Reviewerly/exHarmony huggingface dataset:
A Robust Deepfake Video Detection Method based on Continuous Frame Face-swapping

Detection of deepfake videos faces serious generalization problem in real world application scenarios. Existing robust deepfake detection methods can only works on single frame image but not continuous frame videos. In this paper, we propose a robust deepfake video detection method based on continuous frame face-swapping. We design our face-swapping dataset with Delaunay triangulation and piecewise affine transform to achieve continuous frame face-swapping. We design a feature enhancement module with facial and background information covered to make the method focus on the mask fusion zone. We build our detection model with Efficient Net to extract intra-frame fusion feature and LSTM to extract inter-frame time feature. Cross-domain experiments show that our method achieves better detection AUC than existing methods, which proves our method is robust because of generalization.
Safety helmet detection algorithm in complex scenarios based on YOLOX

Aiming at the problems that the existing helmet wearing detection algorithms have many application scenarios and rich detection targets. An improved helmet detection method EPP-YOLOX based on YOLOX is proposed. Firstly, the SPP structure of YOLOX is replaced by Enhance Pyramid pooling structure to strengthen shallow feature extraction. Secondly, YOLOX multi-scale fusion structure is added, and the spatial information and semantic information extracted from the attention mechanism image are added to reduce the loss of image details Finally, the experimental results show that the average accuracy of the proposed method is as high as 93.06%, which is 3.28% higher than the original model, while the size of the model parameters is unchanged, and the detection accuracy is also greatly improved. It realizes the high-precision detection of helmet in different scenes such as weak light scene, occlusion scene and multi-target.
IR$$^2$$Net: information restriction and information recovery for accurate binary neural networks

Weight and activation binarization can efficiently compress deep neural networks and accelerate model inference, but they cause severe accuracy degradation. Existing optimization methods for binary neural networks (BNNs) focus on fitting full-precision networks to reduce quantization errors and suffer from a tradeoff between accuracy and efficiency. In contrast, considering information loss and the mismatch between model capacity and input information quantity caused by network binarization, we propose Information Restriction and Information Recovery Network (IR $$^2$$ Net) to stimulate the potential of BNNs and achieve improved network accuracy by restricting the input information and recovering feature information. The proposed approach includes (1) information restriction, which evaluates the feature information extracted from the input by a BNN, discards some of the information it cannot focus on, and reduces the amount of the input information to match the model capacity; and (2) information recovery: due to the information loss incurred during forward propagation, the extracted feature information of the network is not sufficient for supporting accurate classification. Shallow feature maps with richer information are selected, and these feature maps are fused with the final feature maps to recover the extracted feature information and further enhance the model capacity to match the amount of input information. In addition, the computational cost is reduced by streamlining the information recovery method to strike a better tradeoff between accuracy and efficiency. Experimental results demonstrate that our approach still achieves comparable accuracy even with a $$\sim$$ 10x floating-point operations (FLOPs) reduction for ResNet-18. The models and code are available at https://github.com/pingxue-hfut/IR2Net .
InSAR Phase Unwrapping Algorithm Based on Deep GAN

At present, the traditional phase unwrapping algorithm is difficult to balance the accuracy and unwrapping efficiency. The traditional phase unwrapping algorithm is difficult to balance the accuracy and efficiency in the phase unwrapping experiments of simulated and measured topographic interferograms. In this paper, the phase unwrapping technology will be studied under the framework of deep learning theory according to the development trend of InSAR intelligence. A phase unwrapping algorithm based on deep GAN is proposed. The model structure includes dense convolution layer blocks and series structure, so that the network can achieve a better balance between feature extraction and detail preservation of interferogram. This is helpful to improve the phase unwrapping accuracy and training efficiency of the network. The experimental results show that the algorithm has a good expansion effect on the interferogram with high signal-to-noise ratio. The synthesis algorithm makes full use of the advantages of the branch cutting method and the finite element method. The phase reliable region and unreliable region are determined, and the transmission of phase error from the unreliable region to reliable region is effectively avoided. The accuracy of the phase unwrapping results in the reliable region is ensured, and the overall phase unwrapping convergence accuracy is greatly improved.
Dynamic attention-based detector and descriptor with effective and derivable loss for image matching

