Reviewerly/exHarmony huggingface dataset:
Dual-Attention GAN for Large-Pose Face Frontalization

Face frontalization provides an effective and efficient way for face data augmentation and further improves the face recognition performance in extreme pose scenario. Despite recent advances in deep learning-based face synthesis approaches, this problem is still challenging due to significant pose and illumination discrepancy. In this paper, we present a novel Dual-Attention Generative Adversarial Network (DA-GAN) for photo-realistic face frontalization by capturing both contextual dependencies and local consistency during GAN training. Specifically, a self-attention-based generator is introduced to integrate local features with their long-range dependencies yielding better feature representations, and hence generate faces that preserves identities better, especially for larger pose angles. Moreover, a novel face-attention-based discriminator is applied to emphasize local features of face regions, and hence reinforce the realism of synthetic frontal faces. Guided by semantic segmentation, four independent discriminators are used to distinguish between different aspects of a face (i.e., skin, keypoints, hairline, and frontalized face). By introducing these two complementary attention mechanisms in generator and discriminator separately, we can learn a richer feature representation and generate identity preserving inference of frontal views with much finer details (i.e., more accurate facial appearance and textures) comparing to the state-of-the-art. Quantitative and qualitative experimental results demonstrate the effectiveness and efficiency of our DA-GAN approach.
Robust Reversible Watermarking in Encrypted Image With Secure Multi-Party Based on Lightweight Cryptography

With the rapid development of network media, increasing research on reversible watermarking has focused on improving its robustness to resisting attacks during digital media transmission. There are some other reversible watermarking schemes that work in the encrypted domain for preserving the privacy of the cover image. The robustness of the watermarking and the privacy preserving of the cover image have become the key factors of reversible watermarking. However, there are few robust reversible watermarking schemes in the encrypted domain that could resist common attacks (such as JPEG compression, noise addition) and preserve privacy at the same time. In addition, the embedding capacity of a robust watermark and the efficiency of the encryption method must be considered. Recently, cloud computing technology has led to the rapid growth of network media, and many multimedia properties are owned by multiple parties, such as a film’s producer and multiple distributors. Multi-party watermarking has become an important demand for network media to protect all parties’ rights. In this paper, a Robust Reversible Watermarking scheme in Encrypted Image with Secure Multi-party (RRWEI-SM) based on lightweight cryptography is first proposed. Additive secret sharing and block-level scrambling are developed to generate the encrypted image. Then, the robust reversible watermarking based on significant bit Prediction Error Expansion (PEE) is performed by Secure Multi-party Computation (SMC). For applications with high robustness, a Modified RRWEI-SM is proposed by exploiting two-stage architecture. Furthermore, both the RRWEI-SM scheme and Modified RRWEI-SM scheme are separable and can be applied to multiparty copyright protection. The experimental results and theoretical analysis demonstrate here that the RRWEI-SM and the Modified RRWEI-SM are secure, robust and effective.
SAMM Long Videos: A Spontaneous Facial Micro- and Macro-Expressions Dataset

With the growth of popularity of facial micro-expressions in recent years, the demand for long videos with micro- and macro-expressions remains high. Extended from SAMM, a micro-expressions dataset released in 2016, this paper presents SAMM Long Videos dataset for spontaneous micro- and macro-expressions recognition and spotting. SAMM Long Videos dataset consists of 147 long videos with 343 macro-expressions and 159 micro-expressions. The dataset is FACS-coded with detailed Action Units (AUs). We compare our dataset with Chinese Academy of Sciences Macro-Expressions and Micro-Expressions (CAS(ME) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) dataset, which is the only available fully annotated dataset with micro- and macro-expressions. Furthermore, we preprocess the long videos using OpenFace, which includes face alignment and detection of facial AUs. We conduct facial expression spotting using this dataset and compare it with the baseline of MEGC III. Our spotting method outperformed the baseline result with F1-score of 0.3299.
Color correction and adaptive contrast enhancement for underwater image enhancement

To address the color cast and low contrast of underwater images, we propose a color correction and adaptive contrast enhancement algorithm for underwater image enhancement. In our work, we first design the dedicated fractions to compensate the lower color channels, which are calculated by considering the ratio of the differences between the upper and lower color channels to the lower color channels. Subsequently, an adaptive contrast enhancement algorithm is applied to each color channel is proposed to produce the background-stretched and foreground-stretched images, the combination of these two images significantly improves the contrast of the output image. Finally, a unsharp masking technique is applied to sharpen the overall image. Experiments on the standard underwater image enhancement benchmark dataset have proven that our algorithm can output high-quality images, while the performer of our algorithm is at least 5% better than the second-best in the average of all metric scores.
Integrating Sensor Ontologies with Global and Local Alignment Extractions

