Reviewerly/exHarmony huggingface dataset:
Increasing the security and traceability of biological samples in biobanks by blockchain technology

One of the main tasks in a biobank consists in storing biological samples in a high-quality condition in order to future research. At moment, there exist many applications to manage a biobank. However, in general, these are web-based applications. In these web-based applications different tasks can be done. Among them, it is possible to remark the following: informed consent, confidentiality, non-profit, respect for quality and safety standards, including traceability of samples. In this paper, we describe a blockchain smart contract to ensure the traceability of the processes done in a biobank meaning a step forward to guarantee this traceability. Use of blockchain technology to improve security, integrity and traceability of the processes carried out in a biobank. In particular IBM Hyperledger Fabric. As a result, a set of smart contracts have been developed describing the biobank processes. Improvement of the security, integrity, and traceability of samples in biobanks.
Impact of Remittances on Income Inequality: Empirical Evidence from Emerging Economies

Emerging economies are becoming increasingly important role and are expected as a new engine of the global economy. The purpose of this report is to examine the impact of remittances on inequality in 18 emerging economies between 1985–2019. The study result confirms that remittances have a negative impact on income inequality, in which, the more received remittances, the lower the level of income inequality in these countries. Besides, foreign direct investment, liquidity of the economy, and trade openness also help to reduce income inequality. However, the increase in GDP per capita, government expenditure, and inflation can widen the income gap between rich and poor people in these countries. Finally, some policy implications are suggested for reducing income inequality in these countries in the future.
SD-HRNet: Slimming and Distilling High-Resolution Network for Efficient Face Alignment

Face alignment is widely used in high-level face analysis applications, such as human activity recognition and human-computer interaction. However, most existing models involve a large number of parameters and are computationally inefficient in practical applications. In this paper, we aim to build a lightweight facial landmark detector by proposing a network-level architecture-slimming method. Concretely, we introduce a selective feature fusion mechanism to quantify and prune redundant transformation and aggregation operations in a high-resolution supernetwork. Moreover, we develop a triple knowledge distillation scheme to further refine a slimmed network, where two peer student networks could learn the implicit landmark distributions from each other while absorbing the knowledge from a teacher network. Extensive experiments on challenging benchmarks, including 300W, COFW, and WFLW, demonstrate that our approach achieves competitive performance with a better trade-off between the number of parameters (0.98 M-1.32 M) and the number of floating-point operations (0.59 G-0.6 G) when compared to recent state-of-the-art methods.
Self-Supervised Learning for the Distinction between Computer-Graphics Images and Natural Images

With the increasing visual realism of computer-graphics (CG) images generated by advanced rendering engines, the distinction between CG images and natural images (NIs) has become an important research problem in the image forensics community. Previous research works mainly focused on the conventional supervised learning framework, which usually requires a good quantity of labeled data for training. To our knowledge, we study, for the first time in the literature, the utility of the self-supervised learning mechanism for the forensic classification of CG images and NIs. The idea is to make use of a large number of readily available unlabeled data, along with a self-supervised training procedure on a well-designed pretext task for which labels can be generated in an automatic and convenient way without human manual labeling effort. Differing from existing self-supervised methods, based on pretext tasks targeted at image understanding, or based on contrastive learning, we propose carrying out self-supervised training on a forensics-oriented pretext task of classifying authentic images and their modified versions after applying various manipulations. Experiments and comparisons showed the effectiveness of our method for solving the CG forensics problem under different evaluation scenarios. Our proposed method outperformed existing self-supervised methods in all experiments. It could sometimes achieve comparable, or better, performance. compared with a state-of-the-art fully supervised method under difficult evaluation scenarios with data scarcity and a challenging forensic problem. Our study demonstrates the utility and potential of the self-supervised learning mechanism for image forensics applications.
The role of capacity constraints in Convolutional Neural Networks for learning random versus natural data

Convolutional neural networks (CNNs) are often described as promising models of human vision, yet they show many differences from human abilities. We focus on a superhuman capacity of top-performing CNNs, namely, their ability to learn very large datasets of random patterns. We verify that human learning on such tasks is extremely limited, even with few stimuli. We argue that the performance difference is due to CNNs’ overcapacity and introduce biologically inspired mechanisms to constrain it, while retaining the good test set generalisation to structured images as characteristic of CNNs. We investigate the efficacy of adding noise to hidden units’ activations, restricting early convolutional layers with a bottleneck, and using a bounded activation function. Internal noise was the most potent intervention and the only one which, by itself, could reduce random data performance in the tested models to chance levels. We also investigated whether networks with biologically inspired capacity constraints show improved generalisation to out-of-distribution stimuli, however little benefit was observed. Our results suggest that constraining networks with biologically motivated mechanisms paves the way for closer correspondence between network and human performance, but the few manipulations we have tested are only a small step towards that goal.
MS-CLAM: Mixed supervision for the classification and localization of tumors in Whole Slide Images

