Reviewerly/exHarmony huggingface dataset:
Decentralizing Information Technology: The Advent of Resource Based Systems

The growth of the Bitcoin network during the first decade of its operation to a global system is a singular event in the deployment of Information Technology systems. Can this approach serve as a broader paradigm for Information Technology services beyond the use case of digital currencies? We investigate this question by introducing the concept of resource based systems and their four fundamental characteristics: (i) resource-based operation, (ii) tokenomics, (iii) decentralized service provision, and (iv) rewards sharing. We explore these characteristics, identify design goals and challenges and investigate some crucial game theoretic aspects of reward sharing that can be decisive for their effective operation.
Publishing Ecological Data in a Repository: An Easy Workflow for Everyone

Abstract For many ecologists, publishing data in a data repository is a new, unfamiliar task. To reduce the learning curve, the Environmental Data Initiative has developed user‐friendly software to make capturing and submitting data and metadata a simple process. In this article, we introduce ezEML and discuss use cases for researchers who publish data infrequently or information managers who regularly update multiple datasets.
Ghost-Free Multi-Exposure Image Fusion Technology Based on the Multi-Scale Block LBP Operator

This paper proposes a ghost-free multi-exposure image fusion technique based on the multi-scale block LBP (local binary pattern) operator. The method mainly includes two steps: first, the texture variation, brightness, and spatial consistency weight maps of the image are computed, and then these three image features are used to construct the initial weight map. Finally, the multi-resolution method is used to fuse the images to obtain the resulting image. The main advantage of this technique lies in the step of extracting the details of the source image based on the multi-scale block LBP operator, which is used to preserve the details of the brightest and darkest areas in high dynamic range scenes and preserve the texture features of the source image. Another advantage is that a new LBP operator-based motion detection method is proposed for fusing multi-exposure images in dynamic scenes containing moving objects. In addition, this paper also studies two spatially consistent weight distribution methods and compares and discusses the effects of these two methods on the results of dynamic image fusion. Through a large number of experimental comparisons, the superiority and feasibility of this method are proved.
Strategic University Management in the Context of Digitalization: The Experience of the World’s Leading Universities

Introduction. The research paper’s aim is to study the ongoing digitalization in all spheres of economy, which inevitably impacted on the strategic university management. It explains how universities and higher educational institutions strategically positioned themselves and searched for the novel pathways for innovation and development, which was further reinforced by the COVID-19 pandemic and the digital surge it brought. Materials and Methods. The authors analyzed the strategic university management in the context of digitalization using the literature review and their own analysis. They focus on the experience of the world’s best universities for inspiring the institutions of higher education in other countries and regions, helping them to improve information and communication technologies and digital skills. Results. Our results corroborate the ongoing digitalization in all spheres of economy and social life which was pushed further by the COVID-19 pandemic contributed to the transformation of the higher educational institutions challenging them to embark upon the path of in-depth transition, restructuring, and re-thinking their role and their mission with regard to the new challenges and novel technologies that are available on the market Discussion and Conclusion. The outcomes present interesting possibilities for their application in practice for some countries, most notably Russia. They can be of a special interest not only for the government officials responsible for fostering higher education and devising guidelines for the strategic management of universities and higher educational institutions, but also for the academic environment with its researchers and lecturers who seek to innovate based on the most recent and advanced trends in higher education (e.g. ministries and national authorities responsible for science, research, and education in different countries, such as the Ministry of Science and Higher Education, Ministry of National Education, Ministry for Research, as well as National Educational Authority or the World Bank, just to name a few).
Recognition of Student Emotions in Classroom Learning Based on Image Processing

During the learning process, students should keep positive emotions like delightfulness, cheerfulness, joy, and enthusiasm, and try to realize happy learning. This would improve learning efficiency and learning effect. However, most of the existing models cannot recognize negative discrete emotions and dimensional emotions, such as indignation and sadness, of students in classroom learning. To solve the problem, this paper deeply explores the recognition of student emotions in classroom learning based on image processing. Specifically, the authors designed a recognition model of emotional state of classroom learning, modeled the discrete emotions of students in classroom learning, and discussed the relationship between learning effect, response efficacy and stimulus. Furthermore, the generation process of student emotions in classroom learning was analyzed, and the recognition model of emotional state of classroom learning was finalized based on ResNet18. In addition, the recognition model was optimized by introducing the local importance pooling and adding a recalibration module. The experimental results verify the effectiveness of the constructed model.
Robot location privacy protection based on Q-learning particle swarm optimization algorithm in mobile crowdsensing

In the recent years, with the rapid development of science and technology, robot location-based service (RLBS) has become the main application service on mobile intelligent devices. When people use location services, it generates a large amount of location data with real location information. If a malicious third party gets this location information, it will cause the risk of location-related privacy disclosure for users. The wide application of crowdsensing service has brought about the leakage of personal privacy. However, the existing privacy protection strategies cannot adapt to the crowdsensing environment. In this paper, we propose a novel location privacy protection based on the Q-learning particle swarm optimization algorithm in mobile crowdsensing. By generalizing tasks, this new algorithm makes the attacker unable to distinguish the specific tasks completed by users, cuts off the association between users and tasks, and protects users' location privacy. The strategy uses Q-learning to continuously combine different confounding tasks and train a confounding task scheme that can output the lowest rejection rate. The Q-learning method is improved by particle swarm optimization algorithm, which improves the optimization ability of the method. Experimental results show that this scheme has good performance in privacy budget error, availability, and cloud timeliness and greatly improves the security of user location data. In terms of inhibition ratio, the value is close to the optimal value.
GAN-Based Encoding Model for Reversible Image Steganography

In carrying out reversible image steganography, the Generative Adversarial Networks (GANs-based) models have proven to be the most suitable deep learning models for image steganography. Image steganography is a steganography system that hides secret data in an image cover medium without arousing suspicion, and it is defined by the ability to reconstruct the cover medium with no visible distortion after the steganography system has been decoded by extracting the hidden data. In this study, we try achieve the encoding phase in image steganography, where two GAN-base models (CycleGAN and DCGAN) were proposed. Empirical analysis was done to determine a better model for the encoding of image steganography. The Peak Signal-to-Noise Ratio (PSNR), the Structural Similarity Index Metric (SSIM), and bit per pixel (bpp) were used as the metrics for the analysis. The outcome of DCGAN yielded (SSIM=0.48; PSNR=19.86; bpp=24.79) and the outcome of using CycleGAN yielded (SSIM=0.97; PSNR=41.45; bpp=24.97). These values concluded that the CycleGAN was preferable over the DCGAN. Hence, the CycleGAN was adopted as the encoding model.
Pixel‐wise supervision for presentation attack detection on identity document cards

Identity documents (or IDs) play an important role in verifying the identity of a person with wide applications in banks, travel, video-identification services and border controls. Replay or photocopied ID cards can be misused to pass ID control in unsupervised scenarios if the liveness of a person is not checked. To detect such presentation attacks on ID card verification process when presented virtually is a critical step for the biometric systems to assure authenticity. In this paper, a pixel-wise supervision on DenseNet is proposed to detect presentation attacks of the printed and digitally replayed attacks. The authors motivate the approach to use pixel-wise supervision to leverage minute cues on various artefacts such as moiré patterns and artefacts left by the printers. The baseline benchmark is presented using different handcrafted and deep learning models on a newly constructed in-house database obtained from an operational system consisting of 886 users with 433 bona fide, 67 print and 366 display attacks. It is demonstrated that the proposed approach achieves better performance compared to handcrafted features and Deep Models with an Equal Error Rate of 2.22% and Bona fide Presentation Classification Error Rate (BPCER) of 1.83% and 1.67% at Attack Presentation Classification Error Rate of 5% and 10%.
YASH: YET ANOTHER STEGO HIDING

We present a novel pseudorandom insertion least significant bit (LSB) based hiding scheme using Circle map byte output. The proposed algorithm is analysed by means of computer simulation. We evaluated the designed LSB method with NIST and ENT statistical packages, peak signal-to-noise ratio, and histogram analysis. The results data show good performance of the novel stego hiding
Prediction of Incident Solar Radiation Using a Hybrid Kernel Based Extreme Learning Machine

Forecasting solar radiation for a given region is an emerging field of study. It will help to identify the places for installing large-scale photovoltaic-systems, designing energy-efficient buildings and energy estimation. The different machine learning kernel-based approaches for prediction problems uses either a local or global kernel. These models can provide either strong training capability or good generalization performance. In this paper, a new hybrid kernel is proposed using the combination of a local and global kernel. A novel algorithm Hybrid Kernel-based Extreme Learning Machine is proposed for predictive modelling of Incident Solar Radiation (ISR) time series using new hybrid kernel. The proposed algorithm uses the surface, atmospheric, cloud properties obtained from the MODIS instrument and observed ISR at time t – 1 to predict ISR at time t. This study is conducted for 41 diverse sites of Australia for the period of 2012–2015. Further, the proposed model is experimented with other time series datasets to prove its efficacy. It is shown that the proposed methodology outperforms five other benchmarking methods in terms of MAE and Willmott’s Index (WI). Therefore, the suggested approach can be used for modelling solar energy at a national scale using remotely-sensed satellite footprints.
Hybrid quantum edge computing network

Edge computing network and quantum network are two emerging technologies in current communication fields. Edge computing has emerged to support the computational demand of delay-sensitive applications in which substantial computing and storage are deployed at the network edge close to data sources. Quantum network supports distributed quantum computing, which could provide exponentially computation capabilities for certain problems. The vision of a hybrid quantum-edge is to provide a fundamentally new computing paradigm by expanding the computing capabilities and security of edge computing with quantum computing and quantum communications. The distributed nature of edge computing networks will also enable new scalable quantum networking schemes and applications. Such a hybrid computing paradigm will achieve unparalleled capabilities that are provably impossible by using only classical computing or quantum computing schemes alone. In this paper, we introduce the concept of hybrid quantum-edge computing network and discuss its challenges and opportunities.
The role of libraries in promoting knowledge: How to make science truly open?

The modern open science makes an ecosystem comprising open scientific knowledge, science infrastructure, dialogue with other systems of knowledge and engaged social actors. The latter is channelized through the concept of wide social involvement in scientific research – the civil science concept. Despite the libraries are mostly oriented toward the former two components, which is due to their information functions, they are also able to build themselves in the ecosystem through collaboration with civil science project. However, such projects have not become popular, which significantly narrows down the potentialities of open science. The purpose of the article is to analyze the possibilities for libraries; entering the open science ecosystem through supporting is to open engagement of social players in science. The author discusses the obstacles on the way of the citizen science as a part open science ecosystem, and libraries involvement. Thus, underdevelopment of civil science in Russia may be partially due to insufficient dissemination of scientific thinking in the non-scientific spheres, the closeness of scientific community and its low interest in communicating with the wider audience. The analysis of 431 websites of Russian research institutes proves that just about 42 % of them popularize scientific activities. Besides, many research institute are not so eager to disseminate instructions for developing the sphere of open and citizen science. Consequently, the libraries cannot jump to the collaboration with citizen science. As the first step, the author suggests to contribute to popularization of science and scientific system and in particular, the open science, and within its context, of the citizen science oriented on the community and researchers.
Safe-haven or speculation? Research on price and risk dynamics of Bitcoin

This paper investigates the price and risk dynamics of Bitcoin. Applying SVAR to study Bitcoin, gold and U.S. dollar in one system, we find that neither the gold nor U.S. dollar can explain Bitcoin pricing dynamics in the short-run. We further apply the DCC-MGARCH model to study the risk correlations. The results show that there exists volatility spillover effect and dynamic correlation between three markets, which is magnified with the advent of COVID-19. We can thus draw a conclusion that the boom of Bitcoin is just a hype and speculative bubble.
Multihop fault-tolerant joint remote state preparation of an arbitrary single-qubit state

We present a strategy for multihop fault-tolerant joint remote state preparation of an arbitrary single-qubit state. To reduce the requirements of the channels, our scheme uses the nonmaximally entangled channel instead of the maximally entangled channel as the quantum channel. However, several typical operational errors such as X, Z, or XZ may occur on the channel qubits when the qubits are distributed from the former node to the next adjacent node. Note that the quantum error correction matrix is considered to deal with the influence of all possible operational errors by the final receiver instead of each intermediate node. Since all the measurement operations in the scheme can be executed in parallel and all the error corrections and the preparation of the target state are handled by the receiver with one unified unitary operation, our scheme significantly reduces both the time delay and the operation complexity and also enhances the implementation efficiency.
Factors Affecting the Success of E-Learning-Based Training using Learning Management System Platforms: Adaptations of Updated DeLone and McLean Models

The purpose of this study is to determine the factors, both direct and indirect, that affect the success of the learning management systems implemented in the Ministry of Education's “Sekolah Penggerak” training program. The DeLone-McClean IS Success Model was modified to be used as the study's analysis tool. The study employed a quantitative method with 122 samples. The survey technique employing Google Forms was used in the data collection process. The Partial Least Square Structural Model was analyzed using the Smart-PLS 3.0 software. As the t-statistic value was more than 1.96 and the p-value was less than 0.05, all hypotheses testing direct influence on success were accepted. Only four of the thirteen hypotheses on indirect influence were rejected. In conclusion, the DeLone-McClean IS Success Model can be used to evaluate the effectiveness of a learning management system in various e-learning activities.
A swarm optimizer with attention-based particle sampling and learning for large scale optimization

