Reviewerly/exHarmony huggingface dataset:
Attention Network with Information Distillation for Super-Resolution

Resolution is an intuitive assessment for the visual quality of images, which is limited by physical devices. Recently, image super-resolution (SR) models based on deep convolutional neural networks (CNNs) have made significant progress. However, most existing SR models require high computational costs with network depth, hindering practical application. In addition, these models treat intermediate features equally and rarely explore the discriminative capacity hidden in their abundant features. To tackle these issues, we propose an attention network with information distillation(AIDN) for efficient and accurate image super-resolution, which adaptively modulates the feature responses by modeling the interactions between channel dimension and spatial features. Specifically, gated channel transformation (GCT) is introduced to gather global contextual information among different channels to modulate intermediate high-level features. Moreover, a recalibrated attention module (RAM) is proposed to rescale these feature responses, and RAM concentrates the essential contents around spatial locations. Benefiting from the gated channel transformation and spatial information masks working jointly, our proposed AIDN can obtain a more powerful ability to identify information. It effectively improves computational efficiency while improving reconstruction accuracy. Comprehensive quantitative and qualitative evaluations demonstrate that our AIDN outperforms state-of-the-art models in terms of reconstruction performance and visual quality.
Implementation of Language Appropriateness in Intercultural Communication Based on Distributed Control System

In recent years, with the increasingly close international communication, the importance of intercultural communication language has become more and more important. Proficiently mastering the appropriateness of intercultural communication language is an important part of national etiquette, which can represent the self-cultivation of a country. However, in reality, many students cannot master language appropriateness very well. Therefore, it is necessary to retrieve and classify intercultural communication languages based on distributed control systems, so as to improve language appropriateness and national comprehensive strength. This study proposed a language processing method based on distributed computing, which solved the problems of a large amount of computation and low accuracy in traditional language processing methods and simply designed a distributed control system, which could process language for intercultural communication. The results of this study showed that the language appropriateness score of the respondents was 53 points before the system developed in this study was used. After using the system developed in this study to retrieve and correct the language errors, the language appropriateness score of the respondents was 95 points, which was 42 points higher and also showed that the system developed in this study could effectively improve Language appropriateness.
Efficient Visual Tracking Using Local Information Patch Attention Free Transformer

The state-of-the-art (SOTA) transformer tracker TransT achieves high tracking accuracy. Nevertheless, the time and space complexity of its attention operation is quadratic to the spatial dimension of feature vectors. Thus it is difficult to deploy TransT on resource constrained devices. This paper proposes Local Information Patch Attention Free Transformer (LIP-AFT) based Local Information Patch Self-Attention Free Transformer (LIPS-AFT) and Local Information Patch Cross-Attention Free Transformer (LIPC-AFT) for linear time and space complexity and high accuracy. LIP-AFT benefits from global connectivity between patches while it focuses on naïve strong local attention patterns. The proposed tracker outperforms both SOTA trackers and TransT with various SOTA attention algorithms on accuracy and complexity. Moreover, its inference phase runs at 41 fps on RTX 2070S GPUs.
Novel Authentication Protocols Based on Quadratic Diophantine Equations

The Diophantine equation is a strong research domain in number theory with extensive cryptography applications. The goal of this paper is to describe certain geometric properties of positive integral solutions of the quadratic Diophantine equation x12+x22=y12+y22(x1,x2,y1,y2&gt;0), as well as their use in communication protocols. Given one pair (x1,y1), finding another pair (x2,y2) satisfying x12+x22=y12+y22 is a challenge. A novel secure authentication mechanism based on the positive integral solutions of the quadratic Diophantine which can be employed in the generation of one-time passwords or e-tokens for cryptography applications is presented. Further, the constructive cost models are applied to predict the initial effort and cost of the proposed authentication schemes.
Explainable AI: introducing trust and comprehensibility to AI engineering

Abstract Machine learning (ML) rapidly gains increasing interest due to the continuous improvements in performance. ML is used in many different applications to support human users. The representational power of ML models allows solving difficult tasks, while making them impossible to be understood by humans. This provides room for possible errors and limits the full potential of ML, as it cannot be applied in critical environments. In this paper, we propose employing Explainable AI (xAI) for both model and data set refinement, in order to introduce trust and comprehensibility. Model refinement utilizes xAI for providing insights to inner workings of an ML model, for identifying limitations and for deriving potential improvements. Similarly, xAI is used in data set refinement to detect and resolve problems of the training data.
Decentralized Application (DApp) for Microfinance Using a Blockchain Network

