Reviewerly/exHarmony huggingface dataset:
Image watermarking using separable fractional moments of Charlier–Meixner

This paper proposes a new set of discrete orthogonal separable moments of fractional order, named Fractional Charlier–Meixner Moments (FrCMMs). The latter are constructed from fractional Charlier polynomials (FrCPs) and fractional Meixner polynomials (FrMPs) proposed in this paper. The proposed FrMPs are constructed algebraically using the spectral decomposition of classical Meixner polynomials and singular value decomposition (SVD). The proposed FrCMMs generalize the separable moments of Charlier–Meixner of integer order (CMMs). In addition, FrCMMs are characterized by the polynomial parameters and by the fractional orders of the two fractional kernel functions of Charlier and Meixner, which allows them to be used efficiently for different applications such as local and global image reconstruction and image watermarking. Based on the proposed FrCMMs, a new watermarking scheme for copyright protection of digital images in the transform domain is proposed where the watermark is embedded in the FrCMM coefficients leading to an efficient watermarking scheme in terms of imperceptibility, robustness and security. The performances of the proposed moments are evaluated and compared with discrete fractional moments existing in the literature and with classical separable moments of integer order.
Deep Learning-Based Thermal Image Reconstruction and Object Detection

Recently, thermal cameras are being widely used in various fields, such as intelligent surveillance, biometrics, and health monitoring. However, the high cost of the thermal cameras poses a challenge in terms of purchase. Additionally, thermal images have an issue pertaining to blurring caused by object movement, camera movement, and camera focus settings. There have been very few studies on image restoration centered around thermal images to address such problems. Moreover, it is important to increase the processing speed of image restoration methods to jointly conduct with methods such as action recognition and object tracking that use temporal information from thermal videos. However, no study has been conducted on simultaneously performing super-resolution reconstruction and deblurring using thermal images. Furthermore, existing studies on object detection using thermal images have errors owing to the incapability in distinguishing reflections on the surrounding ground or wall due to the heat radiated from the object. To address such issues, this study proposes a deep learning-based thermal image restoration method that simultaneously performs super-resolution reconstruction and deblurring. According to recent development of deep learning, generative adversarial network (GAN)-based methods which have ability to preserve texture details in images, and yield sharper and more plausible textures than classical feed forward encoders show success in image-to-image translation tasks. Considering the advantages of GAN, we propose a deblur-SRRGAN for thermal image reconstruction. In addition, we propose a light-weighted Mask R-CNN for object detection in the reconstructed thermal image. For the input, we employ an image processing method that converts 1-channel thermal images (often used in the existing studies) into 3-channel images. The results of the experiments conducted using self-collected databases and an open database demonstrate that our method outperforms the state-of-the-art methods.
Domain-Aware Unsupervised Hyperspectral Reconstruction for Aerial Image Dehazing

Haze removal in aerial images is a challenging problem due to considerable variation in spatial details and varying contrast. Changes in particulate matter density often lead to degradation in visibility. Therefore, several approaches utilize multi-spectral data as auxiliary information for haze removal. In this paper, we propose SkyGAN for haze removal in aerial images. SkyGAN consists of 1) a domain-aware hazy-to-hyperspectral (H2H) module, and 2) a conditional GAN (cGAN) based multi-cue image-to-image translation module (I2I) for dehazing. The proposed H2H module reconstructs several visual bands from RGB images in an unsupervised manner, which overcomes the lack of hazy hyperspectral aerial image datasets. The module utilizes task supervision and domain adaptation in order to create a "hyperspectral catalyst" for image dehazing. The I2I module uses the hyperspectral catalyst along with a 12-channel multi-cue input and performs effective image de-hazing by utilizing the entire visual spectrum. In addition, this work introduces a new dataset, called Hazy Aerial-Image (HAI) dataset, that contains more than 65,000 pairs of hazy and ground truth aerial images with realistic, non- homogeneous haze of varying density. The performance of SkyGAN is evaluated on the recent SateHaze1k dataset as well as the HAI dataset. We also present a comprehensive evaluation of HAI dataset with a representative set of state-of-the-art techniques in terms of PSNR and SSIM.
The ethical dimension of digital competence in teacher training

Communication technologies should be considered as a means available for people to pursue the objectives of supporting the development of equality, freedom, solidarity and socialisation. In this case, Infant and Primary Education teachers play an important role in the acquisition of digital competences by their students. In this article we ask to what extent the university degrees of Early Childhood and Primary Education Teachers in Spain, where students are trained in subjects related to the use and learning of ICT applied to Education, address digital competences from the ethical dimension. We present here some of the results of a research project carried out in Spain, using the method of content analysis, in which a total of 118 study plans for the 2019-20 university degrees were reviewed. The research results indicated that, although digital competences were present in most of the assessed plans (78%), through a subject related to the learning and use of ICT in education, only 26.1% of the teaching guides incorporated the ethical dimension of this subject. This leads us to conclude that future teachers currently receive little training in ethics regarding the development of digital competences.
Value drivers of blockchain technology: A case study of blockchain-enabled online community