Abstract Attention mechanism, which is a cognitive process of selectively concentrating on certain information while ignoring others, has been successfully employed in deep learning. In this paper, we introduce the attention mechanism into a particle swarm optimizer and propose an attention-based particle swarm optimizer (APSO) for large scale optimization. In the proposed method, the attention mechanism is introduced such that activating different particles to participate in evolution at different stages of evolution. Further, an attention-based particle learning is devised to randomly select three particles from a predominant sub-swarm, which is activated by the attention mechanism, to guide the learning of particles. The cooperation of these two strategies could be employed to achieve a balanced evolution search, thus appropriately searching the space of large-scale optimization problems. Extensive experiments have been carried out on CEC’2010 and CEC’2013 large scale optimization benchmark functions to evaluate the performance of proposed method and to compare with related methods. The results show the superiority of proposed method.
Parallel optimization of tridimensional deformation measurement based on correlation function constraints of a multi-camera network

This study primarily investigates the low accuracy and redundant time-consuming problem of speckle registration in the full-field deformation measurement of slender and biggish specimens. To solve these problems, a parallel optimization of the tridimensional deformation measurement method is proposed based on what we believe is a novel correlation function constraints of a multi-camera network. First, a neotype correlation function is built based on the joint constraint relationship among the multiple cameras, which is capable of accurately restricting the search for homologous points in image pairs to the epipolar line, instead of the entire image, while significantly narrowing the search space and accelerating the search. The multiple cameras are bundled as a whole, thus reducing the dimension of the Jacobian matrix and the normalized matrix to a certain extent. Subsequently, more speckle images can be calculated in one iteration. Furthermore, the decomposition of the derived correlation function and the scheme of the parallel algorithm are decomposed via the kernel function based on the GPU parallel mechanism of the compute unified device architecture source program, thus increasing the subpixel search speed of speckle matching and ensuring the calculation performance of the stereo deformation measurement method to reach a higher level. Lastly, the experimental results revealed that the proposed strategy could allow the calculation speed-up ratio of speckle sequence and stereo registration to reach 20.390 times and 17.873 times, respectively, while ensuring the out-of-plane displacement average measuring accuracy to be higher than 0.179 mm within the spatial range of [2 m, 2 m, 3 m]. As a result, the proposed approach has crucial applications in rapid and stable tridimensional deformation measurement.
FT-HID: a large-scale RGB-D dataset for first- and third-person human interaction analysis

Analysis of human interaction is one important research topic of human motion analysis. It has been studied either using first-person vision (FPV) or third-person vision (TPV). However, the joint learning of both types of vision has so far attracted little attention. One of the reasons is the lack of suitable datasets that cover both FPV and TPV. In addition, existing benchmark datasets of either FPV or TPV have several limitations, including the limited number of samples, participant subjects, interaction categories, and modalities. In this work, we contribute a large-scale human interaction dataset, namely FT-HID dataset. FT-HID contains pair-aligned samples of first-person and third-person visions. The dataset was collected from 109 distinct subjects and has more than 90K samples for three modalities. The dataset has been validated by using several existing action recognition methods. In addition, we introduce a novel multi-view interaction mechanism for skeleton sequences, and a joint learning multi-stream framework for first-person and third-person visions. Both methods yield promising results on the FT-HID dataset. It is expected that the introduction of this vision-aligned large-scale dataset will promote the development of both FPV and TPV, and their joint learning techniques for human action analysis.
AI copyright

Artificial intelligences creating images inspired by existing art is raising concerns over artists livelihoods, finds Matthew Sparkes
Unsupervised and Pseudo-Supervised Vision-Language Alignment in Visual Dialog

Visual dialog requires models to give reasonable answers according to a series of coherent questions and related visual concepts in images. However, most current work either focuses on attention-based fusion or pre-training on large-scale image-text pairs, ignoring the critical role of explicit vision-language alignment in visual dialog. To remedy this defect, we propose a novel unsupervised and pseudo-supervised vision-language alignment approach for visual dialog (AlignVD). Firstly, AlginVD utilizes the visual and dialog encoder to represent images and dialogs. Then, it explicitly aligns visual concepts with textual semantics via unsupervised and pseudo-supervised vision-language alignment (UVLA and PVLA). Specifically, UVLA utilizes a graph autoencoder, while PVLA uses dialog-guided visual grounding to conduct alignment. Finally, based on the aligned visual and textual representations, AlignVD gives a reasonable answer to the question via the cross-modal decoder. Extensive experiments on two large-scale visual dialog datasets have demonstrated the effectiveness of vision-language alignment, and our proposed AlignVD achieves new state-of-the-art results. In addition, our single model has won first place on the visual dialog challenge leaderboard with a NDCG metric of 78.70, surpassing the previous best ensemble model by about 1 point.