Reviewerly/exHarmony huggingface dataset:
Perception of perspective in augmented reality head-up displays

Augmented Reality (AR) is emerging fast with a wide range of applications, including automotive AR Head-Up Displays (AR HUD). As a result, there is a growing need to understand human perception of depth in AR. Here, we discuss two user studies on depth perception, in particular on the perspective cue. The first experiment compares the perception of the perspective depth cue (1) in the physical world, (2) on a flat-screen, and (3) on an AR HUD. Our AR HUD setup provided a two-dimensional vertically oriented virtual image projected at a fixed distance. In each setting, participants were asked to estimate the size of a perspective angle. We found that the perception of angle sizes on AR HUD differs from perception in the physical world, but not from a flat-screen. The underestimation of the physical world’s angle size compared to the AR HUD and screen setup might explain the egocentric depth underestimation phenomenon in virtual environments. In the second experiment, we compared perception for different graphical representations of angles that are relevant for practical applications. Graphical alterations of angles displayed on a screen resulted in more variation between individuals' angle size estimations. Furthermore, the majority of the participants tended to underestimate the observed angle size in most conditions. Our results suggest that perspective angles on a vertically oriented fixed-depth AR HUD display mimic more accurately the perception of a screen, rather than the perception of the physical 3D environment. On-screen graphical alteration does not help to improve the underestimation in the majority of cases.
RankSRGAN: Super Resolution Generative Adversarial Networks With Learning to Rank

Generative Adversarial Networks (GAN) have demonstrated the potential to recover realistic details for single image super-resolution (SISR). To further improve the visual quality of super-resolved results, PIRM2018-SR Challenge employed perceptual metrics to assess the perceptual quality, such as PI, NIQE, and Ma. However, existing methods cannot directly optimize these indifferentiable perceptual metrics, which are shown to be highly correlated with human ratings. To address the problem, we propose Super-Resolution Generative Adversarial Networks with Ranker (RankSRGAN) to optimize generator in the direction of different perceptual metrics. Specifically, we first train a Ranker which can learn the behaviour of perceptual metrics and then introduce a novel rank-content loss to optimize the perceptual quality. The most appealing part is that the proposed method can combine the strengths of different SR methods to generate better results. Furthermore, we extend our method to multiple Rankers to provide multi-dimension constraints for the generator. Extensive experiments show that RankSRGAN achieves visually pleasing results and reaches state-of-the-art performance in perceptual metrics and quality. Project page: https://wenlongzhang0517.github.io/Projects/RankSRGAN.
E-Res U-Net: An improved U-Net model for segmentation of muscle images

In this paper, we propose a new semantic segmentation network called 'E-Res U-Net', to achieve better segmentation results of deep and superficial muscles in ultrasonic muscle images. This model is based on U-Net, and its structure has been modified to improve the performance of the algorithm. There are three aspects of improvement based on U-Net, including E-Res layer, dilated convolution module, and E-Res path. Additional experiments demonstrate that each designed module in our proposed network is effective, can improve the accuracy compared to the original U-Net. When compared with other algorithms which are state-of-the-art, the experimental result under the overall network structure is even more excellent.
Robust supervised multi-view feature selection with weighted shared loss and maximum margin criterion

Supervised multi-view feature selection has recently been proven to be a valid way for reducing the dimensionality of data. This paper proposes a robust supervised multi-view feature selection method based on weighted shared loss and maximum margin criterion. Specifically, we fuse all weighted views to establish a shared loss term to maintain the complementary information of views, where the weight of each view can be automatically adjusted according to its contribution to the final task. Moreover, we adopt the capped l2-norm rather than the widely used l2-norm to measure the loss and remove the potential outliers. Further, we introduce the maximum margin criterion (MMC) into the feature selection and design a weighted view-based MMC such that it is a proper regularization associated with the proposed model. In this way, the inter-class and intra-class structure information of data is well exploited, which makes the margins of the samples belonging to the different classes increase and the margins of the samples belonging the same class decrease. Then the discriminative features of all views can be selected. The corresponding problem can be solved by the alternating direction method of multipliers (ADMM), which can realize the view-block calculation and reduce the computational complexity. Comprehensive experiments on various benchmark datasets and the application on the hydraulic system confirm the validity of our method.
Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model

This paper addresses the problem of the unsupervised approach of credit card fraud detection in unbalanced datasets using the ARIMA model.The ARIMA model is fitted to the regular spending behaviour of the customer and is used to detect fraud if some deviations or discrepancies appear.Our model is applied to credit card datasets and is compared to four anomaly detection approaches, namely, the K-means, box plot, local outlier factor and isolation forest approaches.The results show that the ARIMA model presents better detecting power than that of the benchmark models.
Garbage Disposal of Complex Background Based on Deep Learning With Limited Hardware Resources

With the increasingly severe problems of garbage contamination, automatic garbage identification has significant value in actual application. However, the real implementation is still confronted with the limitations of equipment performance and the need for more computer resources. To effectively tackle the challenges, this paper proposes a model that uses the MobileNet as a base network to improve the performance of Mask R-CNN, which could be applied to the low-cost embedded devices. For a numerical evaluation on real images, we construct a garbage image data set containing multiple morphological types based on the complex background. Specifically, when pre-processing the data set, we use the methods of data expansion and transfer learning. Consequently, high-precision detection of garbage could be achieved under the condition of a few samples. The experimental results present that the approach can achieve a detection accuracy up to 93%. Furthermore, the proposed approach is a complete system combining both hardware and software design, which dramatically promotes the automation of production and provides a reference for the intelligent garbage sorting machines.
Finite-Time Analysis and Restarting Scheme for Linear Two-Time-Scale Stochastic Approximation

Motivated by its broad applications in machine learning and reinforcement learning, we study the linear two-time-scale stochastic approximation, an iterative method using two different step sizes for finding the solutions of a system of two equations. Our main focus is to characterize the finite-time complexity of this method under time-varying step sizes and Markov randomness. In particular, we show that the mean square errors of the variables generated by the method converge to zero at a sublinear rate ${\cal O}(k^{2/3})$, where $k$ is the number of iterations. We then improve the performance of this method by considering the restarting scheme, where we restart the algorithm after every predetermined number of iterations. We show that using this restarting method the complexity of the algorithm under time-varying step sizes is as good as the one using constant step sizes, but still achieving an exact convergence to the desired solution. Moreover, the restarting scheme also helps to prevent the step sizes from getting too small, which is useful for the practical implementation of the linear two-time-scale stochastic approximation.
An evolutionary game‐theoretic analysis of enterprise information security investment based on information sharing platform

The threat to enterprise information security is growing through advancing in information and communications technology; in order to enhance the efficiency of information security sharing platform, it is necessary to analyze the process about information security sharing between enterprises that take part in the sharing platform. In this paper, the evolutionary game theory (EGT) is applied to form a better understanding of information security investment based on information sharing platform. The evolution of information sharing and its influencing factors in information security sharing platforms are analyzed. The paper provides some suggestions for promoting the efficiency of platform information sharing.
Novel SDDM Rating Prediction Models for Recommendation Systems

The accuracy of behavioral interactive features is a key factor for improving the performance of rating prediction. In order to deeply explore the potential rules of user behavior and enhance the accurate representation of interactive features, this paper proposes two rating prediction models, based on the spatial dimension and distance measurement (SDDM), under the premise of taking the mean value of the user behavior history as a user feature, and obtaining the interactive features of an item and a user by calculating the distance between them in each feature dimension. In the proposed SDDM-Var and SDDM-PCC models, the variance and the Pearson correlation coefficient (PCC) are respectively utilized to evaluate the user’s attention to each feature dimension as to further obtain the weight vector of the interactive features. Finally, in order to improve the generalization ability of the proposed models, the rating prediction is accomplished by means of a specially designed multi-layer full-connection neural network. The conducted experiments with two public MovieLens datasets demonstrate the superior rating prediction performance of the proposed models in comparison with the existing baseline models, in terms of the root mean square error (RMSE), by achieving values of 0.865 and 0.872 on MovieLens 100K, and 0.839 and 0.832 on MovieLens 1M, respectively for SDDM-Var and SDDM-PCC.
An Efficient Ciphertext-Policy Attribute-Based Encryption Scheme Supporting Collaborative Decryption With Blockchain

In the last few decades, ciphertext-policy attribute-based encryption (CP-ABE) technology has attracted great interest, since it can provide fine-grained, flexible, and access control for sensitive data to implement a high secure and efficient data-sharing mechanism. In this article, based on the linear secret sharing scheme (LSSS), an efficient scheme is proposed to realize a collaborative decryption function. For any user group, when the user’s attribute set cannot access the ciphertext alone, the private key of other users in the same group can be used for collaborative decryption with the permission of the data owner. Our scheme uses the LSSS matrix that can significantly reduce the computation and storage overhead when comparing with the existing schemes. Then, a multiauthorization model is created based on the Bohen–Lynn–Shacham technology in order to solve the key-management issue. Finally, we implemented the specific functions of the framework through JAVA, and built a private chain to verify the feasibility of data transfer between users.
Location-based Daily Human Activity Recognition using Hybrid Deep Learning Network

