Reviewerly/exHarmony huggingface dataset:
Multi-frame generative network for image super-resolution

In recent years, the field of image super-resolution has mainly focused on the single-image super-resolution (SISR) task, which is to estimate an HR image from a single LR input. Due to the ill-posed ness of the SISR problem, these methods are limited to increasing the high-frequency details of the image by learning the a priori of the image. And multi-frame super-resolution (MFSR) provides the possibility to reconstruct rich details using the spatial and temporal difference information between images. With the increasing popularity of array camera technology, this key advantage makes MFSR an important issue for practical applications. We propose a new structure to complete the task of multi-frame image super-resolution. Our network takes multiple noisy images as input and generates a denoised, super-resolution RGB image as output. First, we align the multi-frame images by estimating the dense pixel optical flow between the images, and construct an adaptive fusion module to fuse the information of all frames. Then we build a feature fusion network to simultaneously fuse the depth feature information of multiple LR images and the internal features of the initial high-resolution image. In order to evaluate real-world data, We use the BurstSR data set, which includes real images of smartphones and highresolution SLR cameras, to prove the effectiveness of the proposed multiframe image super-resolution algorithm.
Improved shadow suppression with amalgamation of AGWCD and exemplar based inpainting method

In today’s digital times, the quality of video frames is ubiquitous, and the presence of shadows is undesirable in computer vision applications. Shadow suppression is of paramount significance in crucial application areas, especially in outdoor scene environments. The objects present in the environment occlude the light. Most of the work in literature focuses on single shadow regions in a frame or an image. Different methods are proposed in the literature. This challenging area of shadows suppression is addressed with the proposed method, as a novel amalgamation, with Adaptive Gamma Weighted Correction and modified Exemplar based inpainting method. The paper discusses different single shadow scenarios and multiple distributed shadow regions. Across four datasets, and objective evaluation using three performance metrics, the obtained average Entropy of 7.032, ‘Blind Reference Image Spatial Quality Evaluator (BRISQUE)’ of 26.2031, and ‘No-Reference Image Quality Evaluator (NIQE)’ of 3.699 have demonstrated considerable results.
Promoting Techno-Scientific Biomedical Communication in Russia: Pro-government Non-profit Organizations

The rapidly expanding e-health market tends to reinvent the roles of science, entrepreneurship, and state. However, the results of such reinvention remain unclear. This study investigates the decade-long history (2011–2021) of the promotion of techno-scientific biomedical communication by pro-government non-profit organizations in Russia. In 2021, the World Bank pointed out that digital technologies have played a critical role in the pandemic in Russia and Russia's economic recovery is gathering pace. Thus, Russia tends to correspond to many other emerging health markets. We compare texts from non-profit organizations' websites and Facebook accounts with official state actions and the national mass media health agenda. The instruments that were used for text mining were topic modeling with latent Dirichlet allocation and an algorithm for keyphrase relationship visualization. The results revealed that promotion started no later than 2011 as a general private initiative in health. Throughout the state reforms and geopolitical crisis of 2014, in 2017–2019 digitalization was finally chosen as a model for the healthcare system. Simultaneously, the idea of digital reform for the health system was disseminated by the mass media. The pandemic drew attention to this communication, but it did not create significant change. Pro-government non-profit organizations continue to promote new directions for biomedical communication in Russia.
Automatic Recognition Method of Fall Movement of Sports Fitness Human Body Based on Posture Data Sequence

In order to improve the recognition accuracy of human falling actions, the impact of randomness of actions is reduced. To this end, this paper proposes an automatic recognition method for physical fitness human fall based on pose data sequence. The color camera is used to collect the fall motion images of the physical fitness personnel, and the motion image preprocessing is completed by extracting the fall motion features of the human body, tracking and adjusting the fall motion of the human body. A model of human body fall movement displacement feature extraction from posture data sequence is constructed. By tracking the displacement feature points, the automatic recognition of physical fitness human body fall movement based on posture data sequence is realized. The experimental results confirm that the proposed method can effectively obtain the details of the fall motion images of physical fitness. When the number of human falling actions reaches 500, the accuracy of action recognition is also as high as 77%, improving the recognition effect of human fall action.
Automatic Recognition for Fat Reduction Motion Image Landmark Using Computer Vision