Jointly learned detectors and descriptors are becoming increasingly popular because they can simplify the matching process and obtain more correspondences than traditional tools. However, most methods yield low keypoint detection accuracy due to the large receptive field of the detection score map. In addition, existing methods lack efficient detector loss functions because the coordinates of keypoints are discrete and nonderivable. To mitigate these two problems, we propose a method called dynamic attention-based detector and descriptor with effective and derivable loss (DA-Net). For the first problem, a dynamic attention convolution-based feature extraction module is proposed to select the most suitable parameters for different samples. In addition, a multilayer feature self-difference detection (MFSD) module is proposed to detect keypoints with high accuracy. In the MFSD module, multilayer feature maps are used to calculate their feature self-difference maps, and they are fused to obtain a detection score map. For the second problem, an approximate keypoint distance loss function is proposed by approximately regressing the coordinates of the local maximum as keypoint coordinates, allowing the calculations involving keypoint coordinates to backpropagate. Moreover, two descriptor loss functions are proposed to learn reliable descriptors. A series of experiments based on widely used datasets show that DA-Net outperforms other learned detection and description methods.
Visual Watermark Identification From the Transparent Window of Currency by Using Deep Learning

Banknote identification plays an increasingly important role in financial fields due to the diffusion of automatic bank systems in terms of vending machines. Nowadays, YOLOv5 has become the state-of-the-art detector of visual objects because of its relatively outperformed accuracy with a high speed of computing. In this chapter, the squeeze-excitation (SE) attention module is mingled with the terminal of the backbone in YOLOv5 to further improve visual watermark recognition of paper banknotes. The main contribution of this chapter is that the excellent precision reaches 99.99% by utilizing the novel model YOLOv5+SE.
A process perspective of immersive virtual reality user experiences: Transition dynamics and mechanisms during gameplay

Extending the relevant theoretical perspectives of spatial presence and flow, we argue that a process perspective can help capture the complexities of multifaceted user experience. In this study, we examined how players feel, perceive, and act during virtual reality (VR) gameplay. We employed a qualitative research strategy combining in-depth interviews and field observations in a VR amusement park. Based on grounded and thematic analysis, we developed an integrated process model that describes players’ enjoyment states during VR single-player and cooperative games and illuminates the mechanisms that influence players’ gaming experiences. We then further developed process design elements for VR games. The findings and models proposed in this study provide theoretical insights into relevant VR user experience studies. The findings of this study may also provide game designers and industry practitioners with insights into designing interactive systems to enhance user experience and engagement.
An approach for copy-move image multiple forgery detection based on an optimized pre-trained deep learning model

Owing to the rapid increase in the availability of editing tools and image capturing, the amount of fake images available online has increased tremendously. Moreover, social media and other networks have become the foremost channel, which is designed for transmitting manipulated images, propagating rumours, spreading fake news, and so on. Thus, developing effective techniques for detecting such forgeries have become highly essential. One of the most widespread kinds of forgery is Copy Move Forgery (CMF), which alters the image by using the patches inside the image. Though deep learning-based techniques offer superior performance, they suffer from generalization issues. Therefore, a transfer learning-based method for identifying CMF utilizing a Deep Convolutional Neural Network (Deep CNN) is proposed to overcome the issue. Deep CNN is trained with the parameters of the pre-trained GoogLeNet, and is employed for detecting multiple forgeries. Furthermore, a novel optimization algorithm, Fractional Leader Harris Hawks Optimization (FLHHO), is created to modify the weights and bias of the Deep CNN. Further, the presented technique is evaluated for its effectiveness on the basis of the parameters like testing accuracy of 0.930, True Negative Rate (TNR) of 0.938, and True Positive Rate (TPR) of 0.941.
Synonym recognition from short texts: A self-supervised learning approach

Synonyms refer to different expressions for the same entity in the text and affect entity-centric text mining research performance. Therefore, synonym recognition has become a promising research topic in recent years. However, most existing approaches are based on structured, semi-structured, or long text, and only a few studies have tackled synonym recognition in short texts on social networks. Synonyms recognition in short texts confronts several research challenges. First, there are a large number of unlabeled synonyms in the short texts. Second, many new words will appear in short text on social networks. Therefore, in this paper, we propose a self-supervised learning method to recognize synonyms in short texts, which consists of two steps. First, we use a clustering algorithm to generate a pseudo-label for expression. Second, we input the co-occurrence information and the character information of the expressions into a deep-learning model to obtain the feature representation of the expression. The two steps are executed iteratively until the algorithm converges. To demonstrate the effectiveness of the proposed method, we conducted extensive experiments on a real short-text dataset, and the results suggest the effectiveness of our proposal.
When privacy fails, a formula describes an attack: A complete and compositional verification method for the applied π-calculus

