Reviewerly/exHarmony huggingface dataset:
Deep Feature Compression Using Spatio-Temporal Arrangement Toward Collaborative Intelligent World

Collaborative Intelligence is a new paradigm that splits a deep neural network (DNN) into an edge and cloud for deploying a DNN-based image recognition application. In this paradigm, deep features, which are the outputs of the edge DNN, are compressed and transmitted to the cloud DNN. Because the deep features have a number of responses that are similar to each other, for efficient compression, previous methods spatially arrange and compress the deep features as an image to utilize the similarity as a spatial correlation. However, if the deep features are arranged in not only spatial but also temporal directions like those in a video, it may be possible to compress them more efficiently by increasing a temporal correlation. To explore this possibility, we propose a “spatio-temporal arrangement”. This method spatially arranges the deep features as images and temporally arranges them as a video with a novel ordering search algorithm. Our method effectively increases the spatial and temporal correlations hidden in the deep features and achieves high compression efficiency compared with the previous methods. Experimental results demonstrate the compression efficiency of our method is better than that of the previous methods (1.50% to 4.98% on BD-Rate evaluation in a lossy setting). Our analysis shows that our method effectively increases the correlation when the input is an image with rich edges and textures.
Research on the Problems and Countermeasures of Higher Education Management Informatization Development in the Computer Internet Era

Abstract The construction of local high-level universities is an important part of the comprehensive reform of higher education in China. With the coming of the computer “Internet +” era, major strategic tasks such as cloud computing, big data and new generation of artificial intelligence have been gradually laid out, which will further achieve the goal of educational modernization and the development of a powerful educational country. Promoting the development of education informatization in the Internet era has become the top priority in the development of national education. Based on the background of high-level universities, this paper first analyses the necessity of the educational informatization development. Then, this paper puts forward some problems. Finally, some suggestions are put forward [1].
Estimation of Convex Polytopes for Automatic Discovery of Charge State Transitions in Quantum Dot Arrays

In spin based quantum dot arrays, material or fabrication imprecisions affect the behaviour of the device, which must be taken into account when controlling it. This requires measuring the shape of specific convex polytopes. In this work, we present an algorithm that automatically discovers count, shape and size of the facets of a convex polytope from measurements. Results on simulated devices as well as a real 2x2 spin qubit array show that we can reliably find the facets of the convex polytopes, including small facets with sizes on the order of the measurement precision.
Density Aware 3D Object Single Stage Detector

In 3D object detection, a lot of works are focused on the characteristics of disorder and sparseness of point cloud. However, few of them directly explore the nonuniformity of point cloud which limits the detection precision. To deal with the problem, a method called Density Aware 3D Object Single Stage Detector (DA-3DSSD) is proposed in this paper. A method of defining point density of point cloud and the network processing strategies on the basis of the calculated density are proposed in DA-3DSSD. These strategies include a Density-aware Points Sampling (DPS) strategy and a Center Density Attention (CDA) module. Specifically, DPS adds a density aware factor in farthest points sampling (FPS), which improve the sampling process sensibility to density. DPS can reserve more internal points for sparse objects to gather more context information in the feature extraction process. CDA module models the relationships between center points densities and features, which can stress object features and suppress non-object features. CDA module strengthens the classification ability of the network. In the experiments, the proposed DA-3DSSD is evaluated on KITTI benchmark. Compared with the baseline method, the average precision has a significant improvement which verifies the effectiveness of our methods.
Spatially and color consistent environment lighting estimation using deep neural networks for mixed reality

The representation of consistent mixed reality (XR) environments requires adequate real and virtual illumination composition in real-time. Estimating the lighting of a real scenario is still a challenge. Due to the ill-posed nature of the problem, classical inverse-rendering techniques tackle the problem for simple lighting setups. However, those assumptions do not satisfy the current state-of-art in computer graphics and XR applications. While many recent works solve the problem using machine learning techniques to estimate the environment light and scene's materials, most of them are limited to geometry or previous knowledge. This paper presents a CNN-based model to estimate complex lighting for mixed reality environments with no previous information about the scene. We model the environment illumination using a set of spherical harmonics (SH) environment lighting, capable of efficiently represent area lighting. We propose a new CNN architecture that inputs an RGB image and recognizes, in real-time, the environment lighting. Unlike previous CNN-based lighting estimation methods, we propose using a highly optimized deep neural network architecture, with a reduced number of parameters, that can learn high complex lighting scenarios from real-world high-dynamic-range (HDR) environment images. We show in the experiments that the CNN architecture can predict the environment lighting with an average mean squared error (MSE) of 7.85× 10−4 when comparing SH lighting coefficients. We validate our model in a variety of mixed reality scenarios. Furthermore, we present qualitative results comparing relights of real-world scenes.
Balanced distortion and perception in single-image super-resolution based on optimal transport in wavelet domain