Given the size of digitized Whole Slide Images (WSIs), it is generally laborious and time-consuming for pathologists to exhaustively delineate objects within them, especially with datasets containing hundreds of slides to annotate. Most of the time, only slide-level labels are available, giving rise to the development of weakly-supervised models. However, it is often difficult to obtain from such models accurate object localization, e.g., patches with tumor cells in a tumor detection task, as they are mainly designed for slide-level classification. Using the attention-based deep Multiple Instance Learning (MIL) model as our base weakly-supervised model, we propose to use mixed supervision - i.e., the use of both slide-level and patch-level labels - to improve both the classification and the localization performances of the original model, using only a limited amount of patch-level labeled slides. In addition, we propose an attention loss term to regularize the attention between key instances, and a paired batch method to create balanced batches for the model. First, we show that the changes made to the model already improve its performance and interpretability in the weakly-supervised setting. Furthermore, when using only between 12 and 62% of the total available patch-level annotations, we can reach performance close to fully-supervised models on the tumor classification datasets DigestPath2019 and Camelyon16.
Workplace e-learning acceptance: combining symmetrical and asymmetrical perspectives

Purpose The use of e-learning in the workplace is increasing. This increase was mainly because of technological advancement within corporations, but the COVID-19 pandemic has further reinforced this trend. User acceptance is central to e-learning’s success; hence, this study aims to investigate workplace e-learning acceptance in Indonesia. Design/methodology/approach Using the unified theory of acceptance and use of technology (UTAUT) model, this study analyzed survey response data from employees in seven Indonesian industries that use e-learning for their corporate learning programs. The study combined partial least squares structural equation modeling (PLS-SEM) analysis with fuzzy-set qualitative comparative analysis (fsQCA) to gain symmetrical and asymmetrical perspectives. Findings Various combinations of UTAUT model-based antecedents in pursuing workplace e-learning acceptance were supported by the PLS-SEM and fsQCA results. Both analyses point to performance expectancy as the strongest predictor of intention to use e-learning. Research limitations/implications The study offers insight into the causal relationship between constructs in the UTAUT model and uncovers paths and combinations of constructs that lead to e-learning intention. Originality/value This study highlights complex causalities between constructs.
WorldFAIR Chemistry: Making IUPAC Assets FAIR

Abstract “Having chemical terminology and data available in the digital environment using standard file formats and standard identifiers will increase accessibly and interoperability of data by both humans and machines.”
Optimization for Anchor-Free Object Detection via Scale-Independent GIoU Loss

Arbitrary-oriented target detection is widely used in optical remote-sensing image processing, and there have been lots of anchor-based detectors using horizontal bounding boxes. However, the image targets of various scales and shapes make it difficult to tune optimal anchor parameters, whereas the complex background and nonmaximum suppression (NMS) require well-aligned bounding box to predict dense targets. In this letter, a scale-independent IoU (SIoU) loss is proposed for bounding box regression, which can adaptively adjust the shape of predicted boxes and speed up the convergence. Besides, the regression branch of the fully convolutional one-stage object detector (FCOS) is refined to implement the novel intersection over union (IoU) loss for rotated bounding box regression. Extensive experiments on HRSC2016 and a large-scale dataset for object detection in aerial images (DOTA) show that our method obtains 88.1% mean average precision (mAP) under an IoU threshold of 0.5 on HRSC2016, which is 1.1% higher than generalized IoU (GIoU) loss and 0.7% than complete IoU (CIoU) loss.
Exploring Students’ Perception on using Live worksheet as Self-directed Learning of Listening Skills in Online Education