In order to enhance the communication between sensor networks in the Internet of things (IoT), it is indispensable to establish the semantic connections between sensor ontologies in this field. For this purpose, this paper proposes an up-and-coming sensor ontology integrating technique, which uses debate mechanism (DM) to extract the sensor ontology alignment from various alignments determined by different matchers. In particular, we use the correctness factor of each matcher to determine a correspondence’s global factor, and utilize the support strength and disprove strength in the debating process to calculate its local factor. Through comprehensively considering these two factors, the judgment factor of an entity mapping can be obtained, which is further applied in extracting the final sensor ontology alignment. This work makes use of the bibliographic track provided by the Ontology Alignment Evaluation Initiative (OAEI) and five real sensor ontologies in the experiment to assess the performance of our method. The comparing results with the most advanced ontology matching techniques show the robustness and effectiveness of our approach.
Real-Time Face Mask Detector Using YOLOv3 Algorithm and Haar Cascade Classifier

During the pandemic of Covid-19, wearing face mask in some factories, departments, or working offices is required. This paper presents a real-time face mask detector which can alarm when detecting a person without wearing a face mask. Moreover, the system can recognize the person who wears a face mask incorrectly, or wear other things except a face mask. The proposed algorithm for face mask detection in this system utilizes Haar cascade classifier to detect the face and YOLOv3 algorithm to detect the mask. The whole system has been built and demonstrated in a practical application for checking people wearing face mask at the office entrance. The experiment result shows that the accuracy of the system can achieve up to 90.1%.
Reviving Iterative Training with Mask Guidance for Interactive Segmentation

Recent works on click-based interactive segmentation have demonstrated state-of-the-art results by using various inference-time optimization schemes. These methods are significantly more computationally expensive than feedforward approaches, as they run backward gradient passes during inference. Moreover, backward passes are not supported in popular mobile frameworks, which complicates the deployment of such methods on embedded devices. In this paper, we study design choices for interactive segmentation and discover that state-of-the-art results can be obtained without any additional optimization schemes. We propose a simple feedforward model for click-based interactive segmentation that employs the segmentation masks from previous steps. It allows not only segmenting an entirely new object but also correcting an existing mask. We analyze the performance of models trained on different datasets and observe that the choice of a training dataset has a large impact on the quality of interactive segmentation. We find that the models trained on a combination of COCO and LVIS with diverse and high-quality annotations outperform all existing models. The code and trained models are available at https://github.com/saic-vul/ritm_interactive_segmentation.
A Unified Research Data Infrastructure for Catalysis Research – Challenges and Concepts

Abstract Modern research methods produce large amounts of scientifically valuable data. Tools to process and analyze such data have advanced rapidly. Yet, access to large amounts of high‐quality data remains limited in many fields, including catalysis research. Implementing the concept of FAIR data (Findable, Accessible, Interoperable, Reusable) in the catalysis community would improve this situation dramatically. The German NFDI initiative (National Research Data Infrastructure) aims to create a unique research data infrastructure covering all scientific disciplines. One of the consortia, NFDI4Cat, proposes a concept that serves all aspects and fields of catalysis research. We present a perspective on the challenging path ahead. Starting out from the current state, research needs are identified. A vision for a integrating all research data along the catalysis value chain, from molecule to chemical process, is developed. Respective core development topics are discussed, including ontologies, metadata, required infrastructure, IP, and the embedding into research community. This Concept paper aims to inspire not only researchers in the catalysis field, but to spark similar efforts also in other disciplines and on an international level.
WITHDRAWN: Blockchain-based security &amp; privacy for biomedical and healthcare information exchange systems

Blockchain is now one of the world's fast-growing and most critical innovations. Many assume blockchain is about cryptocurrencies or bitcoin, but we can claim that it goes far further than that. It is a technology that generates unchangeable and distributed data records exchanged by individuals in network database systems and record digital events so that they cannot be changed or remembered before they reach their destinations. Most companies have in recent times used blockchain to reinvent their technology. Many of the prominent industries include finance, real estate, health, the Internet, insurance, and much more. Healthcare is one of those industries which takes blockchain very quickly. This chapter will address the blockchain impact on the healthcare and biomedical industry for security and privacy purpose.
Local descriptor-based multi-prototype network for few-shot Learning