Single image super-resolution (SISR) is a classic ill-posed problem in computer vision. In recent years, deep-learning-based (DL-based) models have achieved promising results with the SISR problem. However, most existing methods suffer from an intrinsic trade-off between distortion and perceptual quality. To satisfy the requirements in different real-world situations, the balance of distortion and visual quality for image super-resolution is a critical issue. In DL-based models, the uses of hybrid loss (i.e., the combination of the distortion loss and the perceptual loss) and network interpolation are two common approaches to balancing the distortion and perceptual quality of super-resolved images. However, these two kinds of methods lack flexibility and hold strict constraints on network architectures. In this paper, we propose an image-fusion interpolation method for image super-resolution, which can balance the distortion and visual quality of super-resolved images, based on the optimal transport theory in the wavelet domain. The advantage of our proposed method is that it can be applied to any pretrained DL-based model, without any requirement from the network architecture and parameters. In addition, our proposed method is parameter-free and can run fast without using a GPU. Compared with existing state-of-the-art SISR methods, experiment results show that our proposed method can achieve a better balance between the distortion and visual quality in super-resolved images.
Goal-Driven Visual Question Generation from Radiology Images

Visual Question Generation (VQG) from images is a rising research topic in both fields of natural language processing and computer vision. Although there are some recent efforts towards generating questions from images in the open domain, the VQG task in the medical domain has not been well-studied so far due to the lack of labeled data. In this paper, we introduce a goal-driven VQG approach for radiology images called VQGRaD that generates questions targeting specific image aspects such as modality and abnormality. In particular, we study generating natural language questions based on the visual content of the image and on additional information such as the image caption and the question category. VQGRaD encodes the dense vectors of different inputs into two latent spaces, which allows generating, for a specific question category, relevant questions about the images, with or without their captions. We also explore the impact of domain knowledge incorporation (e.g., medical entities and semantic types) and data augmentation techniques on visual question generation in the medical domain. Experiments performed on the VQA-RAD dataset of clinical visual questions showed that VQGRaD achieves 61.86% BLEU score and outperforms strong baselines. We also performed a blinded human evaluation of the grammaticality, fluency, and relevance of the generated questions. The human evaluation demonstrated the better quality of VQGRaD outputs and showed that incorporating medical entities improves the quality of the generated questions. Using the test data and evaluation process of the ImageCLEF 2020 VQA-Med challenge, we found that relying on the proposed data augmentation technique to generate new training samples by applying different kinds of transformations, can mitigate the lack of data, avoid overfitting, and bring a substantial improvement in medical VQG.
An Evaluation of a Modified Haar-Like Features Based Classifier Method for Face Mask Detection in The COVID-19 Spread Prevention

The COVID-19 pandemic is becoming the cause of the world health crisis and according to the World Health Organization (WHO) one of the effective methods to prevent Covid-19 transmission is to wear a face mask in public spaces. However, accurate and lightweight face mask detection methods are still being evaluated. This paper proposes a face mask detection model using the Haar Cascade method to detect faces that have been modified and trained to detect face mask features on human faces. The dataset to be used in the form of faces with face masks has been collected through several datasets on the Internet. To evaluate the model created, tests were carried out on several scenarios of different lighting conditions to see the effect on several metrics, namely accuracy and average delay. The test results show that the lighting conditions affect the model created and in sufficient lighting conditions, the trained model has better performance than in other conditions, in terms of accuracy and average delay, namely 96.8% and 43 ms, respectively.
Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework

Surrogate task based methods have recently shown great promise for unsupervised image anomaly detection. However, there is no guarantee that the surrogate tasks share the consistent optimization direction with anomaly detection. In this paper, we return to a direct objective function for anomaly detection with information theory, which maximizes the distance between normal and anomalous data in terms of the joint distribution of images and their representation. To make this objective function directly optimizable under the unsupervised setting, we manage to find its lower bound which weights the trade-off between mutual information and entropy, which leads to a novel information theoretic framework for unsupervised image anomaly detection. Extensive experiments on several benchmark data sets have shown that the proposed framework significantly outperforms several state-of-the-arts.
Service-Oriented Justification of Recommender System Suggestions

