Reviewerly/exHarmony huggingface dataset:
A Blockchain-Assisted Key Generation Electric Health Records Sharing Scheme

Electronic health records (EHRs) contain a large amount of private data of patients. Once these data are compromised during the process of sharing, it may threaten the patients’ privacy. In this paper, a novel blockchain-assisted electronic health record sharing scheme is proposed, which utilizes attribute-based encryption (ABE) based on the consortium blockchain to realize the privacy protection in the sharing process of EHRs. Firstly, the master key is negotiated by all consensus nodes of consortium blockchain, no one knows the specific value of the master key. Consensus nodes are acted by medical institutions, they also responsible for generating and managing users’ private keys. Secondly, with the help of powerful cloud services, pre-decryption and ciphertext retrieval assignments are outsourced to cloud services for reducing the burden of blockchain. Thirdly, this scheme is also based on searchable encryption, which allows for quick ciphertext lookup from cloud services. Data owners can generate ciphertext indexes for their private data, and data users with retrieval trapdoors can quickly retrieve ciphertexts. Finally, the security and performance of blockchain-assisted electronic health record sharing scheme are analyzed in detail, the results show that our scheme is safe and feasible.
Decision Making with State-Dependent Preference Systems

In this paper we present some first ideas for decision making with agents whose preference system may depend on an uncertain state of nature. Our main formal framework here are commonly scalable state-dependent decision systems. After giving a formal definition of those systems, we introduce and discuss two criteria for defining optimality of acts, both of which are direct generalizations of classical decision criteria under risk. Further, we show how our criteria can be naturally extended to imprecise probability models. More precisely, we consider convex and finitely generated credal sets. Afterwards, we propose linear pogramming-based algorithms for evaluating our criteria and show how the complexity of these algorithms can be reduced by approximations based on clustering the preference systems under similar states. Finally, we demonstrate our methods in a toy example.
A Novel Anomaly Detection Method in Sensor Based Cyber-Physical Systems

In recent times, Cyber-physical system (CPS) integrates the cyber systems and physical world for performing critical processes that are started from the development in digital electronics. The sensors deployed in CPS are commonly employed for monitoring and controlling processes that are susceptible to anomalies. For identifying and detecting anomalies, an effective anomaly detection system (ADS) is developed. But ADS faces high false alarms and miss detection rate, which led to the degraded performance in CPS applications. This study develops a novel deep learning (DL) approach for anomaly detection in sensor-based CPS using Bidirectional Long Short Term Memory with Red Deer Algorithm (BiLSTM-RDA). The presented BiLSTM-RDA model comprises preprocessing classification, and parameter tuning. Initially, the sensor data undergoes preprocessing to remove the noise present in it. Afterward, the BiLSTM based classification process takes to detect the existence of anomalies in CPS. At last, parameter tuning of the Bi-LSTM model is carried out by the use of RDA for tuning the parameters such as the number of hidden layers, batch size, epoch count, and learning rate. For assessing the experimental outcome of the BiLSTM-RDA technique, a comprehensive experimentation is performed using the data from sensor-based CPS. A detailed comparative analysis takes place to ensure the effective detection performance of the BiLSTM-RDA model and The obtained experimental results verified the superior performance on the applied data over the compared methods with the maximum an average precision of 0.989, recall of 0.984, F-score of 0.985, and accuracy of 0.983.
Bullet-Time Video Synthesis Based on Virtual Dynamic Target Axis

Bullet-time videos have been widely used in movies, TV advertisements, and computer games, and can produce an immersive and smooth orbital free-viewpoint of frozen action. However, existing bullet-time video synthesis methods remain challenging in practical applications, especially in complex situations with poor camera calibration and a variety of camera array structures. This paper proposes a novel bullet-time video synthesis method based on a virtual dynamic target axis. We adopt an image similarity transformation strategy to eliminate image distortion in the bullet-time video. We use a high-order polynomial curve fitting strategy to reserve more bullet-time video frame content. The proposed dynamic target axis strategy can support various camera array structures, including camera arrays with and without a common field of view. In addition, this strategy can also tolerate poor camera calibration situations with unevenly distributed reprojection errors to some extent and synthesize smooth bullet-time videos without high-precision camera calibration. Qualitative and quantitative experiments in real environments and on simulation platforms demonstrate the high performance of our bullet-time video synthesis method. Compared with the state-of-the-art methods, the proposed method shows superiority.
Attentional Conditional Generative Adversarial Network for Ambient Occlusion Approximation