Blockchain is a distributed ledger for keeping an everlasting and immutable log of information about various transactions. It acts as a decentralized database and is run by processors which are part of a peer-to-peer (P2P) network. A smart contract is an agreement stored as a computer code which would be triggered inevitably when positive settings are encountered by possibly avoiding reliable third-party establishments. With blockchain technology, smart contracts could be decentralized, so that they are fair and trust less and are not controlled by one central institution. By way of consensus mechanism which requires agreement from more than 50% of the peer nodes, it is nearly impossible to hack a blockchain network. Blockchain in microfinance eradicates the need for trusted authorities, increases the speed of KYC process using digital identity creation and increases transparency. The objective of this work is to study various types of block chain networks to implement a DApp for microfinance. Microfinance usually refers to minor amounts of credit for small scale businesses and is intended for excluded customers who are unable to avail conventional sources of capital. This sector faces the challenge of increased interest rates, diminished service availability, sluggish KYC procedure and disproportionate transaction charges. The intent is to enable farmers directly procure a loan from their prospective customers. The customer in turn will get benefitted by getting the produce at a reduced rate from the farmer.
Design of a Web-Based Platform: Event-Venues Booking and Management System

Conducting an event require planning multiple tasks and managing finance for the event. Nevertheless, event management and event organization are more of a manual-based works and require a more automated system to produce more efficient and reliable reservations. This research aims to improve the event organization and management by promoting people to find, book a venue, and track the budget expenses through a web-based platform, Event-Venues Booking and Management System, by implementing cloud computing. After the data from a survey and an interview have been obtained and presented, the suggested system features are created based on the gathered results that would meet the needs of the future user. The findings suggest that the online platform assists individuals in arranging and managing their events in a more manageable and timely manner, while lowering the cost and time associated with it. Furthermore, the established system helps both users and suppliers to foster a positive connection between them, as determined by the research questions and objectives.
Performance Analysis of Various Asymmetric Public-Key Cryptosystem

Today, public-key cryptosystems have brought a significant change to the world of computers. Cryptosystems like the RSA and the ECC algorithm tend to top the table regarding usage and reliability. This study aims at meticulous and nuanced research on asymmetric public-key cryptosystems and compares them based on performance-based parameters and metrics. This research involves a comprehensive, comparative, and detailed study of the RSA, ElGamal, and the ECC-ElGamal Public-key Cryptosystems. Performance analysis has been performed considering the wall time, CPU, and memory used for both encryption and decryption of the algorithms. Security has also been measured by finding the similarity between plaintext and ciphertext obtained from these algorithms. The study aims to provide definitive results about the performance characteristics of the algorithms surveyed.
Real Time Facial Emotions Detection of Multiple Faces Using Deep Learning

Facial expression is a gesture developed with the facial muscles that communicates the individual's emotional expression. As Artificial Inteligence(AI) and Computer Vision technology advances, machines must learn to recognize human emotions in order to communicate effectively. Using the Deep Convolutional Neural Network (DCNN) technique, face expression are examined to detect emotion using deep learning. The proposed system is trained and evaluated in real time using the ADFES-BIV dataset. In the training phase, transfer learning technique is used to extract features from pictures as well as live video and a haar-cascade classifier is employed to detect the multiple faces in a frame. The Facial Action Coding System detects five universal emotions: happy, sad, neutral, surprise and anger and some micro emotions such as contempt, embarrass, fear, disgust and pride also can be detected by a person's face. It can recognize many facial emotions at the same time using deep learning. The system achieved a training accuracy of 81.67%.
Optimal Densely Connected Networks with Pyramid Spatial Matching Scheme for Visual Place Recognition

Visual place recognition (VPR) is the procedure of identifying a formerly visited place by the use of visual details under distinct appearance conditions and viewpoint changes. With the recent advances in imaging technologies, camera hardware, and deep learning (DL) models, the VPR becomes a hot research topic among computer vision and robotics communities. The VPR is found to be a challenging process, particularly in uncontrolled outdoor environments and over long duration owing to the environmental factors, light variations, and geometric aspects. With this motivation, this paper presents optimal densely connected network with spatial pyramid matching scheme for VPR, named ODNPSM-VPR technique. The ODNPSM-VPR technique mainly aims to achieve maximum detection performance under varying viewpoints and conditions. The VPR can be considered as an image retrieval process by the inclusion of two stages namely retrieval of closest candidate images and re-ranking. The ODNPSM-VPR technique employs DenseNet-121 model as a feature extractor and the hyperparameters of the DenseNet-121 model are tuned by the grasshopper optimization algorithm (GOA). Besides, image filtering database (IFD) with Manhattan distance is used for the retrieval of top candidate images and pyramid spatial matching scheme is employed for comparing the query image with the candidate images to recognize the place. In order to demonstrate the performance of the proposed ODNPSM-VPR technique, a series of simulations were carried out. The experimental results reported the promising performance of the ODNPSM-VPR technique interms of different measures.
The relational ontology of mobile touchscreens and the body: Ambient proprioception and risk during COVID-19