We prove that three semantics for the applied π-calculus coincide – a testing semantics, a labelled equivalence, and a modal logic – and explain how design decisions in each of these semantics are objectively supported by this convergence. Each of these semantics has a distinct role when verifying security protocols. Testing semantics are suited to security and privacy problems expressed in terms of a process equivalence problem, where tests define the space of attack strategies. A labelled equivalence is suited to proving the given equivalence problem when such security and privacy properties hold; while, dually, the modal logic describes attacks when such security or privacy properties are violated. For this particular investigation into this good-practice design pattern, we select what is perhaps the most powerful (interleaving) testing semantics: open barbed bisimilarity. Selecting open barbed bisimilarity comes with the bonus that it is a congruence hence is suited to compositional reasoning. This reference testing semantics leads to a notion of quasi-open bisimilarity and an intuitionistic modal logic for the applied π-calculus. We argue that, since we characterise the finest (interleaving) testing semantics and tests are attack strategies, our intuitionistic modal logic can describe all attacks whenever a privacy property expressed as an equivalence problem fails.
Regularity bounded sensor clustering

A non-empirical, unsupervised learning, and graph analytic techniques pipeline investigated the earthquake sensor station proximity (<30 km) and neighborhood density (2–5 sensor) constrained geometric properties of New Zealand’s GeoNet seismic network. They are specific to the Propagation of the Local Undamped Motion (PLUM) earthquake detection algorithm. The experimental design considered 14 cloud and graph clustering processes, with variable combinations of 10–50 km distance and 2–5 sensor constraints. We prove that a clustering process alone is inadequate, and a strict graph r-regularity constraint is essential for forming valid sensor clusters. The goodness of fit measurements revealed HDBSCAN was best for initiating the pipeline. On average 44 % of the sensors fit well within the geometric bounds with a 45 % cluster quality score. With the regularity relaxed, 60 % of the sensors resulted in a 42 % node-degree weighted mean absolute error. The method can be applied to any proximity and density-constrained sensor network.
Evaluating the Role of System-Generated Communications in Health Care Organizations

This Viewpoint discusses measures to improve the role of system-generated communications in health care organizations.
4× Super‐resolution of unsupervised CT images based on GAN

Abstract Improving the resolution of computed tomography (CT) medical images can help doctors more accurately identify lesions, which is important in clinical diagnosis. In the absence of natural paired datasets of high resolution and low resolution image pairs, we abandoned the conventional Bicubic method and innovatively used a dataset of images of a single resolution to create near‐natural high–low‐resolution image pairs by designing a deep learning network and utilizing noise injection. In addition, we propose a super‐resolution generative adversarial network called KerSRGAN which includes a super‐resolution generator, super‐resolution discriminator, and super‐resolution feature extractor to achieve a 4× super‐resolution of CT images. The results of an experimental evaluation show that KerSRGAN achieved superior performance compared to the state‐of‐the‐art methods in terms of a quantitative comparison of non‐reference image quality evaluation indicators on the generated 4× super‐resolution CT images. Moreover, in terms of an intuitive visual comparison, the images generated by the KerSRGAN method had more precise details and better perceptual quality.
Post algebras and ternary adders

Abstract Except for qubits for which the different possible values are unordered, the different values of m -valued circuits either with voltage levels, current levels or charge levels are totally ordered. Either at the Math level (Post algebras) or at the circuit level, it means that each multiple valued level must be decomposed into binary levels, processed with binary computation and finally converted into a multiple valued level. Using ternary adders as example, we show that the ternary-to-binary decoding and binary encoding should be applied to the whole adder or to restricted parts of the adder. The second approach using multiplexers leads to the most efficient ternary adders. However, a comparison with binary adders shows that the ternary-to-binary and binary-to-ternary conversions is the reason for which the binary adders are more efficient.
Narrowband composite two-qubit gates for crosstalk suppression

We propose a method to construct composite two-qubit gates with narrowband profiles with respect to the spin-spin coupling. The composite sequences are selective to the variations in the amplitude and duration of the spin-spin coupling, and can be used for highly selective qubit addressing with greatly reduced crosstalk, quantum logic spectroscopy, and quantum sensing.
The Multi-Maximum and Quasi-Maximum Common Subgraph Problem