Ambient occlusion is a method that increases the realism of computer generated images by estimating the reflected illumination in areas where light fails to illuminate them due to occlusions imposed by surrounding objects. In this paper, we introduce a new approach able to generate an approximation of the ambient occlusion, thus estimating the location of soft shadows. Our approach is based on using a conditional generative adversarial network with an attention mechanism to perform a space translation from the input data to the output data space by enforcing the conditioning with an attention mechanism. Our attention module is integrated into the generator architecture and the discriminator to allow the neural network to pay more attention to relevant areas to estimate the location of soft shadows and generate a good approximation of the ambient occlusion. Our method generates images with a satisfactory similarity with the reference method and does not require any pre-processing of the generated image, so it does not perform any computation in the three-dimensional space of the 3D scene.
Predicting the State of a House Using Google Street View

Currently, the state of a house is typically assessed by an expert, which is time and resource intensive. Therefore, an automatic assessment could have economic, social and ecological benefits. Hence, this study presents a binary classification model using transfer learning to classify Google Street View images of houses. For this purpose, a three-by-three analysis is conducted that allows to compare three different network architectures and three differently-sized data sets, using properties located in Leuven, Belgium. A DenseNet201 architecture was found to work best, as illustrated quantitatively as well as by means of state-of-the-art explainability methods.
Pavement Marking Incorporated With Binary Code for Accurate Localization of Autonomous Vehicles

Accurate localization is a critically important issue for autonomous vehicles as it is closely related to the safety and efficiency of autonomous driving. However, current technologies for autonomous vehicle localization face many challenges. To provide accurate and robust localization services to autonomous vehicles, we propose a novel solution by employing a newly designed pavement marking. This marking operates on color contrast, temperature contrast, and binary code with some special features. We also trained and customized an object detector based on a deep learning model: YOLOv5, and integrated it with the decoding algorithm. The localization system is capable of running at a steady frame rate of more than 50 FPS. Road trials up to 80 km/h were conducted, and satisfactory results confirmed the feasibility and robustness of the localization system. Specifically, with a common onboard camera, more than four continuous frames can be detected and decoded correctly when the speed is slower than 30 km/h. At least one frame can be detected and decoded correctly at a higher speed (i.e., 30– 50 km/h). With a high-speed camera, more than 18 frames can be detected and decoded even at 80 km/h. The findings suggest that the specially designed road marking and associated algorithms can provide a viable and economical option for accurate localization of autonomous vehicles. The performance of the system has potentials for further improvement by using better hardware such as faster CPUs, GPUs, and thermal imaging techniques.
Route Tracking Model of Rock Climbing Activity Based on Probabilistic Hough Transform Algorithm

In order to study the cognitive characteristics of the decision-making process of athletes, the expert model analyzes the visual search mode with theoretical and practical value, which helps athletes improve their decision-making ability and exercise level. This article focuses on the research of rock climbing activity route tracking based on probabilistic Hough transform algorithm. After understanding the theory of probabilistic Hough transform algorithm and rock climbing activity route tracking based on literature data, the rock climbing activity route tracking model based on probabilistic Hough transform algorithm is studied. It is constructed, and the constructed model is tested through experiments. The test results show that the model constructed in this paper has a high recognition accuracy and a certain degree of robustness.
Application of Two-Dimensional Code Encryption Algorithm Under Asymmetric Cipher System

In order to understand the application mode of TWO-DIMENSIONAL code encryption algorithm under asymmetric password system, this paper will carry out relevant research, mainly discusses the basic concept of asymmetric password system, and then introduces the principle and common algorithm of asymmetric TWO-DIMENSIONAL code encryption algorithm under the system, and finally launches the design of two-dimensional code security application system based on relevant algorithm. The two-dimensional code security application system designed in this paper can encrypt the asymmetric password of the two-dimensional code through the algorithm. After the encryption, the security of the two-dimensional code will be significantly improved and can be put into practical application.
Improved Color Normalization Method for Histopathological Images

The exponential growing demand for computer-aided systems has significantly increased the detection of cancerous cells from digital histopathology images. However, the cell manual sectioning and color variation inevitably create challenges and affect the performance of computer-assisted diagnosis (CAD) due to misclassification. Therefore, color normalization of hematoxylin and eosin (H&E) plays an important role to attain promising outcomes. This paper proposes the improved color normalization method by incorporating the Gaussian function in the fuzzy modified Reinhard method to enhance the intensity and contrast of the image. To evaluate the improved fuzzy modified Reinhard (IFMR) algorithm, the comparative analysis is performed on several mathematical quality metrics. The observation depicts that the proposed algorithm provides better results and works efficiently in comparison with existing methods.
A Traitor Tracking Method Towards Deep Learning Models in Cloud Environments