In this article, we explore the tension between the significance of touch as a vital sensory modality of human experience and how, with the ongoing coronavirus pandemic, proximity and (tactile) intimacy with other bodies in urban and domestic spaces becomes fraught with the risk of viral contagion. Informed by haptic media studies, the corporeal or sensory turn in contemporary theory, and phenomenology-informed mobile media studies, we examine the possible impacts for mobile device use of the risks of viral contagion associated with our routinized uses of haptic interfaces. We also examine the role and possibility of mobile haptics and the touchscreen in these contexts, and our capacity-via embodied and material metaphor-to extend corporeal reach through the mobile interface. Our contention is that, while the "stand in" for touch that mobile media offers may be perpetually incomplete, the "as-if" structure of habitual experience can play a significant role in narrowing the sensorial gap.
Solar tracking: The best alternative to obtain more solar power output

By conventional solar panels, only 15–22 % of incoming solar energy is converted to electric energy. Therefore, to achieve a stable output, a programmed system is required that can constantly gyrate the solar panel. The Solar Ray Tracking Device was created to address the issue stated above. This system is entirely automated, as it gyrates the panel to face the sun till it’s visible and takes Sun as a reference source rather than the earth, with its sensing elements continually following the solar rays and rotating the panel to achieve the maximum outcome. Photovoltaic panels are a packed, interconnected arrangement of PV cells that have been kept in an optimum position at 90°with respect to the incoming solar rays to optimize the power outcome. The dual-axis mechanism is an effective technique to enhance the efficiency of solar cells which was developed to track the Sun’s movement. Solar radiation and PV parameters were studied using characterization techniques such as spectral analysis, PV panel analysis, and LDR sensors. Spectral analysis reveals a good amount of current and solar output power at 435 nm wavelength, which helps in the material selection for PV panels. PV parameter was observed and checked, which shows the maximum solar energy that can be received by a given panel. Solar tracking at a small scale is tested, but in reality, large-scale testing can still be done in the future.
COIN: Correlation Index-Based Similarity Measure for Clustering Categorical Data

In this paper, a correlation index-based clustering algorithm (COIN) is proposed for clustering the categorical data. The proposed algorithm was tested on nine datasets gathered from the University of California at Irvine (UCI) repository. The experiments were made in two ways, one by specifying the number of clusters and another without specifying the number of clusters. The proposed COIN algorithm is compared with five existing categorical clustering algorithms such as Mean Gain Ratio (MGR), Min–Min-Roughness (MMR), COOLCAT, K-ANMI, and G-ANMI. The result analysis clearly reports that COIN outperforms other algorithms. It produced better accuracies for eight datasets (88.89%) and slightly lower accuracy for one dataset (11%) when compared individually with MMR, K-ANMI, and MGR algorithms. It produced better accuracies for all nine datasets (100%) when it is compared with G-ANMI and COOLCAT algorithms. When COIN was executed without specifying the number of clusters, it outperformed MGR for 88.89% of the test instances and produced lower accuracy for 11% of the test instances.
A single‐model multi‐task method for face recognition and face attribute recognition in internet of things and visual computing

Abstract The use of the internet of things (IoT) is steadily increasing in a wide range of applications. Integration of IoT, computer vision, and artificial intelligence can improve people's daily life in various domains such as smart homes, smart cities, and smart industries. There are a large number of face recognition and face attribute recognition scenarios in reality, and the industry commonly decomposes these tasks, with three models responsible for handling face detection, face recognition, and face attribute recognition. The multi‐model approach requires a lot of computational resources for context switching, while training one model with one dataset is not only complex, but also leads to overfitting of the multi‐model approach. The authors propose a single‐model multi‐task approach, which can complete all tasks using only one model, and thus obtains a great improvement in inference speed, especially in scenes with high face density. After an experimental comparison, our approach saves a maximum of 96% of inference time, 49.5% of memory usage, and 59.7% of CPU time, avoids frequent context switching, and simplifies the training steps while improving the generalization performance of the model.
CycleGAN with DenseU-Net for Medical Image Synthesis