There is growing recognition that blockchain technology has significant potential to alter how organizations and people work and communicate. However, theoretical guidance concerning how organizations leverage blockchain technology to enhance value creation for users is still limited. Grounded in the socio-technical perspective and leveraging the rich data obtained from case analyses of blockchain-enabled online communities, this paper develops a theoretical model to identify the core value drivers that blockchain enables for online communities. The core value drivers include: a reputation-value system, data ownership mechanisms, and verification & tracking mechanisms. Our findings suggest that these three value drivers enhance value creation of online communities by motivating participation and protecting contributions.
Residual attention and other aspects module for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task designed to predict the sentiment polarity of each aspect term in a text. Recent research mainly uses neural networks to model text and utilizes attention mechanisms to interact for associate aspect terms and context to obtain more effective feature representation. However, the general attention mechanism is easy to lose the original information. Besides, in the multi-aspect text, the sentiment information of other aspect terms interferes with the sentiment analysis of the current aspect term likely. In this paper, we propose two models named RA-CNN and RAO-CNN for ABSA tasks. In RA-CNN, we apply CNN to model the aspect term and utilize a specially designed residual attention mechanism to interact with the text. Based on the RA-CNN, RAO-CNN adds other aspect terms module, which can reduce interference of sentiment information related to other aspect terms in the multi-aspect text. To verify the proposed models’ effectiveness, we conduct a large number of experiments and comparisons on seven public datasets. Experimental results show that our proposed models are useful and achieve state-of-the-art results.
Temperature network for few-shot learning with distribution-aware large-margin metric

Few-shot learning learns to classify unseen data with few training samples in hand and has attracted increasing attentions recently. In this paper, we propose a novel Temperature Network to tackle few-shot learning tasks motivated by three crucial factors that are seldom considered in the existing literature. First, to encourage compact intra-class distribution, a general improvement for prototype-based methods is proposed to ensure compact intra-class distribution and the effectiveness is theoretically and experimentally validated. Second, the proposed Temperature Network can implicitly generate query-specific prototypes and thus enjoys a more effective distribution-aware metric. Third, to further strengthen the generalization ability of the proposed model, a novel and simple large-margin based method is developed by leveraging the temperature function and we gradually tune the learning temperature to stabilize the training process. Moreover, we note that the commonly used datasets in few-shot learning are actually contrived from large-scale datasets, and thus may not represent a real few-shot problem. We propose a real-life few shot problem, i.e., Dermnet skin disease, to comprehensively evaluate the performance of few-shot learning methods. Experiments conducted on conventional datasets as well as the proposed skin disease dataset demonstrate the superiority of the proposed method over other state-of-the-art methods. The source code of our method is available.1
Enabling Efficient Data Sharing With Auditable User Revocation for IoV Systems

The rapid development of cloud computing and Internet of Things enables widely use of Internet of Vehicles (IoV) and drives a revolutionary change for intelligent transport system. The advent of 5G and fog computing architectures also facilitates applications of IoV and makes the enormous IoV data outsourcing and sharing in cloud and fog more convenient. However, the data outsourcing and sharing will incur many security concerns to IoV including data leakage and breach as the users cannot direct control their data. To guarantee IoV data security, a novel scheme is proposed for IoV data sharing with high efficiency. In our scheme, we utilize ciphertext-policy attribute-based encryption (CP-ABE) to achieve confidentiality and fine-grained access control for IoV data shared in cloud and fog. Nevertheless, as another intrinsic feature in IoV environment, the dynamicity in a group of connected vehicles brings some challenges to our scheme. To deal with this situation, we bring forward a concept of auditable user revocation for CP-ABE to adapt to the dynamic vehicle groups. Moreover, online/offline and verifiable outsourcing techniques are leveraged to improve the efficiency and guarantee the correctness of decryption. At the end, we demonstrate the security and efficiency for our scheme by giving detailed security analysis and performance evaluation with extensive experiments.
Information Needs Mining of COVID-19 in Chinese Online Health Communities