Cloud computing can speed up the training process of deep learning models. In this process, training data and model parameters stored in the cloud are prone to threats of being stolen. In model protection, model watermarking is a commonly used method. Using the adversarial example as model watermarking can make watermarked images have better concealment. Oriented from the signature mechanism in cryptography, a signature-based scheme is proposed to guarantee the performance of deep learning algorithms via identifying these adversarial examples. In the adversarial example generation stage, the corresponding signature information and classification information will be embedded in the noise space, so that the generated adversarial example will have implicit identity information, which can be verified by the secret key. The experiment using the ImageNet dataset shows that the adversarial examples generated by the authors’ scheme must be correctly recognized by the classifier with the secret key.
Equity, Diversity and Inclusion in Designing AI-Based Solutions for Migrants’ Integration: REBUILD Project

Comprehensive solutions for migrants and asylum seekers, without any distinction, need to involve the whole society to improve the integration of citizens coming from countries with different cultures, religions and life patterns. A Digital Companion that will support migrants and refugees in Europe in accessing existing services, and, at the same time, will enable local service providers who interact with migrants and refugees, has been developed and tested at EU level in different countries. AI-based services are at the heart of the Digital Companion, which will support more effective communication between public administrations and migrants. The human-centred, iterative, participative and critical design and development approach proved valuable to address the heterogeneous needs of the end users as well as of the local service providers. Concrete issues in defining, testing and refining the AI are discussed in view of provoking an impact on the whole society and towards a scenario of full development and upscaling.
Digital Means for Increased Vocational Participation of People with Intellectual Disabilities

Due to the COVID-19-pandemic, digital participation has become more important than ever before. Since people with intellectual disabilities (ID) are still facing barriers regarding their vocational and digital participation, the miTAS project aims to improve their participation by providing the miTAS app and the miTAS media box. These offer checklists and instructions based on individual needs. This research aims to find out about the framework conditions for using digital tools in vocational training and vocational rehabilitation of people with ID. Implications for how to manage the digital transformation process in these facilities should be derived as well. Eleven semi-structured group interviews with people with ID and social workers were conducted as videoconferences. The research indicates that the COVID-19-pandemic has influenced daily life in the facilities in different ways. Especially facilities in rural areas are still lacking technical equipment and concepts for providing digital participation. Digital tools offer an added value for the vocational participation of people with intellectual disabilities (ID) such as joint learning, fun, gain of digital competencies, autonomy, and self-efficacy. For the digital transformation process, it takes the support of the management board, human resources, and technical infrastructure. The added value of the new digital tool must be clearly recognizable. Within the facilities, transparency, clarity in internal communication, and an appealing concept are needed. Moreover, the inclusion of people with ID as experts and new ways of peer-to-peer support is highly recommended. Interdisciplinary cooperation can be helpful as well.
Optimizing the Multi-Objective Discrete Particle Swarm Optimization Algorithm by Deep Deterministic Policy Gradient Algorithm

Deep deterministic policy gradient (DDPG) has been proved to be effective in optimizing particle swarm optimization (PSO), but whether DDPG can optimize multi-objective discrete particle swarm optimization (MODPSO) remains to be determined.The present work aims to probe into this topic.Experiments showed that the DDPG can not only quickly improve the convergence speed of MODPSO, but also overcome the problem of local optimal solution that MODPSO may suffer.The research findings are of great significance for the theoretical research and application of MODPSO.
Educational programs for adults as a tool for personal growth: designing vectors

Program planning in adult education is one of the main activities of personnel working in this area, however, it has not been sufficiently studied in Russia. In recent years, research on this topic has been actively carried out in a number of countries, and results have been published, including the methodology for designing educational programs, the principles of program planning, etc. This article aims to contribute to the study of program planning in our country. The authors of the article reveal multimodal approaches to the design of adult education programs that respond to its modern challenges. In this context, the special significance of the principle of personification is emphasized. The article presents the results of the study of theoretical approaches to the development of programs and the identification of their &amp;quot;genetic structure&amp;quot;, which makes it possible to realize the idea of personification. The practical part of the study was the analysis of the experience of cultural education for adults in St. Petersburg over a 20-year period, including the results of a large-scale survey of teachers and administration of educational institutions conducted in 2021. The article ends with summing up the results of the first stage of the study and determining the direction of further work.
Intelligent Prediction of Daily Global Solar Radiation Using Hybrid Machine Learning Algorithms