In the selection of products or services, overviewing the list of options to identify the most promising ones is key to decision-making. However, current models for the justification of recommender systems results poorly support this task because, as they exclusively focus on item properties, they generate detailed justifications that are lengthy to skim. Moreover, they overlook the existence of a complex item fruition process which can impact customer satisfaction as well. For instance, consumer feedback shows that relevant factors in home booking include both the properties of apartments, and previous customers’ perceptions of the interaction with the personnel who manages the homes. To address this issue, we propose a visual model that exploits an explicit representation of the service underlying item fruition to generate a high-level, holistic summary of previous consumers’ opinions about the suggested items. From this overview, the user can identify the relevant items and retrieve detailed information about them, in a selective way, thus reducing information load. Our model is instantiated on the Airbnb experiences domain and uses the Service Blueprints to identify evaluation dimensions for the incremental presentation of data about items. A preliminary user study has shown that our model supports user awareness about items by enabling people to quickly filter out the unsuitable recommendations, so that they can analyze in detail the most relevant options.
Feasibility Study of an Earthquake Early Warning System in Eastern Central Italy

An earthquake early warning system (EEWS) is a monitoring infrastructure that allows alerting strategic points (targets) before the arrival of strong shaking waves during an earthquake. In a region like Central Italy, struck by recent and historical destructive earthquakes, the assessment of implementation of an EEWS is a significant challenge due to the proximity of seismic sources to many potential targets, such as historical towns, industrial plants, and hospitals. In order to understand the feasibility of an EEWS in such an area, we developed an original method of event declaration simulation (EDS), a tool for assessing the effectiveness of an EEWS for existing seismic networks, improving them with new stations, and designing new networks for EEW applications. Values of the time first alert (TFA), blind zone radius (BZ), and lead time (LT) have been estimated with respect to selected targets for different network configurations in the study region. Starting from virtual sources homogeneously arranged on regular mesh grids, the alert response was evaluated for actual and improved seismic networks operating in the area, taking into account the effects of the transmission and acquisition systems. In the procedure, the arrival times of the P wave picks, the association binder, the transmission latencies, and the computation times were used to simulate the configuration of PRESTo EEWS, simulating both real-time and playback elaborations of real earthquakes. The NLLOC software was used to estimate P and S arrival times, with a local velocity model also implemented in the PRESTo EEWS. Our results show that, although Italy’s main seismic sources are located close to urban areas, the lead times calculated with the EDS procedure, applied to actual and to improved seismic networks, encourage the implementation of EEWS in the study area. Considering actual delays due to data transmission and computation time, lead times of 5–10 s were obtained simulating real historical events striking some important targets of the region. We conclude that EEWSs are useful tools that can contribute to protecting people from the harmful effects of earthquakes in Italy.
Multi-Scale Feature Guided Low-Light Image Enhancement

Low-light image enhancement aims at enlarging the intensity of image pixels to better match human perception and to improve the performance of subsequent vision tasks. While it is relatively easy to enlighten a globally low-light image, the lighting condition of realistic scenes is usually non-uniform and complex, e.g., some images may contain both bright and extremely dark regions, with or without rich features and information. Existing methods often generate abnormal light-enhancement results with over-exposure artifacts without proper guidance. To tackle this challenge, we propose a multi-scale feature guided attention mechanism in the deep generator, which can effectively perform a spatially-varying light enhancement. The attention map is fused by both the gray map and extracted feature map of the input image, to focus more on those dark and informative regions. Our baseline is an unsupervised generative adversarial network, which can be trained without any low/normal light image pair. Experimental results demonstrate the superiority in visual quality and performance of subsequent object detection over state-of-the-art alternatives.
Human Pose and Shape Estimation From Single Polarization Images

This paper focuses on a new problem of estimating human pose and shape from single polarization images. Polarization camera is known to be able to capture the polarization of reflected lights that preserves rich geometric cues of an object surface. Inspired by the recent applications in surface normal reconstruction from polarization images, in this paper, we attempt to estimate human pose and shape from single polarization images by leveraging the polarization-induced geometric cues. A dedicated two-stage pipeline is proposed: given a single polarization image, stage one (Polar2Normal) focuses on the fine detailed human body surface normal estimation; stage two (Polar2Shape) then reconstructs clothed human shape from the polarization image and the estimated surface normal. To empirically validate our approach, a dedicated dataset (PHSPD) is constructed, consisting of over 500 K frames with accurate pose and parametric shape annotations. Empirical evaluations on this real-world dataset as well as a synthetic dataset, SURREAL, demonstrate the effectiveness of our approach. It suggests polarization camera as a promising alternative to the more conventional RGB camera for human pose and shape estimation.
Feature Matching Method Based on SURF and Fast Library for Approximate Nearest Neighbor Search

