Reviewerly/exHarmony huggingface dataset:
Reinforcement learning and blockchain‐based intelligent and secure vaccine recommender system

Abstract By combining blockchain technology (BT) and reinforcement learning (RL), the proposed work addresses the difficulties associated with vaccine recommendations. The need for individualized recommendations is obvious as vaccine schedules become more complicated and there are more vaccines available. The proposed work presents a novel approach that combines the adaptability of RL with the security, privacy, and transparency of BT. Layers for data processing, application, consensus, and smart contracts are included in the system architecture. It provides user‐managed secure access, individualized vaccine recommendations, and decentralized data storage. The system aims to improve public health outcomes by using smart contracts to automate procedures and RL to improve recommendations. Using 10‐fold cross‐validation on the Vaccine Adverse Event Reporting System (VAERS) dataset, the experimental study verifies the performance of the system. The study focuses on metrics like accuracy, sensitivity, specificity, and F‐measure when contrasting the proposed model with current solutions. The ability of proposed model to offer precise and well‐informed vaccine recommendations is demonstrated by its consistent outperformance of competitors in accuracy and sensitivity.
Repairing a Service Composition with Graph Embedding and Planning Graph in a Cloud-Edge Environment

In order to keep up with the ever-changing world of services, it is essential to fix a broken service composition in a cloud-edge environment when it fails or when users' requests change. However, current methods for replanning or repairing a service composition need to take a while to generate a new solution. To address this issue, we propose using a damaged planning graph repairing approach with graph embeddings to repair broken service compositions in the cloud-edge environment. To begin with, our approach employs graph embedding to acquire the low-dimensional vectors of service nodes within the service network graph. Secondly, we compute the candidate service set by utilizing the failed service, its parameters, and the user's initial input through graph embedding. Thirdly, we design an approach for repairing service compositions in a cloud-edge environment by relying on the candidate service set and the damaged planning graph. To verify the validity of the proposed approach, we analyze and compare the performances of the certain proposed method with referencing re-planning and repair methods, and the proposed method may repair a service composition within a shorter time.
Fashion-GPT: Integrating LLMs with Fashion Retrieval System

Customers on a fashion e-commerce platform although expressing their clothing preferences through combined imagery and textual information, they are limited to retrieve with single-round fixed inputs. At the same time, large language models (LLMs) have been gaining attention across various fields. ChatGPT is a remarkable example of an LLM, known for its user-friendly language interface, impressive conversational proficiency, and reasoning abilities. To this end, we propose Fashion-GPT, a system paradigm that integrates ChatGPT with a pool of AI models in the fashion domain to achieve a multi-round multi-modal search. Specifically, it enables the system to utilize the LLMs for understanding user queries, select retrieval models based on their function descriptions, execute each subtask with the selected fashion models, and leverage LLMs to summarize the response corresponding to the execution results.
Bitcoin and carbon dioxide emissions: Evidence from daily production decisions

Environmental externalities from cryptomining may be large, but have not been linked causally to mining incentives. We exploit daily variation in Bitcoin price as a natural experiment for an 86 megawatt waste coal-fired power plant with on-site cryptomining. We find that carbon emissions respond swiftly to mining incentives, with price elasticities of 0.69–0.71 in the short-run and 0.33–0.40 in the longer run. A $1 increase in Bitcoin price leads to $3.11–$6.79 in external damages from carbon emissions alone, well exceeding cryptomining's value added (using a $190 social cost of carbon, but ignoring increased local air pollution). As cryptomining requires ever more computing power to mine a given number of blocks, our study highlights both the revitalization of US fossil assets and the need for financial industry accounting to incorporate cryptomining externalities.
Power Minimization in Federated Learning with Over-the-air Aggregation and Receiver Beamforming

Combining over-the-air uplink transmission and multi-antenna beamforming can improve the efficiency of federated learning (FL). However, to mitigate the significant aggregation error due to communication noise and signal distortion, pre-processing of device signals and post-processing at the server are required. In this paper, we study the optimization of receiver beamforming and device transmit weights in over-the-air FL, to minimize the total transmit power in each communication round while guaranteeing the convergence of FL. We establish sufficient convergence conditions based on the analysis of gradient descent with error and formulate a power minimization problem. An alternating optimization approach is then employed to decompose the problem into tractable subproblems, and efficient solutions are developed for these subproblems. Our proposed method is evaluated through simulation on standard image classification tasks, demonstrating its effectiveness in achieving substantial reductions in transmit power compared with existing alternatives.
Objects guide human gaze behavior in dynamic real-world scenes