To solve the problems existing in the traditional algorithm, such as image quality, the ability to predict the coordinate of the motion fat reduction image, the Intersection over Union (IOU) of the estimated coordinate of the landmark, the peak signal-to-noise ratio (PSNR) of the image, and the recognition accuracy of the image landmark, an automatic recognition algorithm for fat reduction motion image landmark using computer vision is proposed. The OV7725 camera is used to obtain the fat reduction motion image, and the histogram equalization method is used to enhance the fat reduction motion image. After that, the estimated coordinates of the landmark are obtained by the automatic recognition model of the landmarks of the fat reduction motion image. According to the coordinates of the landmark, after using a field programmable gate array to segment the image of the fat reduction motion image, the incremental updated freeman chain code extracts the contour of the landmark of the fat reduction motion image through 8-neighborhood retrieval, so as to realize the automatic recognition of landmark. The results show that the fat reduction motion image enhanced by the algorithm is clear and the contrast is obvious. The ability to predict the coordinates of the landmark of the fat reduction image is strong. The IOU of the estimated landmark coordinates is about 0.96. The processed image has a high PSNR, which can effectively recognize the landmark of the fat reduction motion image, and the recognition accuracy is about 97%, which has certain value in the field of image recognition and can improve the fat reduction movement normality.
Visual Question Answering for Intelligent Interaction

With the application of deep learning method in the field of image processing, the image-related intelligent interaction technology has also been rapidly developed. Visual question answering (VQA) collects the image information by asking questions related to the image and ultimately achieves the purpose for enriching the image understanding. Vision and language are the two core parts of human intelligence to understand the real world, and also the basic components to realize artificial intelligence, and a lot of research has been carried out in their respective fields. With the continuous promotion and application of deep learning in the fields of computer vision and natural language processing, visual question answering technology across the visual field and natural language disciplines has become a research hotspot in recent years. Visual question answering (VQA) for intelligent interaction collects image information by asking relevant questions to the content of the image and finally achieves the purpose of enriching image understanding. At the same time, as an emerging research direction, the challenges faced by the visual question answering system are huge, and we need to learn and excavate. Through the comprehensive comparison and analysis of the existing models and methods of visual question answering, this paper summarizes the shortcomings and development directions of the current research work and analyzes several models of visual question answering technology for the processing of image input and question input of the visual question answering model. The working principle of the model and the common public data set of the model: it is concluded that extending the structured knowledge base and applying mature technologies such as text question answering and natural language processing to deal with VQA problems are the future development directions of the VQA model.
Decision making model on the choice of crops for cultivation in uncertain weather conditions based on game theory

The article discusses an approach to planning crop areas in order to increase profits in the face of uncertain weather conditions by making decisions based on a mathematical model of game theory. Game theory deals with decision making in conflict situations between opposing players. In this situation, "nature" can play the role of the opposing side In this case, nature is not an opposing side of an antagonistic nature, but due to its unpredictability, natural phenomena can cause no less harm.
The Khirbet Qeiyafa Ostracon: A New Collation Based on the Multispectral Images, with Translation and Commentary

This paper provides a new collation of the Khirbet Qeiyafa ostracon on the basis of the multispectral images produced by Gregory Bearman and William Christens-Barry in collaboration with a number of imaging labs in 2009. It proposes 59 letters in total, with 12 new readings, including 5 new partial reconstructions, all supported by an analysis of these detailed, high-definition images. This new collation permits a renewed attempt at translation of the ostracon, which is interpreted as a record of the summons of a defendant in a legal dispute. In the course of analysis, it is argued that if the decipherment is accepted, the language of the text appears to be an archaic, dialectal form of Hebrew.
Selection Strategy of Mining Pool under Various Different Payment Mechanisms

Blockchain is becoming increasingly popular and has received extensive attention in various fields. In a proof-of-work-based blockchain, miners usually choose to join a mining pool for mining to gain revenue. Different mining pools may use other payment mechanisms, and each miner can earn different revenues in different pools. There are currently four common payment mechanisms used by mining pools to distribute mining revenue, namely, PPS, PPLNS, PPS+, and FPPS, and there are no relevant research results on the selection strategies of these four payment mechanisms. To this end, this paper models the pool selection problem as a risky decision problem and proposes the selection strategies of these four mining pool payment mechanisms. Firstly, miners’ income under the four payment mechanisms is given; then, a mining pool selection strategy based on the change of computing power is constructed based on the Laplace criterion; finally, the proposed strategy is verified and analyzed by simulation. The experiments show that the proposed mining pool selection strategy is effective. The results of this paper can provide an essential reference for miners when making pool selection decisions.
Handling irregularly sampled signals with gated temporal convolutional networks