Aiming at dimensional feature vector matching problem of low accuracy, a kind of image matching algorithm is based on SURF and fast library for approximate nearest neighbor search. Fast-Hessian was used to detect the feature points, and the SURF feature description vector proposed was generated; through fast library for approximate nearest neighbor search algorithm, pre-matching point was got. With the introduction of Random Sample Consensus (RANSAC) algorithm points false-matching, based on the SIFT algorithm, the result of SURF algorithm and optimization algorithm was proposed and mismatching points were eliminated after the experiment simulation. Experimental results show that at the same time the matching algorithm improve matching accuracy rate, and the algorithm of the real-time performance is also improved.
Differential evolution and particle swarm optimization against COVID-19

COVID-19 disease, which highly affected global life in 2020, led to a rapid scientific response. Versatile optimization methods found their application in scientific studies related to COVID-19 pandemic. Differential Evolution (DE) and Particle Swarm Optimization (PSO) are two metaheuristics that for over two decades have been widely researched and used in various fields of science. In this paper a survey of DE and PSO applications for problems related with COVID-19 pandemic that were rapidly published in 2020 is presented from two different points of view: 1. practitioners seeking the appropriate method to solve particular problem, 2. experts in metaheuristics that are interested in methodological details, inter comparisons between different methods, and the ways for improvement. The effectiveness and popularity of DE and PSO is analyzed in the context of other metaheuristics used against COVID-19. It is found that in COVID-19 related studies: 1. DE and PSO are most frequently used for calibration of epidemiological models and image-based classification of patients or symptoms, but applications are versatile, even interconnecting the pandemic and humanities; 2. reporting on DE or PSO methodological details is often scarce, and the choices made are not necessarily appropriate for the particular algorithm or problem; 3. mainly the basic variants of DE and PSO that were proposed in the late XX century are applied, and research performed in recent two decades is rather ignored; 4. the number of citations and the availability of codes in various programming languages seems to be the main factors for choosing metaheuristics that are finally used.
InfoVAEGAN: Learning Joint Interpretable Representations by Information Maximization and Maximum Likelihood

Learning disentangled and interpretable representations is an important step towards accomplishing comprehensive data representations on the manifold. In this paper, we propose a novel representation learning algorithm which combines the inference abilities of Variational Autoencoders (VAE) with the generalization capability of Generative Adversarial Networks (GAN). The proposed model, called InfoVAEGAN, consists of three networks: Encoder, Generator and Discriminator. InfoVAEGAN aims to jointly learn discrete and continuous interpretable representations in an unsupervised manner by using two different data-free log-likelihood functions onto the variables sampled from the generator’s distribution. We propose a two-stage algorithm for optimizing the inference network separately from the generator training. Moreover, we enforce the learning of interpretable representations through the maximization of the mutual information between the existing latent variables and those created through generative and inference processes.
Between Necessity and Legal Responsibility: The Development of EEWS in Italy and its International Framework

Earthquake Early Warning Systems (EEWSs) represent a technical-scientific challenge aimed at improving the chance of the population exposed to the earthquake shaking of surviving or being less affected. The ability of an EEWS to affect the risk and, in particular, vulnerability and exposure, may determine serious legal responsibilities for people involved in the system, as scientists and experts. The main question concerns, in fact, the relationship between EEWSs and the predictability and avoidability of earthquake effects-i.e., the ground shaking affecting citizens and infrastructures - and the possibility for people to adopt self-protective behavior and/or for industrial infrastructures to be secured. In Italy, natural disasters, such as the 2009 L’Aquila earthquake, teach us that the relationship between science and law is really difficult. So, before EEW’s become operational in Italy, it is necessary to: 1) examine the legislative and technical solutions adopted by some of the international legal systems in countries where this service is offered to citizens; 2) reconstruct the international and European regulatory framework that promotes the introduction of EW systems as life-saving tools for the protection of the right to life and understand whether and how these regulatory texts can impose an obligation on the Italian legal system to develop EEWS; 3) understand what responsibilities could be ascribed to the scientists and technicians responsible for managing EEWS in Italy, analyzing the different impact of vulnerability and exposure on the predictability and avoidability of the harmful event; 4) reflect on the lessons that our legal system will have to learn from other Countries when implementing EEW systems. In order to find appropriate solutions, it is essential to reflect on the opportunity to provide shared and well-structured protocols and creating detailed disclaimers clearly defining the limits of the service. A central role must be recognized to education, because people should not only expect to receive a correct alarm but must be able to understand the uncertainties involved in rapid estimates, be prepared to face the risk, and react in the right way.