The complexity of natural scenes makes it challenging to experimentally study the mechanisms behind human gaze behavior when viewing dynamic environments. Historically, eye movements were believed to be driven primarily by space-based attention towards locations with salient features. Increasing evidence suggests, however, that visual attention does not select locations with high saliency but operates on attentional units given by the objects in the scene. We present a new computational framework to investigate the importance of objects for attentional guidance. This framework is designed to simulate realistic scanpaths for dynamic real-world scenes, including saccade timing and smooth pursuit behavior. Individual model components are based on psychophysically uncovered mechanisms of visual attention and saccadic decision-making. All mechanisms are implemented in a modular fashion with a small number of well-interpretable parameters. To systematically analyze the importance of objects in guiding gaze behavior, we implemented five different models within this framework: two purely spatial models, where one is based on low-level saliency and one on high-level saliency, two object-based models, with one incorporating low-level saliency for each object and the other one not using any saliency information, and a mixed model with object-based attention and selection but space-based inhibition of return. We optimized each model's parameters to reproduce the saccade amplitude and fixation duration distributions of human scanpaths using evolutionary algorithms. We compared model performance with respect to spatial and temporal fixation behavior, including the proportion of fixations exploring the background, as well as detecting, inspecting, and returning to objects. A model with object-based attention and inhibition, which uses saliency information to prioritize between objects for saccadic selection, leads to scanpath statistics with the highest similarity to the human data. This demonstrates that scanpath models benefit from object-based attention and selection, suggesting that object-level attentional units play an important role in guiding attentional processing.
Female Gendering of Artificial Intelligence in Travel: A Social Interaction Perspective

Implementing artificial intelligence enables the travel industry to improve efficiency, attract customers, and enhance their experiences. However, the typically biased social perceptions of humans are now applied to artificial intelligence, which has reinforced its female gendering in the industry. Nevertheless, the mechanism of its gendering process remains unclear. Guided by the gender theory, this study has explored how gendering is constructed through the social interaction perspective. Sixteen respondents who interacted with service robots or virtual assistants during their trip were recruited and interviewed via Zoom. Our findings suggest that the female gendering of artificial intelligence can be mapped from human-human to human-artificial intelligence interaction. Four main factors related to the social construction of the gendering process are identified. The findings contribute to understanding human-technology interaction and help tourism practitioners find a more cost-effective way to integrate human-technology collaboration strategies into their daily operations.
Impact of using sinogram domain data in the super‐resolution of CT images on diagnostic information

Abstract Background In recent times, deep‐learning‐based super‐resolution (DL‐SR) techniques for computed tomography (CT) images have shown outstanding results in terms of full‐reference image quality (FR‐IQ) metrics (e.g., root mean square error and structural similarity index metric), which assesses IQ by measuring its similarity to the high‐resolution (HR) image. In addition, IQ can be evaluated via task‐based IQ (Task‐IQ) metrics that evaluate the ability to perform specific tasks. Ironically, most proposed image domain‐based SR techniques are not possible to improve a Task‐IQ metric, which assesses the amount of information related to diagnosis. Purpose In the case of CT imaging systems, sinogram domain data can be utilized for SR techniques. Therefore, this study aims to investigate the impact of utilizing sinogram domain data on diagnostic information restoration ability. Methods We evaluated three DL‐SR techniques: using image domain data (Image‐SR), using sinogram domain data (Sinogram‐SR), and using sinogram as well as image domain data (Dual‐SR). For Task‐IQ evaluation, the Rayleigh discrimination task was used to evaluate diagnostic ability by focusing on the resolving power aspect, and an ideal observer (IO) can be used to perform the task. In this study, we used a convolutional neural network (CNN)‐based IO that approximates the IO performance. We compared the IO performances of the SR techniques according to the data domain to evaluate the discriminative information restoration ability. Results Overall, the low‐resolution (LR) and SR exhibit lower IO performances compared with that of HR owing to their degraded discriminative information when detector binning is used. Next, between the SR techniques, Image‐SR does not show superior IO performances compared to the LR image, but Sinogram‐SR and Dual‐SR show superior IO performances than the LR image. Furthermore, in Sinogram‐SR, we confirm that FR‐IQ and IO performance are positively correlated. These observations demonstrate that sinogram domain upsampling improves the representation ability for discriminative information in the image domain compared to the LR and Image‐SR. Conclusions Unlike Image‐SR, Sinogram‐SR can improve the amount of discriminative information present in the image domain. This demonstrates that to improve the amount of discriminative information on the resolving power aspect, it is necessary to employ sinogram domain processing.
The Essence of Pedagogical Interaction in the Educational Process of Higher Education

