM's memory. Furthermore, using our approach, we can extract endorsement keys of SEV-enabled CPUs, which allows us to fake attestation reports or to pose as a valid target for VM migration without requiring physical access to the target host. Moreover, we reverse-engineered the Versioned Chip Endorsement Key (VCEK) mechanism introduced with SEV Secure Nested Paging (SEV-SNP). The VCEK binds the endorsement keys to the firmware version of TCB components relevant for SEV. Building on the ability to extract the endorsement keys, we show how to derive valid VCEKs for arbitrary firmware versions. With our findings, we prove that SEV cannot adequately protect confidential data in cloud environments from insider attackers, such as rogue administrators, on currently available CPUs.
Feature super-resolution based Facial Expression Recognition for multi-scale low-resolution images

Facial Expression Recognition (FER) for various low-resolution images is an important task and need in applications of analyzing crowd scenes (station, classroom, etc.). Due to the discriminative feature loss caused by reduced resolution, classifying various low-resolution facial images into the right category is still a challenging task. In this work, we proposed a novel generative adversarial network-based feature level super-resolution method for robust facial expression recognition (FSR-FER), which can reduce the chance of privacy leaking without restoring high-resolution facial images. In particular, a pre-trained FER model was employed as a feature extractor, and a generator network G and a discriminator network D are trained with features extracted from low-resolution and corresponding high-resolution images. Generator network G tries to transform features of low-resolution images to more discriminative ones by making them closer to the ones of corresponding high-resolution images. For better classification performance, we also proposed an effective classification-aware loss reweighting strategy based on the classification probability calculated by a fixed FER model to make our model focus more on samples that are prone to misclassification. Experimental results on the Real-World Affective Faces (RAF) Database and Static Facial Expressions in the Wild (SFEW) 2.0 dataset demonstrate that our method achieves satisfying results on various down-sample factors with a single model and has better performance on low-resolution images compared with methods using image super-resolution and expression recognition separately.
An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction

Machine learning (ML) models are commonly used in solar modeling due to their high predictive accuracy. However, the predictions of these models are difficult to explain and trust. This paper aims to demonstrate the utility of two interpretation techniques to explain and improve the predictions of ML models. We compared first the predictive performance of Light Gradient Boosting (LightGBM) with three benchmark models, including multilayer perceptron (MLP), multiple linear regression (MLR), and support-vector regression (SVR), for estimating the global solar radiation (H) in the city of Fez, Morocco. Then, the predictions of the most accurate model were explained by two model-agnostic explanation techniques: permutation feature importance (PFI) and Shapley additive explanations (SHAP). The results indicated that LightGBM (R2 = 0.9377, RMSE = 0.4827 kWh/m2, MAE = 0.3614 kWh/m2) provides similar predictive accuracy as SVR, and outperformed MLP and MLR in the testing stage. Both PFI and SHAP methods showed that extraterrestrial solar radiation (H0) and sunshine duration fraction (SF) are the two most important parameters that affect H estimation. Moreover, the SHAP method established how each feature influences the LightGBM estimations. The predictive accuracy of the LightGBM model was further improved slightly after re-examination of features, where the model combining H0, SF, and RH was better than the model with all features.
A learning automata-based hybrid MPA and JS algorithm for numerical optimization problems and its application on data clustering

Nature-inspired meta-heuristic algorithms possess various actions inspired by natural phenomena, animal behaviors, chemistry or physics laws etc. The actions are utilized to relocate agents in the problem space and update solutions. Selecting the best action to update a particle is a challenging responsibility in solving real-world NP-Hard problems. The selection process in meta-heuristic algorithms is predominantly random or quasi-random, which is not suitable enough. The Learning-Automata (LA) is a mechanism to make the selection process more dynamic. The LA chooses the most optimal action regarding history and status and balances exploration and exploitation intelligently. In the current paper, a neoteric LA-based hybrid optimization algorithm is presented for global optimization problems. In the proposed algorithm, the artificial Jellyfish search algorithm (JS) and Marine Predator Algorithm (MPA) are rectified to reduce their computational complexity while retaining their strengths. Furthermore, the LA’s probability vector is augmented to make it more efficient. Afterward, the JS and MPA algorithm are redeveloped using the proposed LA mechanism. The proposed LA-based hybrid is tested on thirty-eight low- and high-dimensional benchmark functions compared with state-of-the-art algorithms, statistically and visually. Moreover, proficiency of the proposed LA-based hybrid algorithm is investigated on the data clustering problem. The proposed algorithm is applied to ten datasets, and the results are compared with competitor algorithms using various metrics. The experimental results exposed the preponderance of the proposed LA-based hybrid algorithm.
Multi-User Virtual Reality for Remote Collaboration in Construction Projects: A Case Study with High-Rise Elevator Machine Room Planning

Virtual Reality (VR) is considered among the major technologies to address the inefficient collaboration issue caused by the predominant use of 2D drawings in the construction industry. However, there is still a knowledge gap between researchers and practitioners about the actual benefits of VR in the business context. This paper presents the benefits of VR usage in four real-life high-rise elevator projects from the user and business perspectives. Four VR environments of actual machine rooms for planning were created and tested in a multi-user setting. Overall, users find VR more intuitive than conventional tools to enhance planning accuracy and collaboration. The results also show that VR brings significant economic savings and gains for business in the industry. Future study should investigate the real cost-benefit ratio of VR and streamline its technical implementation within construction projects. The research contributes to the current body of knowledge by providing real-life economic benefits and directions to address the research gap in both academia and industry to promote the wide adoption of VR.
Co-attention network with label embedding for text classification

Most existing methods for text classification focus on extracting a highly discriminative text representation, which, however, is typically computationally inefficient. To alleviate this issue, label embedding frameworks are proposed to adopt the label-to-text attention that directly uses label information to construct the text representation for more efficient text classification. Although these label embedding methods have achieved promising results, there is still much space for exploring how to use the label information more effectively. In this paper, we seek to exploit the label information by further constructing the text-attended label representation with text-to-label attention. To this end, we propose a Co-attention Network with Label Embedding (CNLE) that jointly encodes the text and labels into their mutually attended representations. In this way, the model is able to attend to the relevant parts of both. Experiments show that our approach achieves competitive results compared with previous state-of-the-art methods on 7 multi-class classification benchmarks and 2 multi-label classification benchmarks.
Neural Architecture Search and Hardware Accelerator Co-Search: A Survey

Deep neural networks (DNN) are now dominating in the most challenging applications of machine learning. As DNNs can have complex architectures with millions of trainable parameters (the so-called weights), their design and training are difficult even for highly qualified experts. In order to reduce human effort, neural architecture search (NAS) methods have been developed to automate the entire design process. The NAS methods typically combine searching in the space of candidate architectures and optimizing (learning) the weights using a gradient method. In this paper, we survey the key elements of NAS methods that – to various extents – consider hardware implementation of the resulting DNNs. We classified these methods into three major classes: single-objective NAS (no hardware is considered), hardware-aware NAS (DNN is optimized for a particular hardware platform), and NAS with hardware co-optimization (hardware is directly co-optimized with DNN as a part of NAS). Compared to previous surveys, we emphasize the multi-objective design approach that must be adopted in NAS and focus on co-design algorithms developed for concurrent optimization of DNN architectures and hardware platforms. As most research in this area deals with NAS for image classification using convolutional neural networks, we follow this trajectory in our paper. After reading the paper, the reader should understand why and how NAS and hardware co-optimization are currently used to build cutting-edge implementations of DNNs.
Fixed-angle conjectures for the quantum approximate optimization algorithm on regular MaxCut graphs

The quantum approximate optimization algorithm (QAOA) is a near-term combinatorial optimization algorithm suitable for noisy quantum devices. However, little is known about performance guarantees for $p&gt;2$. A recent work computing MaxCut performance guarantees for 3-regular graphs conjectures that any $d$-regular graph evaluated at particular fixed angles has an approximation ratio greater than some worst-case guarantee. In this work, we provide numerical evidence for this fixed angle conjecture for $p&lt;12$. We compute and provide these angles via numerical optimization and tensor networks. These fixed angles serve for an optimization-free version of QAOA and have universally good performance on any 3-regular graph. Heuristic evidence is presented for the fixed angle conjecture on graph ensembles, which suggests that these fixed angles are ``close'' to global optimum. Under the fixed angle conjecture, QAOA has a larger performance guarantee than the Goemans Williamson algorithm on 3-regular graphs for $p\ensuremath{\ge}11$.