Prototype-based few-shot learning methods are promising in that they are simple yet effective to handle any-shot problems, and many prototype associated works are raised since then. However, these traditional prototype-based methods generally use only one single prototype to represent a class, which essentially cannot effectively estimate the complicated distribution of a class. To tackle this problem, we propose a novel Local descriptor-based Multi-Prototype Network (LMPNet) in this paper, a well-designed framework that generates an embedding space with multiple prototypes. Specifically, the proposed LMPNet employs local descriptors to represent each image, which can capture more informative and subtler cues of an image than the normally adopted image-level features. Moreover, to alleviate the uncertainty introduced by the fixed construction (averaging over samples) of prototypes, we introduce a channel squeeze and spatial excitation (sSE) attention module to learn multiple local descriptor-based prototypes for each class through end-to-end learning. Extensive experiments on both few-shot and fine-grained few-shot image classification tasks have been conducted on various benchmark datasets, including miniImageNet, tieredImageNet, Stanford Dogs, Stanford Cars, and CUB-200-2010. The experimental results of our LMPNet on above datasets show tangibly learning performance improvements and distinguishable outcomes over the baseline models.
Artificial intelligence‐powered decentralized framework for Internet of Things in Healthcare 4.0

Abstract Remote patient monitoring and data management have gained much popularity in recent years because of their enhanced access to low‐cost healthcare services. A cloud‐based healthcare system provides numerous solutions for collecting patient data and offers on‐demand well‐managed reports to patients and healthcare providers. However, it equally suffers from single‐point failure, security, privacy, and non‐transparency issues with the data, impacting the continuity of the system. To resolve such concerns, this article proposes an artificial intelligence (AI)‐enabled decentralized healthcare framework that accesses and authenticates Internet of Things (IoT) devices and create trust and transparency in patient healthcare records (PHR). The mechanism is based on the AI‐enabled smart contracts and the conceptualization of the public blockchain network. Alongside this, the framework identifies the malicious IoT nodes in the system. The experimental analyses are performed on the real‐time test environment, and significant improvements are suggested in terms of device energy consumption, data request time, throughput, average latency, and transaction fee.
Development of career guidance quests using WebAR

Abstract Career guidance quests that involve solving intellectual and search problems in a game are associated with one’s future career, being one of modern means of recruiting school leavers to vocational and higher educational institutions. Such specific computer tools of virtual and augmented reality as pupils’ mobile Internet gadgets can enhance visual character and interactivity of quest problems. The article deals with applying augmented reality in the web environment to solving these tasks. The authors propose a prototype of a career guidance quest system using WebAR technology.
The Effect of Covid-19 Pandemic on Business Systems’ Innovation and Entrepreneurship and How to Cope with It: A Theatrical View

This study investigates the issue of coronavirus effects on business intelligence systems regarding business innovation and entrepreneurship. Even though the Covid-19 pandemic has become the most significant crisis in the modern world and has negatively influenced numerous life spheres and business activities, it is possible to mention a few positive effects that might be used by different business organizations. Thus, the paper analyzes the relevant literature and shows that many positive ones follow short-term negative effects. The used the literature review approach to review many studies to answer the study questions and support its claims mentioned previously. The findings reveal that the coronavirus stimulates individuals and businesses to mobilize their efforts to overcome the existing Covid-19 pandemic challenge in the spheres of business innovation and entrepreneurship. The present paper also mentioned a set of advices related to how to cope with dealing with such a pandemic and mentioned a set of practical and theoretical implications for businesses in the near future.
An Enhanced Hybrid Feature Selection Technique Using Term Frequency-Inverse Document Frequency and Support Vector Machine-Recursive Feature Elimination for Sentiment Classification