The chapter discusses the essence of pedagogical interactions as a complex subject-to-subject, socio-psycho-pedagogical concept, reflecting direct or indirect interconnected and mutually conditioned actions that are determined by the needs of subjects participating in the educational process in post-secondary school for enriching knowledge and experience, value and information exchange, carrying out joint duties and activities. Such actions are reflected upon by the actors of the educational process and directed towards facilitation and accomplishment of professional tasks. The chapter outlines the methodological basis for the emergence of pedagogical interaction as an independent category. The classification of scientific approaches to the definition of the essence of the concept of “pedagogical interaction” is given. The essence of “pedagogical interaction,” seen through the fundamental principles of education, is analyzed and discussed. The value-reflexive type of interaction in post-secondary school is analyzed and determined to be the most effective in targeting the humanistic nature of learning. Both the essence and critical features of the value-reflexive accompaniment of professional training allowed it to reveal its structural content, including cognitive, reflexive-active, and personality-value components. The research emphasizes that the orientation in social interaction to reflect the value content involves many features: (1) the definition of main vectors of value content, (2) the most fulsome components of social interaction in the field of professional activity (values, opportunities, morality, rules, methods, and means), and (3) the formation of a harmonious and value-based attitude to the nature of interaction within the educational team through the reflection of personal experience. Based on the theoretical analysis and empirical research, the authors argue that the reflexive value support of professional training is a solution to some issues in education.
Modern Approaches to the Creation of Virtual Museum Exhibits in the Educational Laboratory of 3D Modeling

The chapter discusses the features of creating virtual expositions using educational of the laboratories of 3D modeling and web technologies using the example of the virtual museum “The Soldiers of Victory” developed by students of Moscow State University of Technology and Management named after K.G. Razumovsky with the support of the Department of Education and Science of Moscow and the Federal Agency for Youth Affairs. The authors describe the leading technologies used to create virtual museums. Also, the focus is on the mechanisms for developing students' skills necessary for working with them through participation in the activities of the 3D modeling laboratory and web technologies. The digital transformation of the educational process forms new organizational structures in the university environment, the activities of which are aimed at improving the level of competence of information and communication technologies. The communicative strategy of modern educational tools contributes to the development of a new format for the student.
Specifics of Russian Certification Systems and Personnel Reserves of School Principals

The chapter presents the results of a sociological study of the professional characteristics of school principals in different constituent entities of Russia. Also, the authors conduct the monitoring of the status of existing Russian regional certification models, the appointment system, and the work on the formation of staff reserves for school principals. The regional specifics of the models for managing personnel processes for the appointment and certification of principals are identified. The state of personnel reservation systems for principals of educational organizations in the Russian regions and municipalities is analyzed. The directions for improving the systems for the certification, appointment, and formation of personnel reserves at various levels of the managerial vertical in the field of general education are determined.
Educational Process Variability

The relevance of the problem of high-school student academic mobility development is due to changes in modern education, caused by the processes of globalization, computerization, and digitalization. The purpose of the study was in theoretical justification and the pilot test effectiveness of the influence of professional teachers' competence on forming high school student academic mobility. The chapter presents experimental data justifying positive development dynamics of high-school student academic mobility. It is achieved under the condition of variability of educational process in high school.
Perceptions of University Educational Environments and Their Developmental Challenges

The chapter presents the results of a survey of students from a Russian regional university, conducted at the request of its administration in order to study students' satisfaction with the conditions and quality of education they receive, to clarify the attitude of students to the opportunities provided by the university and their activities outside the educational process at the university. The survey involved 1,636 people, which was about 35% of the total number of full-time students. According to the results of the study, it was found that students are mainly satisfied with the conditions and quality of training, arrangement, and opportunities of the educational environment of the university, relations with teachers and classmates. Furthermore, they positively assess the developmental effect of the education received. In contrast, about 12% of respondents said they were dissatisfied with the quality of education. The analysis shows that dissatisfaction with the learning conditions and the quality of education correlates with the low self-organization of this group of students.
Pseudo-labels Learning for Multi-source Weakly Supervised Salient Object Detection