We investigate the sequential modeling problem and introduce a novel gating mechanism into the temporal convolutional network architectures. In particular, we introduce the gated temporal convolutional network architecture with elaborately tailored gating mechanisms. In our implementation, we alter the way in which the gradients flow and avoid the vanishing or exploding gradient and the dead ReLU problems. The proposed GTCN architecture is able to model the irregularly sampled sequences as well. In our experiments, we show that the basic GTCN architecture is superior to the generic TCN architectures in various benchmark tasks requiring the modeling of long-term dependencies and irregular sampling intervals. Moreover, we achieve the state-of-the-art results on the permuted sequential MNIST and the sequential CIFAR10 benchmarks with the basic structure.
Parallel Corpora in ELT

This chapter reviews research on the use of parallel corpora to support English language teaching and learning to speakers of other languages. A parallel corpus is a corpus that contains a collection of original texts translated into one or more other languages in an aligned fashion so that corresponding segments, usually sentences or paragraphs, are visually matched. Drawing upon the technical advances produced in high-stake multilingual settings such as the European Parliament, multilingual corpora have contributed notably to human and machine translation. As highlighted in a review of the literature, these corpora are now available in a broad range of genres and languages, surpassing even differences of alphabets or characters. We discuss how this availability of tools and languages define factors of core issues. We focus on language teaching and learning, including three case studies illustrating the interest of parallel corpora: multilingual resources, false cognates, and complex phenomena. One caveat related to the use of parallel corpora is the possible over-normalisation or simplification found in translated texts. This chapter concludes with some reflections on technological advances, notably stemming from artificial intelligence, which will continue to influence the possibilities of parallel corpora for language teaching.
DDL for Younger Learners

Advances in the availability, functionality, and user-friendliness of language corpora have resulted in a concomitant rise in the number of “data-driven learning” (DDL) studies focusing on younger learners (YLs) in recent years, with generally positive qualitative and even quantitative results now finally reported in the literature. However, there are still a number of barriers to the implementation and integration of DDL in the primary and secondary classroom, mainly involving the lack of “pedagogic processing” of existing (mainly adult-focused) materials, a lack of software for specific use with YLs, and a lack of corpus literacy reported for pre-/in-service teacher trainees of YLs that often leads to a reluctance to adopt DDL pedagogy. This chapter discusses the affordances of direct, hands-on corpus use by pre-tertiary (language) learners for the purposes of DDL, while also outlining the challenges involved in the successful implementation of DDL with younger learners. I summarise the successes (and occasional failures) of DDL with YLs to date, while reflecting on strategies to increase the adoption of DDL for YLs in future research.
Impact of Technological Burden on Knowledge Management Functions in Jordanian Industrial Companies

The goal of this study is to see how electronic information overload affects knowledge management functions in Jordanian businesses. All Jordanian industrial enterprises registered on the Amman Stock Exchange were included in the study’s sample. Three hundred and seventy-three people were chosen at random from a simple random sample of 30% of the study population of 1242 senior and intermediate managers in the research community. Following the retrieval of the surveys, 206 questionnaires were found to be valid for analysis. It was used to do descriptive and heuristic statistical procedures, like simple and multiple regression analysis. The SPSS.16 application was used to do this. The study ends with the following findings: Electronic information overload (technological overload) has a statistically significant influence on knowledge management functions (acquisition, generation, transmission, exchange, and application) in Jordanian industrial companies. This work made a number of recommendations as a result of its findings, including: Adopting an organizational aspect that suits the nature of the tasks that industrial companies in Jordan perform, as well as providing technical capabilities to reduce the electronic information overload that these companies face while performing their tasks.
A flexible and lightweight privacy-preserving handshake protocol based on DTLShps for IoT

Anonymous authentication is widely studied for preserving the privacy of IoT devices . Although attribute-based signature (ABS) schemes can show identity attributes flexibly, letting the private key be generated by someone else is not safe. The computational overhead of traditional anonymous credential schemes based on zero-knowledge proofs (ZKP) is heavy for resource-constrained IoT devices. DTLShps, a lightweight handshake protocol based on software-defined networking (SDN), brings a new thought to verify certificates for resource-constrained IoT devices without anonymous functions. In this article, a lightweight privacy-preserving handshake protocol is designed based on DTLShps to flexibly present attributes of anonymous certificates for resource-constrained IoT devices. An authorization code is designed to represent the authorization status of each identity attribute defined in the X.509 standard. The IoT device cooperating with the CA can flexibly show arbitrary identity attributes to its peer by the authorization code. Only one signing operation of the elliptic curve digital signature algorithm (ECDSA) is needed to grant the authorization on the IoT device with the cooperation of the controller. The security of the proposed scheme is validated by the BAN logic and the tool Scyther. The performance evaluation shows that the computational delay on the IoT device and overall handshake delay is effectively reduced compared with the existing anonymous certificate schemes.
Arithmetic and Wavelet Based Information Security Using Image