Sentiment classification is increasingly used to automatically identify a positive or negative sentiment in a text review.In classification, feature selection had always been a critical and challenging problem.Most of the related feature selection for sentiment classification techniques unable to overcome problems of evaluating the significant features that will reduce the classification performance.This paper proposes an enhanced hybrid feature selection technique to improve the sentiment classification based on machine learning approaches.First, two customer review datasets namely Sentiment Labelled and large IMDB are retrieved and pre-processed.Next, the proposed feature selection technique which is the hybridization of Term Frequency-Inverse Document Frequency (TF-IDF) and Supports Vector Machine (SVM-RFE) is developed and tested on these two datasets.TF-IDF aims to measure features importance.The SVM-RFE iteratively evaluates and ranks the features.For sentiment classification, only the ktop features from the ranked features will be used.Finally, the Support Vector Machine (SVM) classifier is deployed to observe the performance of the proposed technique.The performance is measured using accuracy, precision, recall, and F-measure.The experimental results show promising performances with 84.54% to 89.56% in the measurements especially from the large IMDB dataset.The results also outperformed other related techniques in certain datasets.Consequently, the proposed technique able to reduce from 19.25% to 70.5% of the features to be classified.This reduction rate is significant in optimally utilizing the computational resources while maintaining the efficiency of the classification performance.
Novel L1 Regularized Extreme Learning Machine for Soft-Sensing of an Industrial Process

Extreme learning machine (ELM) is suitable for nonlinear soft sensor development. Yet it faces an overfitting problem. To overcome it, this work integrates bound optimization theory with variational Bayesian (VB) inference to derive novel L1 norm-based ELMs. An L1 term is attached to the squared sum cost of prediction errors to formulate an objective function. Considering the nonconvexity and nonsmoothness of the objective function, this article uses bound optimization theory, and constructs a proper surrogate function to equivalently convert a challenging L1 norm-based optimization problem into easy one. Then, VB inference is adopted for optimizing the converted problem. Thus, an L1 norm-based ELM can be efficiently optimized by an alternating optimization algorithm with a proved convergence. Finally, a soft sensor is developed based on the proposed algorithm. An industrial case study is carried out to demonstrate that the proposed soft sensor is competitive against recent ones.
Digital inequality at home. The school as compensatory agent

Following the lockdown caused by the COVID-19 crisis, the forced digitization of teaching at all levels of education has highlighted the social problem of digital inequality at home. The article addresses this issue by looking at the role of both social background, as measured by socio-economic-status (SES), in this inequality, and that of schools. A multidimensional approach to digital inequality is proposed, incorporating the frequency and quality of use of digital media, as well as ICT access. To this end, multiple structural equation models are estimated using data from the last PISA cycle (2018), for a total of 161,443 students from 6261 schools and 21 European countries, to check the influence on each of these three dimensions of both the SES and the integration of ICT in schools. The results confirm that for most European countries: (1) access to ICT at home is influenced to a greater extent by the family's SES than by the integration of ICT at school; (2) both the frequency and quality of use of ICT at home are influenced more by the integration of ICT at school than by the SES of the family, while in some countries the influence of the social aspect is practically irrelevant. Therefore, the integration of ICT in schools emerges as a compensatory measure for the social inequalities of students and may contribute to the reduction of digital inequality.
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey

Stereo matching is one of the longest-standing problems in computer vision with close to 40 years of studies and research. Throughout the years the paradigm has shifted from local, pixel-level decision to various forms of discrete and continuous optimization to data-driven, learning-based methods. Recently, the rise of machine learning and the rapid proliferation of deep learning enhanced stereo matching with new exciting trends and applications unthinkable until a few years ago. Interestingly, the relationship between these two worlds is two-way. While machine, and especially deep, learning advanced the state-of-the-art in stereo matching, stereo itself enabled new ground-breaking methodologies such as self-supervised monocular depth estimation based on deep networks. In this paper, we review recent research in the field of learning-based depth estimation from single and binocular images highlighting the synergies, the successes achieved so far and the open challenges the community is going to face in the immediate future.
Geometry-based Distance Decomposition for Monocular 3D Object Detection

Monocular 3D object detection is of great significance for autonomous driving but remains challenging. The core challenge is to predict the distance of objects in the absence of explicit depth information. Unlike regressing the distance as a single variable in most existing methods, we propose a novel geometry-based distance decomposition to recover the distance by its factors. The decomposition factors the distance of objects into the most representative and stable variables, i.e. the physical height and the projected visual height in the image plane. Moreover, the decomposition maintains the self-consistency between the two heights, leading to robust distance prediction when both predicted heights are inaccurate. The decomposition also enables us to trace the causes of the distance uncertainty for different scenarios. Such decomposition makes the distance prediction interpretable, accurate, and robust. Our method directly predicts 3D bounding boxes from RGB images with a compact architecture, making the training and inference simple and efficient. The experimental results show that our method achieves the state-of-the-art performance on the monocular 3D Object Detection and Bird’s Eye View tasks of the KITTI dataset, and can generalize to images with different camera intrinsics <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .