Reviewerly/exHarmony huggingface dataset:
Social Recommendation Based on Multi-Auxiliary Information Constrastive Learning

Social recommendation can effectively alleviate the problems of data sparseness and the cold start of recommendation systems, attracting widespread attention from researchers and industry. Current social recommendation models use social relations to alleviate the problem of data sparsity and improve recommendation performance. Although self-supervised learning based on user–item interaction can enhance the performance of such models, multi-auxiliary information is neglected in the learning process. Therefore, we propose a model based on self-supervision and multi-auxiliary information using multi-auxiliary information, such as user social relationships and item association relationships, to make recommendations. Specifically, the user social relationship and item association relationship are combined to form a multi-auxiliary information graph. The user–item interaction relationship is also integrated into the same heterogeneous graph so that multiple pieces of information can be spread in the same graph. In addition, we utilize the graph convolution method to learn user and item embeddings, whereby the user embeddings reflect both user–item interaction and user social relationships, and the item embeddings reflect user–item interaction and item association relationships. We also design multi-view self-supervising auxiliary tasks based on the constructed multi-auxiliary views. Signals generated by self-supervised auxiliary tasks can alleviate the problem of data sparsity, further improving user/item embedding quality and recommendation performance. Extensive experiments on two public datasets verify the superiority of the proposed model.
Flower pollination student psychology optimization-integrated context deep learning and probabilistic-based fusion for image inpainting

Image inpainting is the process to fill missing pixels in the damaged image and this process has drawn more attraction and gained active and expensive research topic in recent decades, because the high quality in the image inpainting benefits a greater range of applications, like object removal, photo restoration, and so on. Inpainting of larger quality of the image needs to fill the empty regions with plausible content in the damaged image. The existing inpainting methods either fill image regions by stealing the image patches or semantically create coherent patches from the regional context. Most of the traditional models perform well on small holes images, but restoring the image with large holes still results a challenging task. To overcome such issues and to generate effective inpainting results, a proposed method named the hybrid context deep learning approach is designed in order to fill empty regions of crack images. Moreover, the proposed method is more effective by employing a hybrid optimization algorithm for training of classifier to generate a more robust and accurate inpainted result. The developed model includes two different deep learning classifiers to accomplish the process of image inpainting in such a way that the results are fused through the probabilistic model. Moreover, the proposed approach attains higher performance by the metrics such as Peak signal-to-noise ratio (PSNR), Structural Similarity Index (SSIM), Second Derivative like Measure of Enhancement (SDME), and Universal Quality Index (UQI) with the values of 38.02[Formula: see text]db, 0.867, 54.32[Formula: see text]db, and 0.864, respectively.
Aquila Optimization with Transfer Learning Based Crowd Density Analysis for Sustainable Smart Cities

Video surveillance in smart cities provides efficient city operations, safer communities, and improved municipal services. Object detection is a computer vision-based technology, which is utilized for detecting instances of semantic objects of a specific class in digital videos and images. Crowd density analysis is a widely used application of object detection, while crowd density classification techniques face complications such as inter-scene deviations, non-uniform density, intra-scene deviations and occlusion. The convolution neural network (CNN) model is advantageous. This study presents Aquila Optimization with Transfer Learning based Crowd Density Analysis for Sustainable Smart Cities (AOTL-CDA3S). The presented AOTL-CDA3S technique aims to identify different kinds of crowd densities in the smart cities. For accomplishing this, the proposed AOTL-CDA3S model initially applies a weighted average filter (WAF) technique for improving the quality of the input frames. Next, the AOTL-CDA3S technique employs an AO algorithm with the SqueezeNet model for feature extraction. Finally, to classify crowd densities, an extreme gradient boosting (XGBoost) classification model is used. The experimental validation of the AOTL-CDA3S approach is tested by means of benchmark crowd datasets and the results are examined under distinct metrics. This study reports the improvements of the AOTL-CDA3S model over recent state of the art methods.
An Efficient Task Scheduling Based on Hybrid Bird Swarm Flow Directional Model in Cloud Computing Environment