Human activity recognition (HAR) is an interesting and challenging subject of study. HAR provides useful information regarding human movement and activity in ordinary life. A number of HAR-based solutions such as wellness tracking and biometric identification systems have been introduced over the past decade. A number of deep learning algorithms have recently been employed to resolve the complication of handcrafted features in traditional machine learning approaches. The novel deep learning framework to solve the HAR effect on overall accuracy is proposed in this study. The framework is a location-based CNN-LSTM hybrid model. The framework is validated using evaluation measures such as accuracy and other effective measures on a public dataset of wristwatch accelerometer data named the DHA dataset. When comparing the accuracy of alternative deep learning approaches, the proposed location-based CNN-LSTM ranked highest with an accuracy of 96.75%.
On-Shelf Utility Mining of Sequence Data

Utility mining has emerged as an important and interesting topic owing to its wide application and considerable popularity. However, conventional utility mining methods have a bias toward items that have longer on-shelf time as they have a greater chance to generate a high utility. To eliminate the bias, the problem of on-shelf utility mining (OSUM) is introduced. In this article, we focus on the task of OSUM of sequence data, where the sequential database is divided into several partitions according to time periods and items are associated with utilities and several on-shelf time periods. To address the problem, we propose two methods, OSUM of sequence data (OSUMS) and OSUMS + , to extract on-shelf high-utility sequential patterns. For further efficiency, we also design several strategies to reduce the search space and avoid redundant calculation with two upper bounds time prefix extension utility ( TPEU ) and time reduced sequence utility ( TRSU ). In addition, two novel data structures are developed for facilitating the calculation of upper bounds and utilities. Substantial experimental results on certain real and synthetic datasets show that the two methods outperform the state-of-the-art algorithm. In conclusion, OSUMS may consume a large amount of memory and is unsuitable for cases with limited memory, while OSUMS + has wider real-life applications owing to its high efficiency.
PLIERS

Programming language design requires making many usability-related design decisions. However, existing HCI methods can be impractical to apply to programming languages: languages have high iteration costs, programmers require significant learning time, and user performance has high variance. To address these problems, we adapted both formative and summative HCI methods to make them more suitable for programming language design. We integrated these methods into a new process, PLIERS, for designing programming languages in a user-centered way. We assessed PLIERS by using it to design two new programming languages. Glacier extends Java to enable programmers to express immutability properties effectively and easily. Obsidian is a language for blockchains that includes verification of critical safety properties. Empirical studies showed that the PLIERS process resulted in languages that could be used effectively by many programmers and revealed additional opportunities for language improvement.
A Densely Connected Network Based on U-Net for Medical Image Segmentation

The U-Net has become the most popular structure in medical image segmentation in recent years. Although its performance for medical image segmentation is outstanding, a large number of experiments demonstrate that the classical U-Net network architecture seems to be insufficient when the size of segmentation targets changes and the imbalance happens between target and background in different forms of segmentation. To improve the U-Net network architecture, we develop a new architecture named densely connected U-Net (DenseUNet) network in this article. The proposed DenseUNet network adopts a dense block to improve the feature extraction capability and employs a multi-feature fuse block fusing feature maps of different levels to increase the accuracy of feature extraction. In addition, in view of the advantages of the cross entropy and the dice loss functions, a new loss function for the DenseUNet network is proposed to deal with the imbalance between target and background. Finally, we test the proposed DenseUNet network and compared it with the multi-resolutional U-Net (MultiResUNet) and the classic U-Net networks on three different datasets. The experimental results show that the DenseUNet network has significantly performances compared with the MultiResUNet and the classic U-Net networks.
Tiny-CFA: Minimalistic Control-Flow Attestation Using Verified Proofs of Execution