This study explores the information needs for the novel coronavirus pneumonia (COVID-19) in Chinese online health communities (OHCs). Based on the question and answer data about COVID-19 in six Chinese OHCs, topic mining and data analysis were conducted. We propose a CL-LDA topic model (Latent Dirichlet Allocation Model with co-occurrence of lexical meaning) based on lexical meaning co-occurrence analysis and LDA topic model. Four main information need topics and their proportion are found in this study, including symptom (45.50%), prevention (36.11%), inspection (10.97%), and treatment (7.42%). We also discover that men are most concerned about symptom information while women are most concerned about prevention information; young users have the largest proportion of information needs, and they are most concerned about prevention information. Experiment results show that the CL-LDA model can well adapt to the topic mining task of short text which is semantic sparse and lacking co-occurrence information in OHCs. The research results are helpful for OHCs to provide accurate information assistance and improve service quality.
Joint Encryption and Compression-Based Watermarking Technique for Security of Digital Documents

Recently, due to the increase in popularity of the Internet, the problem of digital data security over the Internet is increasing at a phenomenal rate. Watermarking is used for various notable applications to secure digital data from unauthorized individuals. To achieve this, in this article, we propose a joint encryption then-compression based watermarking technique for digital document security. This technique offers a tool for confidentiality, copyright protection, and strong compression performance of the system. The proposed method involves three major steps as follows: (1) embedding of multiple watermarks through non-sub-sampled contourlet transform, redundant discrete wavelet transform, and singular value decomposition; (2) encryption and compression via SHA-256 and Lempel Ziv Welch (LZW), respectively; and (3) extraction/recovery of multiple watermarks from the possibly distorted cover image. The performance estimations are carried out on various images at different attacks, and the efficiency of the system is determined in terms of peak signal-to-noise ratio (PSNR) and normalized correlation (NC), structural similarity index measure (SSIM), number of changing pixel rate (NPCR), unified averaged changed intensity (UACI), and compression ratio (CR). Furthermore, the comparative analysis of the proposed system with similar schemes indicates its superiority to them.
MeliusNet: An Improved Network Architecture for Binary Neural Networks

Binary Neural Networks (BNNs) are neural networks which use binary weights and activations instead of the typical 32-bit floating point values. They have reduced model sizes and allow for efficient inference on mobile or embedded devices with limited power and computational resources. However, the binarization of weights and activations leads to feature maps of lower quality and lower capacity and thus a drop in accuracy compared to their 32-bit counterparts. Previous work has increased the number of channels or used multiple binary bases to alleviate these problems. In this paper, we instead present an architectural approach: MeliusNet. It consists of alternating a DenseBlock, which increases the feature capacity, and our proposed ImprovementBlock, which increases the feature quality. Experiments on the ImageNet dataset demonstrate the superior performance of our MeliusNet over a variety of popular binary architectures with regards to both computation savings and accuracy. Furthermore, BNN models trained with our method can match the accuracy of the popular compact network MobileNet-v1 in terms of model size and number of operations. Our code is published online: https://github.com/hpi-xnor/BMXNet-v2.
Obstacle avoidance and model‐free tracking control for home automation using bio‐inspired approach

Abstract This article proposes a control algorithm for obstacle avoidance and trajectory tracking for a redundant‐manipulator in smart‐homes. The redundancy provides dexterity and flexibility for the applications like picking, dropping, and transporting objects, tracking predefined paths while avoiding obstacles. The obstacle avoidance is one of the critical problems that need to be addressed. Our proposed algorithm, zeroing neural network with beetle antennae search (ZNNBAS), unifies these two problems into a single constrained optimization problem, which includes a penalty function to reward the optimizer on avoiding obstacles while tracking the reference trajectory. The penalty function is based on the principle of maximizing the minimum distance between the joints of the manipulator and the obstacles. Gilbert Johnson Keerthi (GJK) algorithm is used to calculate the distance between the manipulator and the obstacle as it uses the 3D geometry of the object and manipulator. To test the working of ZNNBAS, we simulated the model of the KUKA LBR robotic manipulator. We used two reference trajectories, that is, hypotrochoid and a character “M,” with an arbitrarily shaped obstacle. The simulation results show that ZNNBAS was able to trace the reference path while successfully avoiding the obstacle accurately.
Chaos and compressive sensing based novel image encryption scheme

Out of various cryptographic attacks, Chosen-Plaintext Attack (CPA) is one of the most powerful and widely used attack on encrypted images. In order to efficiently resist such a strong attack, a novel chaos and compressive sensing based image encryption algorithm is presented in this work. Firstly, the original plaintext image is compressed via Orthogonal Matching Pursuit with Partially Known Support (OMP-PKS) and then the compressed image is confused and diffused using TD-ERCS and Skew-tent chaotic maps, respectively. Correlation among the compressed pixels is break down via confusing the image pixels using Tangent Delay Ellipse Reflecting Cavity Map System (TD-ERCS). Skew-tent chaotic map is employed for the pixel diffusion process. To get the final ciphertext image, the confused pixels are further changed through bitwise XORed operation via random matrix. For the sake of higher security, the initial conditions of chaotic maps are made dependent on the plaintext image and the parameters are computed via SHA-512. Furthermore, to decrease the transmission bandwidth, the measurement matrix is generated via Beta chaotic map. Instead of sending the whole measurement matrix, the sender will just send the Beta chaotic map initial conditions and control parameters (key) values along with the compressed ciphertext. The reliability and robustness of the designed image compression and encryption scheme are verified via experimental analysis and simulation results. All the experimental and simulation results are in favor of the proposed scheme.
Attenuation Coefficient Guided Two-Stage Network for Underwater Image Restoration