The cloud computing has developed a vital service in information technology (IT). It assures resource pooling as well as offers services on-demand around the network. The efficient task scheduling and the balanced task distribution become the major challenging problem in the cloud computing system because of the active heterogeneous nature of resources as well as the tasks. The resources are unstable in nature whenever a large number of resources are requested for completing the tasks. The main part of this issue is to design the effective intelligent searching arrangement for scheduling the tasks in suitable virtual machines and how VM schedules the task in the efficient way. In this article, an effective method using MAP reduces structure and HBSFD for the effective task scheduling in the provided cloud. First, from the client’s task, task features are extracted. Later, these extracted features are chosen by the feature choice using the adjusted rand index and the standard deviation ratio (FSASR) method. Then, the larger tasks are divided to smaller subtasks using the map-reduce structure. Finally, the tasks are effectively scheduled with the help of the hybrid bird swarm-based flow directional algorithm (HBSFD). The experimental evaluations are conducted on the platform of cloudsim, and the experimental outcomes demonstrates that the proposed HBSFD technique performs better than other state-of-art approaches with respect to measures such as average turnaround time and processing time.
Biomedical named entity recognition with the combined feature attention and fully-shared multi-task learning

Biomedical named entity recognition (BioNER) is a basic and important task for biomedical text mining with the purpose of automatically recognizing and classifying biomedical entities. The performance of BioNER systems directly impacts downstream applications. Recently, deep neural networks, especially pre-trained language models, have made great progress for BioNER. However, because of the lack of high-quality and large-scale annotated data and relevant external knowledge, the capability of the BioNER system remains limited.In this paper, we propose a novel fully-shared multi-task learning model based on the pre-trained language model in biomedical domain, namely BioBERT, with a new attention module to integrate the auto-processed syntactic information for the BioNER task. We have conducted numerous experiments on seven benchmark BioNER datasets. The proposed best multi-task model obtains F1 score improvements of 1.03% on BC2GM, 0.91% on NCBI-disease, 0.81% on Linnaeus, 1.26% on JNLPBA, 0.82% on BC5CDR-Chemical, 0.87% on BC5CDR-Disease, and 1.10% on Species-800 compared to the single-task BioBERT model.The results demonstrate our model outperforms previous studies on all datasets. Further analysis and case studies are also provided to prove the importance of the proposed attention module and fully-shared multi-task learning method used in our model.
Distributing entanglement in first-generation discrete- and continuous-variable quantum repeaters

Quantum repeaters are used to overcome the exponential photon loss scaling that quantum states acquire as they are transmitted over long distances. While repeaters for discrete-variable encodings of quantum information have existed for some time, approaches for continuous-variable encoding quantum repeaters have only recently been proposed. In this work, we present a method of using a discrete-variable repeater protocol to distribute continuous-variable states and utilize it to compare the rates of continuous-variable entanglement distribution between first-generation continuous- and discrete-variable quantum repeaters. Such a comparison allows us to begin to benchmark the two quite different approaches.
Efficient Single-image Super-resolution Using Dual path Connections with Multiple scale Learning

Deep convolutional neural networks have been demonstrated to be effective for single-image super-resolution in recent years. On the one hand, residual connections and dense connections have been used widely to ease forward information and backward gradient flows to boost performance. However, current methods use residual connections and dense connections separately in most network layers in a sub-optimal way. On the other hand, although various networks and methods have been designed to improve computation efficiency, save parameters, or utilize training data of multiple scale factors for each other to boost performance, they either do super-resolution in high-resolution space to have a high computation cost or cannot share parameters between models of different scale factors to save parameters and inference time. To tackle these challenges, we propose an efficient single-image super-resolution network using dual path connections with multiple scale learning (EMSRDPN). By introducing dual path connections inspired by Dual path Networks into EMSRDPN, it uses residual connections and dense connections in an integrated way in most network layers. Dual path connections have the benefits of both reusing common features of residual connections and exploring new features of dense connections to learn a good representation for single-image super-resolution. To utilize the feature correlation of multiple scale factors, EMSRDPN shares all network units in low-resolution space between different scale factors to learn shared features and only uses a separate reconstruction unit for each scale factor, which can utilize training data of multiple scale factors to help each other to boost performance, meanwhile, which can save parameters and support shared inference for multiple scale factors to improve efficiency. Experiments show EMSRDPN achieves better performance and comparable or even better parameter and inference efficiency over state-of-the-art methods. Code will be available at https://github.com/yangbincheng/EMSRDPN .
Assessment of Learners’ Needs Regarding Learning Videos and Immersive Learning Environments

