Reviewerly/exHarmony huggingface dataset:
Determinants of 21st-century digital skills: A large-scale survey among working professionals

The current workplace increasingly asks for workers with highly digitally skilled knowledge who produce and distribute ideas and information. As such, understanding the factors behind differences in the level of various 21st-century digital skills is of decisive importance. This study aims to examine (1) the level of 21st-century digital skills among knowledge workers, and (2) the determinants contributing to the level of these skills. The following digital skills are investigated: information, communication, collaboration, critical thinking, creativity, and problem solving. Potential determinants that can be influenced by stakeholders are also included, such as social support and training. A large-scale online survey was conducted among professionals (N = 1,222) who work within knowledge-intensive creative industries. The results show that the level of 21st-century digital skills varies considerably. Furthermore, each 21st-century digital skill is explained by a different set of determinants, thereby requiring unique approaches for the development of each skill.
Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network

In recent years, surface electromyography (sEMG) signals have been increasingly used in pattern recognition and rehabilitation. In this paper, a real-time hand gesture recognition model using sEMG is proposed. We use an armband to acquire sEMG signals and apply a sliding window approach to segment the data in extracting features. A feedforward artificial neural network (ANN) is founded and trained by the training dataset. A test method is used in which the gesture will be recognized when recognized label times reach the threshold of activation times by the ANN classifier. In the experiment, we collected real sEMG data from twelve subjects and used a set of five gestures from each subject to evaluate our model, with an average recognition rate of 98.7% and an average response time of 227.76 ms, which is only one-third of the gesture time. Therefore, the pattern recognition system might be able to recognize a gesture before the gesture is completed.
Deep inverse rendering for high-resolution SVBRDF estimation from an arbitrary number of images

In this paper we present a unified deep inverse rendering framework for estimating the spatially-varying appearance properties of a planar exemplar from an arbitrary number of input photographs, ranging from just a single photograph to many photographs. The precision of the estimated appearance scales from plausible when the input photographs fails to capture all the reflectance information, to accurate for large input sets. A key distinguishing feature of our framework is that it directly optimizes for the appearance parameters in a latent embedded space of spatially-varying appearance, such that no handcrafted heuristics are needed to regularize the optimization. This latent embedding is learned through a fully convolutional auto-encoder that has been designed to regularize the optimization. Our framework not only supports an arbitrary number of input photographs, but also at high resolution. We demonstrate and evaluate our deep inverse rendering solution on a wide variety of publicly available datasets.
eHealth Cloud Security Challenges: A Survey

Cloud computing is a promising technology that is expected to transform the healthcare industry. Cloud computing has many benefits like flexibility, cost and energy savings, resource sharing, and fast deployment. In this paper, we study the use of cloud computing in the healthcare industry and different cloud security and privacy challenges. The centralization of data on the cloud raises many security and privacy concerns for individuals and healthcare providers. This centralization of data (1) provides attackers with one-stop honey-pot to steal data and intercept data in-motion and (2) moves data ownership to the cloud service providers; therefore, the individuals and healthcare providers lose control over sensitive data. As a result, security, privacy, efficiency, and scalability concerns are hindering the wide adoption of the cloud technology. In this work, we found that the state-of-the art solutions address only a subset of those concerns. Thus, there is an immediate need for a holistic solution that balances all the contradicting requirements.
Visual Object Tracking by Hierarchical Attention Siamese Network

Visual tracking addresses the problem of localizing an arbitrary target in video according to the annotated bounding box. In this article, we present a novel tracking method by introducing the attention mechanism into the Siamese network to increase its matching discrimination. We propose a new way to compute attention weights to improve matching performance by a sub-Siamese network [Attention Net (A-Net)], which locates attentive parts for solving the searching problem. In addition, features in higher layers can preserve more semantic information while features in lower layers preserve more location information. Thus, in order to solve the tracking failure cases by the higher layer features, we fully utilize location and semantic information by multilevel features and propose a new way to fuse multiscale response maps from each layer to obtain a more accurate position estimation of the object. We further propose a hierarchical attention Siamese network by combining the attention weights and multilayer integration for tracking. Our method is implemented with a pretrained network which can outperform most well-trained Siamese trackers even without any fine-tuning and online updating. The comparison results with the state-of-the-art methods on popular tracking benchmarks show that our method achieves better performance. Our source code and results will be available at https://github.com/shenjianbing/HASN.
Proposing the use of blockchain to improve the solid waste management in small municipalities