Multi-modality images, such as magnetic resonance (MR) images and computed tomography (CT) images, provide more detailed information in radiotherapy. However, CT images, which are basis for dose calculation in radiotherapy planning, are sometimes unavailable due to various reasons, such as high cost, ionizing radiation, and so on. To synthesize higher quality CT images from MR images, we proposed an improved cycle generative adversarial network (CycleGAN) with densely connected convolutional network (DenseNet) and U-Net. It used DenseNets to reuse feature map and optimize information flow and used U- Nets to get multi-scale fusion information. It not only alleviated the disappearance of input information and the vanishing of gradient information, but also synthesized more credible CT images. Compared with the original CycleGAN method, the proposed method was 5.12% less on MAE, 0.7% greater on SSIM and 1.29% greater on PSNR, trained and tested on a dataset of 18 patients. Compared with state-of-the-art methods, including the DCNN, the atlas-based, the pix2pix, the DualGAN and the DiscoGAN methods, its relative error of MAE was 1.901%, 2.384%, 0.295%, 2.16% and 1.91% less, respectively. The proposed method synthesizes higher quality CT images owing to the advantages of deep learning, better meets the requirements of radiotherapy treatment based on MR images.
Photovoltaic Power Prediction Under Insufficient Historical Data Based on Dendrite Network and Coupled Information Analysis

In order to achieve reliable ultra-short-term photovoltaic (PV) power generation prediction for new-built PV plants or technologically modified PV plants, a prediction approach based on coupled information analysis and a dendrite network is presented in this paper. Firstly, by analyzing the information between strongly coupled variables, an improved Hampel filter is presented to improve the accuracy of the abnormal data processing. In addition, a dendritic network modeling method is introduced for PV power prediction. This model is able to predict the ultra-short-term power generation under the condition of insufficient historical data, and it has the benefits of a low requirement for the amount of training data and a simple structure. Forty different sets of tests were conducted according to different weather conditions and data conditions. Experimental results indicate that the presented approach can achieve higher predictive performance and stability compared with benchmark models.
Ensemble method using real images, metadata and synthetic images for control of class imbalance in classification

Binary classification and anomaly detection face the problem of class imbalance in data sets. The contribution of this paper is to provide an ensemble model that improves image binary classification by reducing the class imbalance between the minority and majority classes in a data set. The ensemble model is a classifier of real images, synthetic images, and metadata associated with the real images. First, we apply a generative model to synthesize images of the minority class from the real image data set. Secondly, we train the ensemble model jointly with synthesized images of the minority class, real images, and metadata. Finally, we evaluate the model performance using a sensitivity metric to observe the difference in classification resulting from the adjustment of class imbalance. Improving the imbalance of the minority class by adding half the size of the majority class we observe an improvement in the classifier’s sensitivity by 12% and 24% for the benchmark pre-trained models of RESNET50 and DENSENet121 respectively.
Sustaining Formal and Informal English Language Learning through Social Networking Sites (SNS): A Systematic Review (2018–2022)

There has been a gradual shift over the years towards the use of social networking sites (SNS) in formal and informal English language learning which was accelerated during the COVID-19 pandemic. Though there is an abundance of articles dedicated to probing into the in-depth use of SNS in English language learning, a clear correlation between the formal and informal application of SNS in English language learning is still scarce. Therefore, this systematic review aims to exhaustively analyse the recent findings regarding the integration of SNS in English language learning in both formal and informal learning contexts. Two databases were employed, which are the Web of Science (WoS) and Educational Resources Information Centre (ERIC) and thirty articles were extracted for further review. These articles were selectively restricted to a five-year (2018–2022) range and have been screened for any contradiction against the research objectives. As an overview, SNS is favoured for different kinds of applications in teaching and learning purposes due to observed improvements in overall language skills, social interactions, motivation and flexibility. It is proposed for future researchers to focus on a specific target group as well as specific SNS platforms which could help the researchers to minimize discrepancies.
Image Enhancement

Image enhancement is the process of altering the appearance of an image for improved visibility of features of interest or to facilitate subsequent image analysis such as the measurement or classification of objects. While the visibility of selected features can be improved, the inherent information content cannot be increased. The design of an image enhancement algorithm should consider both the specific features of interest and the imaging process itself. In microscope imaging, the images are often acquired at different focal planes, different time intervals, and in different spectral channels. The design of an enhancement algorithm should take full advantage of this multidimensional information. Image enhancement can be done in the spatial domain by manipulating the gray scale globally or in local neighborhoods of pixels. It can be done in a transform domain by manipulating transform coefficients. Fourier transform domain methods include low-pass, band-pass, and high-pass filtering, deconvolution, and Wiener filtering. Wavelet transform methods include coefficient thresholding and multiscale procedures. Methods used to enhance grayscale images are easily generalized to operate on color images.