The Maximum Common Subgraph problem has been long proven NP-hard. Nevertheless, it has countless practical applications, and researchers are still searching for exact solutions and scalable heuristic approaches. Driven by applications in molecular science and cyber-security, we concentrate on the Maximum Common Subgraph among an indefinite number of graphs. We first extend a state-of-the-art branch-and-bound procedure working on two graphs to N graphs. Then, given the high computational cost of this approach, we trade off complexity for accuracy, and we propose a set of heuristics to approximate the exact solution for N graphs. We analyze sequential, parallel multi-core, and parallel-many core (GPU-based) approaches, exploiting several leveraging techniques to decrease the contention among threads, improve the workload balance of the different tasks, reduce the computation time, and increase the final result size. We also present several sorting heuristics to order the vertices of the graphs and the graphs themselves. We compare our algorithms with a state-of-the-art method on publicly available benchmark sets. On graph pairs, we are able to speed up the exact computation by a 2× factor, pruning the search space by more than 60%. On sets of more than two graphs, all exact solutions are extremely time-consuming and of a complex application in many real cases. On the contrary, our heuristics are far less expensive (as they show a lower-bound for the speed up of 10×), have a far better asymptotic complexity (with speed ups up to several orders of magnitude in our experiments), and obtain excellent approximations of the maximal solution with 98.5% of the nodes on average.
High Precision Mesh-Based Drone Image Stitching Based on Salient Structure Preservation and Regular Boundaries

Addressing problems such as obvious ghost, dislocation, and distortion resulting from the traditional stitching method, a novel drone image-stitching method is proposed using mesh-based local double-feature bundle adjustment and salient structure preservation which aims to obtain more natural panoramas.The proposed method is divided into the following steps. First, reducing parallax error is considered from both global and local aspects. Global bundle adjustment is introduced to minimize global transfer error, and then the local mesh-based feature-alignment model is incorporated into the optimization framework to achieve more accurate alignment. Considering the sensitivity of human eyes to linear structure, the global linear structure that runs through the images obtained by segment fusion is introduced to prevent distortions and align matching line segments better. Rectangular panoramas usually have better visual effects. Therefore, regular boundary constraint combined with mesh-based shape-preserving transform can make the results more natural while preserving mesh geometry. Two new evaluation metrics are also developed to quantify the performance of linear structure preservation and the alignment difference of matching line segments. Extensive experiments show that our proposed method can eliminate parallax and preserve global linear structures better than other state-of-the-art stitching methods and obtain more natural-looking stitching results.
Some Properties of the Computation of the Modular Inverse with Applications in Cryptography

In the field of cryptography, many algorithms rely on the computation of modular multiplicative inverses to ensure the security of their systems. In this study, we build upon our previous research by introducing a novel sequence, (zj)j≥0, that can calculate the modular inverse of a given pair of integers (a,n), i.e., a−1;mod,n. The computational complexity of this approach is O(a), which is more efficient than the traditional Euler’s phi function method, O(n,ln,n). Furthermore, we investigate the properties of the sequence (zj)j≥0 and demonstrate that all solutions of the problem belong to a specific set, I, that only contains the minimum values of (zj)j≥0. This results in a reduction of the computational complexity of our method, especially when a∼n and it also opens new opportunities for discovering closed-form solutions for the modular inverse.
Transmitting Culture through Building Systems: The Case of the Tile Vault

Until now, the technology behind building systems has largely been neglected, and an exploration of the cultural transmission of these systems has been undervalued as a way of understanding changes in the symbolic worldview of society. This article analyses the developmental transitions in a specific building system, specifically the tile vault, as a case study that illustrates the changing values of a construction process as it has evolved, together with an understanding of the philosophy that has accompanied those changes over time. An ethnohistorical and ethnographic method has been used, enabling a longitudinal and rich analysis through time that illustrates the indissolubility of the technical issues from the prevailing social institutions of each era. These are based on a careful reading of changing conditions (environmental, social, and cultural), and of the progressive participatory appropriation which takes place through cooperative, synergistic working.
Depth-Aware Neural Style Transfer for Videos

Temporal consistency and content preservation are the prominent challenges in artistic video style transfer. To address these challenges, we present a technique that utilizes depth data and we demonstrate this on real-world videos from the web, as well as on a standard video dataset of three-dimensional computer-generated content. Our algorithm employs an image-transformation network combined with a depth encoder network for stylizing video sequences. For improved global structure preservation and temporal stability, the depth encoder network encodes ground-truth depth information which is fused into the stylization network. To further enforce temporal coherence, we employ ConvLSTM layers in the encoder, and a loss function based on calculated depth information for the output frames is also used. We show that our approach is capable of producing stylized videos with improved temporal consistency compared to state-of-the-art methods whilst also successfully transferring the artistic style of a target painting.