The use of the “Blockchain” technological architecture is growing worldwide, given its structural characteristics aimed at security and information integrity, without the need for a central guarantor. Although Blockchain has become known for its support of crypto-coins (or crypto currencies), especially Bitcoin, its scope has become much larger, including businesses of relevant social impact. This paper presents an application of the Ethereum’s Blockchain digital architecture for the solid waste management in a small municipality in the State of São Paulo, Brazil. This application comes to replace a paper-based current system. The current system uses printed cards called Green Coins, presenting vulnerabilities that should be overcome with the implementation of the new system. The blockchain-based system provides financial management for the collection of waste in the municipality aiming at better health and socio-environmental education and the financial and social inclusion of volunteer citizens, through the use of social currency. The proposed application uses social crypto-coins and security support through Ethereum’s Blockchain, instead of the printed cards currently being used, including volunteer citizens, store owners and public agents. It should contribute in a relevant way to improving the quality of life in the municipality in relation to the typical aspects of sustainability: education, health, environment, social inclusion and local economy, contributing to achieve the Sustainable Development Goals (SDGs). The proposed application may eventually expand in to other municipalities, including major urban centers and private condominiums.
Adopting augmented reality in the age of industrial digitalisation

Industrial augmented reality (IAR) is one of the key pillars of the industrial digitalisation concepts, which connects workers with the physical world through overlaying digital information. Augmented reality (AR) market is increasing but still its adoption levels are low in industry. While companies strive to learn and adopt AR, there are chances that they fail in such endeavours due to lack of understanding key challenges and success factors in this space. This study identifies critical success factors and challenges for IAR implementation projects based on field experiments. The broadly used technology, organisation, environment (TOE) framework was used as a theoretical basis for the study, while 22 experiments were conducted for validation. It is found that, while technological aspects are of importance, organisational issues are more relevant for industry, which has not been reflected to the same extent in the literature.
Fitness-distance balance (FDB): A new selection method for meta-heuristic search algorithms

Selection methods have an important role in the meta-heuristic search (MHS) process. However, apart from a few successful methods developed in the past, new and effective studies have not been found in recent years. It is known that solution candidates selecting from the population during the search process directly affects the direction and success of the search. In this study, a new selection method based on fitness-distance balance (FDB) is developed in order to solve the premature convergence problem in the MHS process. Thanks to the developed method, solution candidates with the highest potential to improve the search process can be determined effectively and consistently from the population. Experimental studies have been conducted to test and verify the developed FDB selection method. For this purpose, 90 benchmark functions with different types and complexity levels have been used. In order to test the developed FDB method, numerous variants have been formed. These variants have been compared to each other to determine the most effective FDB variant. In the validation study, the FDB-SOS (FDB-based symbiotic organism search) algorithm is compared with thirteen well-known and up-to-date MHS techniques. The search performance of the algorithms has been analyzed by the Wilcoxon Rank Sum Test. The results show that the developed selection method makes a significant contribution to the meta-heuristic search process.
Massive MIMO Channel Estimation With an Untrained Deep Neural Network

This paper proposes a deep learning-based channel estimation method for multi-cell interference-limited massive MIMO systems, in which base stations equipped with a large number of antennas serve multiple single-antenna users. The proposed estimator employs a specially designed deep neural network (DNN) based on the deep image prior (DIP) network to first denoise the received signal, followed by conventional least-squares (LS) estimation. We analytically prove that our LS-type deep channel estimator can approach minimum mean square error (MMSE) estimator performance for high-dimensional signals, while avoiding complex channel inversions and knowledge of the channel covariance matrix. This analytical result, while asymptotic, is observed in simulations to be operational for just 64 antennas and 64 subcarriers per OFDM symbol. The proposed method also does not require any training and utilizes several orders of magnitude fewer parameters than conventional DNNs. The proposed deep channel estimator is also robust to pilot contamination and can even completely eliminate it under certain conditions.
A Multimodal Saliency Model for Videos With High Audio-Visual Correspondence