Background&#x0D; Learning videos are widely used in higher education and design principles are established. Immersive learning environments (e.g., augmented reality, virtual reality) are so far barely used for educational purposes, resulting in a lack of recommendations for their design. Therefore, when designing immersive learning environments, potential users should be involved from the beginning.&#x0D; Objective&#x0D; The objective of this paper was to explore learners’ needs and perspectives regarding learning videos and immersive learning environments, to examine their fit with established design principles, and to derive new design recommendations. Furthermore, we aimed to identify suitable application fields from learners’ perspectives.&#x0D; Methods&#x0D; In total, 14 current and former students participated in two online focus groups. They were asked about their usage habits for learning videos, their experience with immersive learning environments, and potential fields of useful implementation for immersive learning in their studies. Thematic analysis was used to identify relevant themes.&#x0D; Results&#x0D; Results confirm established principles (e.g. coherence, minimalism, pacing, guided activity); however, additional design recommendations were found (brevity, focus, merry, accessibility). Learning videos are mostly used for demanding and dry subjects, when a different approach is needed, or to catch up on lacking previous knowledge. Empathy, three-dimensional space, and tangibility were identified as suitable applications for immersive technologies.&#x0D; Conclusion&#x0D; Experience with immersive learning technologies is scarce among students, nonetheless they have ideas about how the design of immersive learning environments could contribute to their learning activities. The recommendations presented should be implemented and their effect on learning outcomes should be empirically tested.
To What Extent Is White-Collar Crime Understandable? An Empirical Study of Determinants Based on the Theory of Convenience

White-collar crimes (WCC) or financial crimes (FC) are the crimes that are linked with the top-level management representatives who are responsible to make the right decisions for the better functioning of the organization, these individuals are highly respectable as they hold a position of high repute. However, when the same individuals either under the pressure of performance or personal issues misuse the power and control vested on them due to the position they hold in the organization, take certain decisions which lead the organization into jeopardy. These individuals not only put the hard-earned money of the investors on stake but also enjoy the best of the facilities on the basis of their position. In the said article the authors have tried to answer two major questions regarding the abuse of position and manipulation of the financial statements at the struggling phase of the firm. The base of the study is convenience theory which was used to understand the concept of convenience theory for which a questionnaire based on 7-point Likert scale was used in which responses of 230 MBA students were studied. The results were near to what were expected supporting the fact that individuals are misusing their position to commit WCC, supporting the concept of convenience theory being used in the study.
Effective image splicing detection using deep neural network

Digital forgery is increasing day by day due to the increase in the usage of digital media files and the ease of editing media files. But digital media editing is a serious issue, as digital media are the primary source of evidence for criminal cases in the courtrooms. Consequently, there is an urge to detect and localize forgeries in digital media to aid digital forensics. The main aim of the proposed work is to detect forgeries in digital images. Image splicing forgery involves copying an image region from one image and pasting it into another image. A deep learning-based technique is proposed to detect image splicing forgery. A pre-trained deep Convolution Neural Network is transferred for the proposed application. The network is trained using spliced and original images to adapt it to the image splicing detection problem. The layers in the network are modified and fine-tuned to make it perform well for the new unseen dataset. This re-designed convolution network discriminates spliced and original images accurately. Also, the proposed work locates the spliced image regions if both the source and spliced images are provided as input image pairs. The proposed work is tested on CASIA and Columbia splicing image datasets and achieved good results.
When Brands Become Stans: Netflix, Originals, and Enacting a Fannish Persona on Instagram

While brand-run accounts of Twitter have attracted many a meme for their hit-and-miss attempts at relatable humor, on Instagram one type of brand account is quietly thriving: the Netflix Original series Instagram account. This paper studies brand personification on social media at a niche angle. By analyzing posts from three popular Netflix Original series Instagram accounts, we found that each account co-ops fan practices and enacts its own specific fannish persona. This paper reports on how and why Netflix enacts a fannish persona on its Original series Instagram accounts, to locate the personified Instagram account within the context of social media communication strategies. As the original advertisers of Instagram, there is established research on influencers which investigates the implications of people turning themselves into brands. What may call for more research in the future is the opposite: what are the consequences of brands turning themselves into people?
Client-Oriented Blind Quality Metric for High Dynamic Range Stereoscopic Omnidirectional Vision Systems