Research on the use of digital technology in promoting English Foreign Language (EFL) students’ self-directed learning of listening skills has been widely acknowledged in the literature. However, few empirical studies have been conducted on interactive worksheets as mediated instruction in the teaching of listening. This study used a qualitative method, and seventy-seven students were selected from the Education Department at State University of Makassar. This research instrument is used in interviews. Grounded in a web-based learning approach, the researchers investigated the students’ perceived experiences in conducting the “liveworksheet” website and its potential to promote their self-learning, activeness, and autonomy in listening practices. As a result, the study highlights several significant findings: students’ self-directed learning, automatic scoring, accessing the website, students’ vocabulary mastery, students’ learning autonomy, and students’ technology skills. Moreover, this study suggests employing web-based “liveworksheet” activities in a technology-mediated EFL learning environment, thus increasing their self-directed learning and ease to make distance learning happen through the website. Therefore, this study highlighted the advantages of in-making live worksheets for teachers and students in answering live worksheets which are very important to support, provide interesting learning, and get their learning outcomes
Recommendation System from Microsoft News Data using TF-IDF and Cosine Similarity Methods

The rapidly growing information causes information overload, so news portals publish information massively. Readers need time to search and read more news, but the time relevance of news wears off quickly. A recommendation system is needed that can recommend news according to the preferences of readers. This study recommends news using the TF-IDF method. TF-IDF gives weight to each word in the news title, and then looks for similarity between stories using cosine similarity. To prove the accuracy of whether the system recommendation results were actually clicked by the reader, the recommendation results were matched with the reader's news history on the online news portal Microsoft News using a hit-rate. The hit-rate result in this study was 80.77%.
Augmented Reality Application with Multimedia Content to Support Primary Education

Education is continually reinventing itself to meet the growing needs of learners. In the context of an emergency, confinement and mobilization difficulties have necessitated the adoption of online education. This new offer brings technological means to conventional processes, but its main disadvantage is the limited access in some places. This has motivated the present study, which proposes the development of a mobile application using augmented reality (AR) to complement primary education. This application aims to disseminate multimedia content without needing a stable internet connection. A high-performance computer is required for the design and a mid-range smartphone for its execution. Four scripts are generated in c# programming language using Visual Studio. The environment and the three-dimensional objects are developed using Unity software and the packages ARFoundation, Unity MARS, and Vuforia. Two groups of 13 children each participated in the experimental testing. The experimental group used the application for two weeks to complement the virtual classes in the subjects of language, natural sciences, and mathematics. The analysis with SPSS software shows a statistically significant increase in the average grades compared to the control group. This research shows that the use of technology can contribute to improving the current teaching-learning processes.
Iteratively distributed instrumental variable-based pseudo-linear information filter for angle-only tracking

This paper presents a distributed filtering problem for three-dimensional angle-only target tracking (AOTT) in sensor (i.e., observer) networks. An instrumental variable-based pseudo-linear information filter (IVIF) is firstly derived on the basis of the designed bias-compensated pseudo-linear information filtering, with the help of summation forms of information quantities and bias compensation in a centralized fusion manner. Then, the distributed IVIF (DIVIF) is put forward by using finite-time average consensus to obtain the arithmetic means of defined information quantities and compensated bias in observer networks, which ensures that the filtering result of every observer is consistent with the centralized one. Finally, the iteratively DIVIF is proposed via gradually approaching the true values of relative distance and the corresponding angles between the target and every observer to get the filtering parameters more and more accurately, in order to achieve higher filtering precision. In addition, the computational complexity of the proposed method is also analyzed. The advantages of filtering precision of the proposed method over the existing pseudo-linear Kalman filter and its variants are demonstrated by an AOTT example in observer networks in terms of iteration steps, different levels of process noises and observer’s accuracy.
Review of CNN in aerial image processing

In recent years, deep learning algorithm has been used in many applications mainly in image processing of object detection and classification. The use of image processing techniques is becoming more interesting with the existence of drone technology with the employ of deep learning in aerial view image processing because of the high resolution and heaps of images taken. This paper aims to review neural networks specifically on the aerial view image by drones and to discuss the work principles and classic architectures of convolutional neural networks, its latest research trend and typical models along with target detection in object detection, image classification and semantic segmentation. In addition, this study also provided a specific application in the aerial image. Finally, the limitations of the convolutional network and expected future development trends were also discussed. Based on the findings, the deep learning algorithm was observed to provide high accuracy, it outperformed other generally image processing-based techniques.
Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super Resolution