Underwater images suffer from severe color casts, low contrast and blurriness, which are caused by scattering and absorption when light propagates through water. However, existing deep learning methods treat the restoration process as a whole and do not fully consider the underwater physical distortion process. Thus, they cannot adequately tackle both absorption and scattering, leading to poor restoration results. To address this problem, we propose a novel two-stage network for underwater image restoration (UIR), which divides the restoration process into two parts viz. horizontal and vertical distortion restoration. In the first stage, a model-based network is proposed to handle horizontal distortion by directly embedding the underwater physical model into the network. The attenuation coefficient, as a feature representation in characterizing water type information, is first estimated to guide the accurate estimation of the parameters in the physical model. For the second stage, to tackle vertical distortion and reconstruct the clear underwater image, we put forth a novel attenuation coefficient prior attention block (ACPAB) to adaptively recalibrate the RGB channel-wise feature maps of the image suffering from the vertical distortion. Experiments on both synthetic dataset and real-world underwater images demonstrate that our method can effectively tackle scattering and absorption compared with several state-of-the-art methods.
LAG-Net: Multi-Granularity Network for Person Re-Identification via Local Attention System

Person re-identification (Re-ID) is a challenging research topic which aims to retrieve the pedestrian images of the same person that captured by non-overlapping cameras. Existing methods either assume the body parts of the same person are well-aligned, or use attention selection mechanisms to constrain the effective region of feature learning. But these methods concentrate only on coarse feature representation and cannot model complex real scenes effectively. We propose a novel Local Attention Guided Network (LAG-Net) to not only exploit the most salient area among different people, but also extract important local detail through a Local Attention System (LAS). LAS is an attention selection unit that could extract approximate semantic local features of human body parts without extra supervision. To learn discriminative attention feature representation, we explore an attention feature regularization scheme to enhance the relevance of body part features that belong to same personal identity. Considering the effectiveness of feature augmentation in the Re-ID task and the defect of the existing methods, we propose a Batch Attention DropBlock (BA-DropBlock) to further improve DropBlock by combining the attention selection mechanism. Results on mainstream datasets demonstrate the superiority of our model over the state-of-the-art. Especially, our approach exceeds the current best method by a large margin of 4.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\%}$</tex-math></inline-formula> on the most challenging dataset CUHK03.
The Sars-Cov-2 Pandemic and Agile Methodologies in Software Development: A Multiple Case Study in Germany

In recent years, agile methodologies have been established in software development. Today, many companies use agile or hybrid approaches in software development projects. The Sars-Cov-2 pandemic has led to a paradigm shift in the way people work in Germany. While it was customary for German software development teams to work co-located before the pandemic, teams have been working on a distributed remote basis since March 2020. Studies show that distributed work impacts the performance of agile software development teams. To examine the effects of the Sars-Cov-2 pandemic on agile software development in Germany, we planned, carried out, and evaluated a multiple case study with three cases. The results show that the majority of teams did not experience any loss in performance. We present some problems and challenges and derive specific suggestions for software development practice from the results of the study.
Three Variants of Differential Privacy: Lossless Conversion and Applications

We consider three different variants of differential privacy (DP), namely approximate DP, Rényi DP (RDP), and hypothesis test DP. In the first part, we develop a machinery for optimally relating approximate DP to RDP based on the joint range of two f-divergences that underlie the approximate DP and RDP. In particular, this enables us to derive the optimal approximate DP parameters of a mechanism that satisfies a given level of RDP. As an application, we apply our result to the moments accountant framework for characterizing privacy guarantees of noisy stochastic gradient descent (SGD). When compared to the state-of-the-art, our bounds may lead to about 100 more stochastic gradient descent iterations for training deep learning models for the same privacy budget. In the second part, we establish a relationship between RDP and hypothesis test DP which allows us to translate the RDP constraint into a tradeoff between type I and type II error probabilities of a certain binary hypothesis test. We then demonstrate that for noisy SGD our result leads to tighter privacy guarantees compared to the recently proposed f-DP framework for some range of parameters.