A high dynamic range (HDR) stereoscopic omnidirectional vision system can provide users with more realistic binocular and immersive perception, where the HDR stereoscopic omnidirectional image (HSOI) suffers distortions during its encoding and visualization, making its quality evaluation more challenging. To solve the problem, this paper proposes a client-oriented blind HSOI quality metric based on visual perception. The proposed metric mainly consists of a monocular perception module (MPM) and binocular perception module (BPM), which combine monocular/binocular, omnidirectional and HDR/tone-mapping perception. The MPM extracts features from three aspects: global color distortion, symmetric/asymmetric distortion and scene distortion. In the BPM, the binocular fusion map and binocular difference map are generated by joint image filtering. Then, brightness segmentation is performed on the binocular fusion image, and distinctive features are extracted on the segmented high/low/middle brightness regions. For the binocular difference map, natural scene statistical features are extracted by multi-coefficient derivative maps. Finally, feature screening is used to remove the redundancy between the extracted features. Experimental results on the HSOID database show that the proposed metric is generally better than the representative quality metric, and is more consistent with the subjective perception.
A longitudinal comparison of public libraries’ posting activities on Twitter in April of 3 years, pre-, during, and post-COVID-19

The purpose of the study was to observe how public libraries’ communication on Twitter has been changed before, during, and after the COVID-19 outbreak. A total of 40 active, public library Twitter accounts were used for data collection and analysis. The tweets examined were a combination of original tweets ( n = 2623) and retweets ( n = 666) posted from other Twitter accounts on the public libraries’ Twitter feeds. A content analysis scheme was used to analyze topical aspects of the tweets. The study found that public libraries were more active in communicating information on social media during the COVID-19 lockdown period. Promoting library events/programs, communicating library operations to patrons, and highlighting library resources for literacy are common in public libraries’ Twitter communication throughout the 3 years period. The study also observed strong associations between the content types of posts and the contextual aspects of the libraries, including an emergency situation such as the COVID-19 lockdown, the size of the population served by the library, and the state in which the library was located. In other words, the study provides evidence that public libraries use different communication strategies on Twitter depending on factors such as community emergencies, service population size, or geographic location. The results of this study illustrate that through social media usage, public libraries demonstrate their competence as public agencies in managing their services, as well as their commitment to the core value of information access and service provision to users, even in the face of unprecedented crises.
Performance Analysis of Data Sharing Using Blockchain Technology in IoT Security Issues

The Internet of Things (IoT) is advancing rapidly in research and industry, but it still has insurance and security flaws. On a fundamental level, common security and assurance approaches will be irrelevant for IoT due to its decentralized geology and the resource requirements of a significant portion of its contraptions. Blockchain (BC), which supports the digital currency Bitcoin, is being used to provide security and insurance via conveyed networks of IoT-related geologies. Regardless, BCs are computationally expensive but have real-world exchange speeds overhead as well as delays, making them unsuitable for IoT devices. In addition to IoT, this location article recommends another secure, one-of-a-kind, but portable design based on BC advancement that eliminates BC operating costs while retaining a significant portion of its security and higher efficiency. The depicted methodology is based on a brilliant internal implementation and is only a specialist relevant examination for a broader range of IoT devices. The proposed strategy is more even-handed and incorporates magnificent homes, an underlay association, and cloud stores coordinating BC information trades that can provide assurance and security. We use various types of BCs based on an organization's hierarchy of leadership when a trade occurs, and we also use flowed trust procedures instead of a distributed geology. The abstract evaluations of a design under standard threat models justify providing IoT device security and insurance.
Bayesian sparse joint dynamic topic model with flexible lead-lag order

Currently, text documents from multiple sources have become available in many fields. It is of great interest to study the relationship between documents from different sources and uncover the underlying causality. Zhu et al. (2021) proposed a joint dynamic topic model (JDTM). They classified all topics into three groups and used the “shared topics” with a fixed time lag order to characterize the shared information between two corpora. Although JDTM is a powerful tool for discovering the lead-lag relationship, there are two potential shortcomings. First, different shared topics should have distinct meanings, which should lead to different time lag orders between the two corpora. Second, for dynamic documents, not all topics are represented in each time slice, and thus topic sparsity should be considered. To address these two problems, we propose a sparse joint dynamic topic model (SJDTM) with a flexible lead-lag order. We assume a birth-and-death mechanism for all topics and a flexible lead-lag order for different shared topics. The performance of SJDTM is evaluated using both synthetic data and two real text corpora consisting of conference papers and journal papers.