In this study, using daily meteorological data, four hybrid machine learning algorithms, including GA-MLP, GA-SVM, GA-GBM, and GA-kNN evaluated to predict the Rs of Sanandaj city. Daily meteorological variables Tx, Tn, RHx, RHn, sunshine hours, and day of the year were set in three different combinations. Scenario I, included all meteorological data and DOY. Scenarios II and III, with four and three inputs were created by deleting RH; and RH and sunshine hour from scenario I. In order to allow comparative analysis, four empirical models of H-S, A-P, modified A-D, and modified-N were investigated. The results showed that the hybrid machine learning models generally had higher accuracy than the empirical models. The performance of the GA-MLP and GA-SVM models was better than the GA-GBM and GA-kNN models in every three scenarios. Evaluation criteria in scenario I, were RMSE=1.320-1.733, MAE=0.877-1.193 (MJm -2 d -1 ), and R 2 =0.971-0.949. In Scenario II, the estimation accuracy of the hybrid models changed very little (RMSE=1.351-1.671, MAE=0.896-1.114 (MJm -2 d -1 ), and R 2 =0.996-0.955). In Scenario III, the accuracy of solar radiation estimation decreased. Among the empirical models, modified-N and A-P estimated solar radiation with better accuracy and had the same performance.
The Role of Education in Shaping the Ecological Culture of Contemporary Students

The paper analyzes the value orientations of students regarding environmental values. Everyone should clearly understand the consequences of the irrational use of natural resources and the consumer attitude toward nature. Environmental education plays a fundamental role in the formation of environmental culture. The paper focuses on the value attitudes of the individual since the ecological state of the environment should be as important to a person as social status and material well-being. The authors conducted a survey (questionnaire) to study the environmental attitudes and value orientations of contemporary youth. The analysis showed that students are dominated by material values. Environmental values are significantly inferior to material values or critically low. This demonstrates the need for additional efforts to form and correct the environmental education and culture of young people. Environmental education should involve individuals in specific environmental activities through spiritual communication with nature and direct interaction with nature.
&lt;P Class="Els-Title"&gt;Deep Learning Image Dehazing Scheme Using Recursive Convolutional Layer Feature Extraction Method&lt;O:P&gt;&lt;/O:P&gt;&lt;/P&gt;

AbstractThe computer vision and graphics role is had seen in wide application fields in these decades. The haze fetches plentiful troubles to numerous graphics applications of computer vision as it minifies the scene clarity, which commonly affects the clear images captured by causing visibility reduction. So, it lowers the contrast and saturation in turn devalues the quality of the image preview and capture. The importance of the dehazing methods comes out, it is a big challenge to take off the haze from the images, in order to increase the clarity of the scene and the quality of the image. Recently, there are multiple methods have been suggested for haze removing and improving images, and the more interesting method by the researchers is the single image dehazing method. So, in this research we proposed a suitable single image dehazing method based on deep learning to do, firstly, pre-processing the dehazing method to estimate atmospheric air light and scattering transmission for the hazy image. Secondly designing a deep learning image dehazing scheme including a new recursive convolutional layer to detect hazy areas and extract the specific features of the hazy image. Thirdly adding interactive statistics features to the RCL contains units to create features maps aim to reduce the number of haze resources desired for preventing the redundant haze data, and deny losing essential or relevant information. The experiment results using two sets of standard datasets RESIDE and HAZERD showed that the scheme has satisfactory results and achieved a higher accuracy compete with the state of art methods.
Visual Analytics of Hierarchical and Network Timeseries Models

Confidence in timeseries models can be gained through visual analytics that represent the many aspects of the models including, the timeseries data (input, predicted, intermediate factors), model structure, model behavior, model sensitivity and model quality in one holistic application. We show examples ranging from simplistic prototypes of financial ratios, to nowcasting and economic forecasting, and massive transaction analysis. The approach is perceptually scalable to the exploration of large-scale structures with millions of nodes by visually representing many node characteristics; on-demand navigation through sub-graphs; hierarchical clustering of nodes; and aggregation of links and nodes. These visual analytics allow expert users to compare the many aspects of the model to their real-world knowledge helping them gain an understanding of the model and ultimately build confidence.
Referring Expression Comprehension

Referring expression comprehension (REC) aims to localize objects in images based on natural language queries. In contrast to the object detection task, in which queried object labels are predefined, the REC problem can only observe the queries during the test. REC is difficult to implement because this task requires a comprehensive understanding of complicated natural language and various types of visual information. In this chapter, we first describe this task and subsequently introduce prevalent datasets proposed for REC tasks such as the RefCOCO, RefCOCO+ and RefCOCOg datasets. Finally, we classify the methods in the REC domain into three main categories: two-stage models, one-stage models and reasoning process comprehension.