Audio information has been bypassed by most of current visual attention prediction studies. However, sound could have influence on visual attention and such influence has been widely investigated and proofed by many psychological studies. In this paper, we propose a novel multi-modal saliency (MMS) model for videos containing scenes with high audio-visual correspondence. In such scenes, humans tend to be attracted by the sound sources and it is also possible to localize the sound sources via cross-modal analysis. Specifically, we first detect the spatial and temporal saliency maps from the visual modality by using a novel free energy principle. Then we propose to detect the audio saliency map from both audio and visual modalities by localizing the moving-sounding objects using cross-modal kernel canonical correlation analysis, which is first of its kind in the literature. Finally we propose a new two-stage adaptive audiovisual saliency fusion method to integrate the spatial, temporal and audio saliency maps to our audio-visual saliency map. The proposed MMS model has captured the influence of audio, which is not considered in the latest deep learning based saliency models. To take advantages of both deep saliency modeling and audio-visual saliency modeling, we propose to combine deep saliency models and the MMS model via a later fusion, and we find that an average of 5% performance gain is obtained. Experimental results on audio-visual attention databases show that the introduced models incorporating audio cues have significant superiority over state-of-the-art image and video saliency models which utilize a single visual modality.
Blockchain-based fair payment smart contract for public cloud storage auditing

Cloud storage plays an important role in today’s cloud ecosystem. Increasingly clients tend to outsource their data to the cloud. In spite of its copious advantages, integrity has always been a significant issue. The audit method is commonly used to ensure integrity in cloud scenarios. However, traditional auditing schemes expect a third-party auditor (TPA), which is not always available in the real world. Also, the former scheme implies a limited pay-as-you-go service, as it requires the client to pay for the service in advance. In this paper, we aim to address the aforementioned drawback by adopting blockchain to replace TPA and designing a blockchain-based fair payment smart contract for public cloud storage auditing. In our system, data owner and cloud service provider (CSP) will run a blockchain-based smart contract. The contract ensures that the CSP is required to submit data possession proof regularly. The CSP gets paid only if the verification is passed; otherwise, it gets no remuneration but has to pay the penalties. To reduce the number of interactions in the execution of contract, we present the notion of non-interactive public provable data possession and design a blockchain-based smart contract for public cloud storage auditing based on this primitive.
Cryptanalysis of a DNA-based image encryption scheme

Recently, an image encryption scheme using 2D Hénon-Sine map and DNA coding approach is proposed. It adopts the permutation-diffusion architecture. The DNA random coding and exclusive OR is introduced for image diffusion, while image scrambling is implemented by pixel swapping operations. This paper reveals that this encryption scheme is not as secure as declared. Substitution boxes (s-boxes) are firstly employed to summarize the involved complicated DNA encryption operations, the whole encryption scheme is subsequently relaxed as an s-box-then-permutation cipher. Chosen-plaintext attack is feasible for recovering the equivalent secret elements of the s-boxes and permutation vector. The proposed concept of generalizing DNA encryption as s-box substitution is expected to be beneficial for security evaluation and theoretical design of DNA-based image encryption schemes in the future.
Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization

In response to recent criticism of gradient-based visualization techniques, we propose a new methodology to generate visual explanations for deep Convolutional Neural Networks (CNN) - based models. Our approach - Ablation-based Class Activation Mapping (Ablation CAM) uses ablation analysis to determine the importance (weights) of individual feature map units w.r.t. class. Further, this is used to produce a coarse localization map highlighting the important regions in the image for predicting the concept. Our objective and subjective evaluations show that this gradient-free approach works better than state-of-the-art Grad-CAM technique. Moreover, further experiments are carried out to show that Ablation-CAM is class discriminative as well as can be used to evaluate trust in a model.
The Struggle is Real: Insights from a Supply Chain Blockchain Case

Despite the anticipated benefits and the numerous announcements of pilot cases, we have seen very few successful implementations of blockchain technology (BCT) solutions in supply chains. Little is empirically known about the obstacles to blockchain adoption, particularly in a supply chain's interorganizational setting. In supply chains, blockchains' benefits, for example, BCT‐based tracking and tracing, are dependent on a critical mass of supply chain actors adopting the technology. While previous research has mainly been conceptual and has lacked both theory and empirical data, we propose a theory‐based model for interorganizational adoption of BCT. We use the proposed model to analyze a unique in‐depth revelatory case study. Our case study confirms previous conceptual work and reveals a paradox as well as several tensions between drivers for and against (positive and negative determining factors, respectively) of BCT adoption that must be managed in an interorganizational setting. In this vertical context, the adoption and integration decision of one supply chain actor recursively affects the adoption and integration decisions of the other supply chain actors. This paper contributes midrange theory on BCT in supply chain management (SCM), future research directions, and managerial insights on BCT adoption in supply chains.
SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing

Small and cluttered objects are common in real-world which are challenging for detection. The difficulty is further pronounced when the objects are rotated, as traditional detectors often routinely locate the objects in horizontal bounding box such that the region of interest is contaminated with background or nearby interleaved objects. In this paper, we first innovatively introduce the idea of denoising to object detection. Instance-level denoising on the feature map is performed to enhance the detection to small and cluttered objects. To handle the rotation variation, we also add a novel IoU constant factor to the smooth L1 loss to address the long standing boundary problem, which to our analysis, is mainly caused by the periodicity of angular (PoA) and exchangeability of edges (EoE). By combing these two features, our proposed detector is termed as SCRDet++. Extensive experiments are performed on large aerial images public datasets DOTA, DIOR, UCAS-AOD as well as natural image dataset COCO, scene text dataset ICDAR2015, small traffic light dataset BSTLD and our released S 2 TLD by this paper. The results show the effectiveness of our approach. The released dataset S 2 TLD is made public available, which contains 5,786 images with 14,130 traffic light instances across five categories.
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results

This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided along with a set of unpaired high-quality target images. In Track 1: Image Processing artifacts, the aim is to super-resolve images with synthetically generated image processing artifacts. This allows for quantitative benchmarking of the approaches w.r.t. a ground-truth image. In Track 2: Smartphone Images, real low-quality smart phone images have to be super-resolved. In both tracks, the ultimate goal is to achieve the best perceptual quality, evaluated using a human study. This is the second challenge on the subject, following AIM 2019, targeting to advance the state-of-the-art in super-resolution. To measure the performance we use the benchmark protocol from AIM 2019. In total 22 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem.
A 3-D Multi-Stable System With a Peanut-Shaped Equilibrium Curve: Circuit Design, FPGA Realization, and an Application to Image Encryption

A new 3-D chaotic dynamical system with a peanut-shaped closed curve of equilibrium points is introduced in this work. Since the new chaotic system has infinite number of rest points, the new chaotic model exhibits hidden attractors. A detailed dynamic analysis of the new chaotic model using bifurcation diagrams and entropy analysis is described. The new nonlinear plant shows multi-stability and coexisting convergent attractors. A circuit model using MultiSim of the new 3-D chaotic model is designed for engineering applications. The new multi-stable chaotic system is simulated on a field-programmable gate array (FPGA) by applying two numerical methods, showing results in good agreement with numerical simulations. Consequently, we utilize the properties of our chaotic system in designing a new cipher colour image mechanism. Experimental results demonstrate the efficiency of the presented encryption mechanism, whose outcomes suggest promising applications for our chaotic system in various cryptographic applications.
DeepEDN: A Deep-Learning-Based Image Encryption and Decryption Network for Internet of Medical Things

Internet of Medical Things (IoMT) can connect many medical imaging equipments to the medical information network to facilitate the process of diagnosing and treating for doctors. As medical image contains sensitive information, it is of importance yet very challenging to safeguard the privacy or security of the patient. In this work, a deep learning based encryption and decryption network (DeepEDN) is proposed to fulfill the process of encrypting and decrypting the medical image. Specifically, in DeepEDN, the Cycle-Generative Adversarial Network (Cycle-GAN) is employed as the main learning network to transfer the medical image from its original domain into the target domain. Target domain is regarded as a "Hidden Factors" to guide the learning model for realizing the encryption. The encrypted image is restored to the original (plaintext) image through a reconstruction network to achieve an image decryption. In order to facilitate the data mining directly from the privacy-protected environment, a region of interest(ROI)-mining-network is proposed to extract the interested object from the encrypted image. The proposed DeepEDN is evaluated on the chest X-ray dataset. Extensive experimental results and security analysis show that the proposed method can achieve a high level of security with a good performance in efficiency.
Variability and reproducibility in deep learning for medical image segmentation

Abstract Medical image segmentation is an important tool for current clinical applications. It is the backbone of numerous clinical diagnosis methods, oncological treatments and computer-integrated surgeries. A new class of machine learning algorithm, deep learning algorithms, outperforms the results of classical segmentation in terms of accuracy. However, these techniques are complex and can have a high range of variability, calling the reproducibility of the results into question. In this article, through a literature review, we propose an original overview of the sources of variability to better understand the challenges and issues of reproducibility related to deep learning for medical image segmentation. Finally, we propose 3 main recommendations to address these potential issues: (1) an adequate description of the framework of deep learning, (2) a suitable analysis of the different sources of variability in the framework of deep learning, and (3) an efficient system for evaluating the segmentation results.