The design of tiny trust anchors attracted much attention over the past decade, to secure low-end MCU-s that cannot afford more expensive security mechanisms. In particular, hardware/software (hybrid) co-designs offer low hardware cost, while retaining similar security guarantees as (more expensive) hardware-based techniques. Hybrid trust anchors support security services (such as remote attestation, proofs of software update/erasure/reset, and proofs of remote software execution) in resource-constrained MCU-s, e.g., MSP430 and AVR AtMega32. Despite these advances, detection of control-flow attacks in low-end MCU-s remains a challenge, since hardware requirements for the cheapest mitigation techniques are often more expensive than the MCU-s themselves. In this work, we tackle this challenge by designing Tiny-CFA - a Control-Flow Attestation (CFA) technique with a single hardware requirement - the ability to generate proofs of remote software execution (PoX). In turn, PoX can be implemented very efficiently and securely in low-end MCU-s. Consequently, our design achieves the lowest hardware overhead of any CFA technique, while relying on a formally verified PoX as its sole hardware requirement. With respect to runtime overhead, Tiny-CFA also achieves better performance than prior CFA techniques based on code instrumentation. We implement and evaluate Tiny-CFA, analyze its security, and demonstrate its practicality using real-world publicly available applications.
Maximum weight multi-modal information fusion algorithm of electroencephalographs and face images for emotion recognition

• The novelty of the work presented in this paper is that a information fusion algorithm of the maximum multi-modal weight decision is proposed. The emotion recognition scheme is developed by using the multi-modal information fusion algorithm. • Another major contribution of this work is the innovative conversion of traditional EEG data into corresponding EEG topographic maps for the emotion recognition. • Besides, this paper builds a multi-scale feature extraction network model for the facial expression recognition. In view of the low accuracy of the traditional emotion recognition methods based on facial expressions, an emotion recognition method based on maximum weight multi-modal information fusion of electroencephalographs (EEGs) and facial expression information is proposed in this paper. First, the induced emotional EEG data is converted into the corresponding EEG topographic map data and sent to the convolutional network for training and outputting decision information. Second, the illumination compensation method is utilized to filter the noise of the face image data. Then, the face image data is trained in the multi-scale feature extraction network, and the decision information is output. Finally, aiming at the decision-level information fusion, a weighted fusion method is proposed in this paper for emotion recognition. Experimental tests show that the recognition accuracy of the multi-scale feature extraction network on the CK+ data set and Fer2013 data reached 94.4% and 72%, respectively. Simultaneously, the multi-modal information fusion method achieves 92.6% accuracy in emotion recognition. The novelty of the work presented in this paper is that a fusion method of maximum weight decision information is proposed. Emotion recognition is realized by the weighted fusion of EEG information and facial expression information. Another major contribution of this work is the innovative conversion of traditional EEG data into corresponding state EEG topographic maps for processing and analysis. Besides, this paper builds a multi-scale feature extraction network for emotion recognition. The graphical abstract of this paper is as below.
Construction of super-resolution model of remote sensing image based on deep convolutional neural network

With the continuous improvement of satellite remote sensing technology, using super-resolution image reconstruction technology to reconstruct remote sensing images has important application significance for social development. In the generator model proposed in this paper, the standard convolution layer in the residual network structure is replaced by empty convolution to improve the overall performance of the model while keeping the number of parameters unchanged and the receptive field of convolution at each stage unchanged. By analyzing the advantages of residual network, dense connection network, and cavity convolution in the field of image super resolution, an optimized super-resolution reconstruction model of GAN image with cavity convolution is constructed with dense connection block of cavity residue as a generator component. The cloud computing-based service model is introduced into the image reconstruction system, and the background management module is built through the cloud service system, which is responsible for model training, image transmission, image processing request and database reading. Through experimental analysis, it is proved that the whole automatic data processing from automatic matching data to processing data can be completed, and the performance is better than the traditional service mode, which can produce great economic benefits.
A Novel Covert Communication Method Based on Bitcoin Transaction

With the global promotion and application of 5G technology, the data transmitted on the network show explosive growth, and the secure sharing of its important data is still one of the research hotspots. Steganography embeds the data that needs to be shared into digital carrier files and transmits it through open channels, which has important applications in protecting data sharing and realizing covert communication. However, the encrypted files generated by traditional steganography are susceptible to compression, cropping, geometric attacks, and man-made destruction in the process of open channel transmission, resulting in data loss, making it difficult for the receiver to correctly extract secret message. Currently, covert communication on blockchain can solve the above problems, but it also brings some new problems such as high computational complexity, low transmission efficiency, and nondetection resistance. Therefore, in this article, a covert communication method based on Bitcoin transactions is proposed. The proposed method first designs the index matrix of the transaction address. Then, the address interaction relationship that carries the secret message through the transaction index matrix is constructed. Finally, the address interaction relationship that carries the secret message is combined with the transaction amount to complete the covert transmission of the secret message on the blockchain environment. The proposed method improves the security and embedding efficiency of covert communication, reduces the number of transactions, and ensures the integrity of extracting secret message. A series of experimental results show that under the condition of ensuring the necessary security, the proposed method retains the strong robustness of the existing blockchain steganography and has strong resistance to detection.