Unsupervised real world super resolution (USR) aims to restore high-resolution (HR) images given low-resolution (LR) inputs, and its difficulty stems from the absence of paired dataset. One of the most common approaches is synthesizing noisy LR images using GANs (i.e., degradation generators) and utilizing a synthetic dataset to train the model in a supervised manner. Although the goal of training the degradation generator is to approximate the distribution of LR images given a HR image, previous works have heavily relied on the unrealistic assumption that the conditional distribution is a delta function and learned the deterministic mapping from the HR image to a LR image. In this paper, we show that we can improve the performance of USR models by relaxing the assumption and propose to train the probabilistic degradation generator. Our probabilistic degradation generator can be viewed as a deep hierarchical latent variable model and is more suitable for modeling the complex conditional distribution. We also reveal the notable connection with the noise injection of StyleGAN. Furthermore, we train multiple degradation generators to improve the mode coverage and apply collaborative learning for ease of training. We outperform several baselines on benchmark datasets in terms of PSNR and SSIM and demonstrate the robustness of our method on unseen data distribution. Code is available at https://github.com/sangyun884/MSSR .
Enhancing the security of free space optical communication system by employing chaos-based modulation scheme

Abstract Security and privacy are two main dominant features of any communication system. In this paper, physical layer security of free space optical communication system using chaotic modulation scheme i.e., differential chaos shift keying (DCSK) is analyzed, where eavesdropper is actively present near the receiver and interfering between the transmission of secret messages from a transmitter to the receiver. In this manuscript, we have derived analytical expressions for the average secrecy capacity and secrecy outage probability which is used as a metric for secrecy performance analysis. The channel characterization is carried out using gamma–gamma model for weak-to-strong turbulence conditions. The effect of physical layer parameters like transmission link length, spreading length, etc. are considered for evaluating the security performance of the system. Numerical analysis is carried out and graphical results are presented. The results depicted that a very good average secrecy capacity can be achieved even in the presence of eavesdropper, however, it requires a tradeoff between high signal-to-noise ratio of main channel and large values of spreading factor. The proposed system is very promising for the future secured communication systems.
Skeleton-Based Fall Detection with Multiple Inertial Sensors Using Spatial-Temporal Graph Convolutional Networks

The application of wearable devices for fall detection has been the focus of much research over the past few years. One of the most common problems in established fall detection systems is the large number of false positives in the recognition schemes. In this paper, to make full use of the dependence between human joints and improve the accuracy and reliability of fall detection, a fall-recognition method based on the skeleton and spatial-temporal graph convolutional networks (ST-GCN) was proposed, using the human motion data of body joints acquired by inertial measurement units (IMUs). Firstly, the motion data of five inertial sensors were extracted from the UP-Fall dataset and a human skeleton model for fall detection was established through the natural connection relationship of body joints; after that, the ST-GCN-based fall-detection model was established to extract the motion features of human falls and the activities of daily living (ADLs) at the spatial and temporal scales for fall detection; then, the influence of two hyperparameters and window size on the algorithm performance was discussed; finally, the recognition results of ST-GCN were also compared with those of MLP, CNN, RNN, LSTM, TCN, TST, and MiniRocket. The experimental results showed that the ST-GCN fall-detection model outperformed the other seven algorithms in terms of accuracy, precision, recall, and F1-score. This study provides a new method for IMU-based fall detection, which has the reference significance for improving the accuracy and robustness of fall detection.
INFWIDE: Image and Feature Space Wiener Deconvolution Network for Non-Blind Image Deblurring in Low-Light Conditions

Under low-light environment, handheld photography suffers from severe camera shake under long exposure settings. Although existing deblurring algorithms have shown promising performance on well-exposed blurry images, they still cannot cope with low-light snapshots. Sophisticated noise and saturation regions are two dominating challenges in practical low-light deblurring. In this work, we propose a novel non-blind deblurring method dubbed image and feature space Wiener deconvolution network (INFWIDE) to tackle these problems systematically. In terms of algorithm design, INFWIDE proposes a two-branch architecture, which explicitly removes noise and hallucinates saturated regions in the image space and suppresses ringing artifacts in the feature space, and integrates the two complementary outputs with a subtle multi-scale fusion network for high quality night photograph deblurring. For effective network training, we design a set of loss functions integrating a forward imaging model and backward reconstruction to form a close-loop regularization to secure good convergence of the deep neural network. Further, to optimize INFWIDE's applicability in real low-light conditions, a physical-process-based low-light noise model is employed to synthesize realistic noisy night photographs for model training. Taking advantage of the traditional Wiener deconvolution algorithm's physically driven characteristics and arisen deep neural network's representation ability, INFWIDE can recover fine details while suppressing the unpleasant artifacts during deblurring. Extensive experiments on synthetic data and real data demonstrate the superior performance of the proposed approach.