Salient object detection based on weakly supervised learning has become an attractive direction because of high cost of pixel-level labels, but the information provided by a single weakly supervised source makes it difficult to train a well-performance model. In this paper, we propose a pseudo-labels learning method that is designed as a unified two-stage framework for multi-source weakly supervised salient object detection. In the first stage, we introduce a two-branch network to learn category labels and bounding boxes respectively. The first branch is the classification network to obtain class activation maps, and the other branch is based on bounding boxes training to generate salient bounding box attributed maps. Then, we propose an aggregation module, which fuses the maps generated by the above two branches and then refines them to get pixel-level pseudo-labels. In the second stage, we train the transformer model to predict salient maps using the final pseudo-labels as ground-truth. The proposed method is compared with existing methods on six datasets, and experimental results verify the effectiveness of our method.
Target Tracking Based on Fusion of Object Detection and Segmentation with a New Classifier

In order to improve the accuracy of target tracking in complex environment, this paper studies a scheme that can achieve high-precision target tracking. This paper first analyzes the shortcomings of the existing particle filter tracking framework and the traditional discriminant model based on the classification idea, proposes a new image classifier based on the Quantitative Theory Ⅰ, and introduces it into the particle filter framework to achieve a target tracking method that integrates target detection and segmentation. Experimental results show that the proposed algorithm has better tracking accuracy and robustness than other algorithms when target deformation, occlusion, background interference and scale change occur in the tracking scene.
Global Calibration of Virtual Multi-camera Vision System Based on 3D Target

Virtual multi-camera vision system (MVS) combining a single camera and catadioptric mirrors has been paid increasing attention for 3D measurement applications due to its wide field of view (FOV), low cost, and synchronization. Setups of the vision system require the global transformation relationships between the involved virtual cameras to be known. This work proposed a novel global calibration approach of virtual multi-camera vision system based on an unconstrained 3D target. Measurement model of the local virtual binocular structure and the global MVS as well as the corresponding parameter estimation methods are presented. Then the global parameters are refined by a non-linear optimization technique based on the reprojection matrix of adjacent local virtual cameras. Results of simulations validate the measurement model and the optimization method, showing that the proposed approach has the advantages of high accuracy, noise immunity and great flexibility.
ArtDiff: Artwork Generation via Conditional Diffusion Models

Artwork Generation is an important research area of computer vision. Recently, kinds of generative models have achieved great success in natural image generation. However, artwork generation has rarely been studied due to the unfixed structure of artworks. Combined with prevailing diffusion models, we propose a simple yet effective framework, named as ArtDiff, for artwork generation. Given a name of an artist, we can generate diverse and novel artworks which reflecting the style of the artist. To the best of our knowledge, ArtDiff is the first diffusion model that generate artworks with the guidance of artist's name. Experimental results demonstrate that ArtDiff is able to recognize the artist's preference and generate artworks with reasonable structures and fine-grained details.
Siamese Network for Object Tracking with Diffusion Model

Recently, Siamese networks have drawn great attention in object tracking community because of their balanced accuracy and speed. However, due to the scarcity of target samples in the training period, Siamese networks suffer from low adaptability to the variations of target appearance in realistic tracking scenarios. Therefore, Siamese network for object tracking with diffusion model is proposed. Based on the target template labeled with bounding box, the network generates more target samples with ADM-G, and compares the feature map of samples to the ones of images from the searching areas in the classic SiamRPN++ architecture. High quality and diversity samples are highly correlated with the object, which greatly enrich the object sample, and enhance the quality of the feature extraction. As demonstrated by experiments, the Diff-SiamRPN++ tracker is superior to the others on different benchmarks.
Joint Sparse and Manifold Learning with Low-rank Embedding for Feature Extraction

Feature extraction has been regarded as an effective dimensionality reduction method in many practical applications. In this paper, we propose a joint sparse and manifold learning with low rank embedding (JSML) method, in which a sparse representation method and a low-dimensional embedding problem are jointly optimized for feature extraction. Particularly,we deploy the local manifold regularization term with low-rank embedding to preserve the local manifold information and global information of the data. By introducing a discriminative analysis constraint term, JSLM can learn a more discriminative projection matrix for feature extraction. Finally, an alternatively iterative algorithm is carefully designed for solving the optimization problem. Experimental results on three commonly used databases verify that our method achieves superior performance in most cases.