The enhancement of information technology extensively changes the communication system in society. The information is rapidly shared through the internet or other electronic message delivery system without proper security measures. The IPR (Intellectual Property Right) of digital content or digital multimedia has to face the main challenge with the fear of distortion of the contents. Steganography and Cryptography are two widely used techniques for online digital contains authentication. Here we are using both of them together to make it more powerful with the help of simple Arithmetic and Binary operations and embed the same in the frequency domain using the wavelet function. Mainly target to provide a secure text information transmission with cryptex and stegano approach with high payload. The standard metric Peak Signal to Noise Ratio (PSNR) and MSE or Mean Squared Error measures the robustness of the proposed method.
Audio Steganography

Electronically communicating has increased in the covid scenario giving birth too numerous new styles to transmit information securely and robustly. So to achieve communication in secured way audio steganography can be used. Audio steganography is a method to hide text message in audio. In this paper, comparison between Least Significant bit (LSB) and phase encoding steganographic methods are carried out by implementing these techniques. Also the audio extraction features like amplitude envelope, Root mean squared energy, Zero crossing rate, Band energy ratio, Spectral centroid, Spectral bandwidth and mfccs.
On the Word Fragment Length for Unambiguous Reconstruction of a Periodic Word from a Complete Multiset of Fragments of Fixed Length

Abstract We consider the problem of reconstructing a word from a multiset of its fragments of fixed length. Words consist of symbols from a finite alphabet. The word to be reconstructed is assumed to be periodic or contain a periodic word as a subword. It is shown that a periodic word with a period p can be reconstructed from a multiset of its fragments of length k , where $$k \geqslant \left\lfloor {\frac{{16}}{7}\sqrt p } \right\rfloor + 5$$ . For a word consisting of a q -periodic prefix repeated m times and a p -periodic suffix repeated l times, if $$l \geqslant m{{q}^{{\left\lfloor {\frac{{16}}{7}\sqrt P } \right\rfloor + 5}}}$$ , then the estimate becomes $$k \geqslant \left\lfloor {\frac{{16}}{7}\sqrt P } \right\rfloor + 5$$ , where $$P = {\text{max(}}p,~q{\text{)}}$$ .
Reducing Exposure to Hateful Speech Online

It has been observed that regular exposure to hateful content online can reduce levels of empathy in individuals, as well as affect the mental health of targeted groups. Research shows that a significant number of young people fall victim to hateful speech online. Unfortunately, such content is often poorly controlled by online platforms, leaving users to mitigate the problem by themselves. It’s possible that Machine Learning and browser extensions could be used to identify hateful content and assist users in reducing their exposure to hate speech online. A proof-of-concept extension was developed for the Google Chrome web browser, using both a local word blocker and a cloud-based model, to explore how effective browser extensions could be in identifying and managing exposure to hateful speech online. The extension was evaluated by 124 participants regarding the usability and functionality of the extension, to gauge the feasibility of this approach. Users responded positively on the usability of the extension, as well as giving feedback regarding where the proof-of-concept could be improved. The research demonstrates the potential for a browser extension aimed at average users to reduce individuals’ exposure to hateful speech online, using both word blocking and cloud-based Machine Learning techniques.
Fast Decoding of AG Codes

We present an efficient list decoding algorithm in the style of Guruswami-Sudan for algebraic geometry codes. Our decoder can decode any such code using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\tilde{\mathcal {O}} (s\ell ^{\omega }\mu ^{\omega -1}(n+g))$ </tex-math></inline-formula> operations in the underlying finite field, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the code length, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$g$ </tex-math></inline-formula> is the genus of the function field used to construct the code, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s$ </tex-math></inline-formula> is the multiplicity parameter, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> is the designed list size and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> is the smallest positive element in the Weierstrass semigroup at some chosen place; the “soft-O” notation <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\tilde{\mathcal {O}} (\cdot)$ </tex-math></inline-formula> is similar to the “big-O” notation <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(\cdot)$ </tex-math></inline-formula> , but ignores logarithmic factors. For the interpolation step, which constitutes the computational bottleneck of our approach, we use known algorithms for univariate polynomial matrices, while the root-finding step is solved using existing algorithms for root-finding over univariate power series.