Reviewerly/exHarmony huggingface dataset:
Multi‐angle face expression recognition based on generative adversarial networks

Abstract Because of the different features of the same facial expressions from different angles, most of the methods are only suitable for face images, and the accuracy of facial expression recognition is low. Therefore, a multi‐angle facial expression recognition method based on generative adversarial networks (GAN) is proposed. Firstly, the depth regression network is used to detect the key points of the face image to achieve face alignment, so as to reduce the difficulty of feature extraction. Then, the image is input to GAN. The generator is composed of encoder and decoder, and a skip connection is designed. In the encoding phase, the generator can unlock the correlation between the facial expression image and the angle, and in the decoding stage, it can generate different angle facial expression images by adding other angle information. Finally, the multi‐angle facial expression images are sent to the convolution neural network for classification and learning, in which the loss weight is adjusted dynamically to improve the recognition accuracy by introducing resistance loss, recognition loss, content loss, and center loss. The experimental results on Multi‐pose illumination expression (PIE) and celebrities in frontal profile (CFP) datasets show that the performance of the proposed method is the best when the learning rate is 0.0002, and the recognition accuracy under different angles is higher than other comparison methods, so it has practical application significance.
Does monitoring deter future cheating? The case of external examiners in Italian schools

We use the repeated random assignment of external examiners to schools in Italy to investigate whether the effect of external monitoring on test score manipulation persists over time. We find that this effect is still present in the tests taken one year after exposure to the examiners. In the second year after exposure, however, this effect disappears, suggesting that persistence is short lived.
Connected Layers: Evaluating Visualizations of Embodiment in Contemporary Dance Performances

There has been a growing interest in interactive visuals in contemporary dance performances. These visuals often rely on embodied interaction techniques, such as motion capture or biosignal sensors. However, there is a lack of research into how audience members experience these interactive visuals, and how to enhance that experience. We conducted an audience study, involving four different dance performances. Each of the performances explored a different approach for interaction involving visuals. We collected data from audience members, regarding their experience of the performances, using questionnaires and interviews. The analysis of this data allows us to identify implications for design: balancing trade-offs within a mapping clarity spectrum; connecting layers; visuals as co-creative mediator; defined territory and individuality of visuals; exploration of perspective shift and abstract fragmentation. We argue that these considerations are relevant for designers of systems visualizing embodied interaction, not just for dance, but also for other related applications.
Online Cluster Drift Detection for Novelty Detection in Data Streams

A major challenge in data stream applications is the change in the target variable over time in unexpected ways, a phenomenon called concept drift (CD). Another challenge is the emergence of novel classes, soliciting novelty detection (ND) by, e.g., one-class or semi-supervised classification. But, in online ND, these two challenges interfere with each other although they should be dealt with jointly. We present the cluster drift detection (CDD) algorithm that, using a single hyper-parameter, performs offline clustering to learn the diverse normal profile, and detects online whether a never-seen-before example is novel or normal using a multivariate statistical test. If it is normal, the CDD uses this example to update the normal-profile cluster, enabling continuous CD monitoring. Experimental results using popular real-world and synthetic data sets, as well as a precision agriculture data set of banana plants under water stress and a COVID-19 data set demonstrate that the CDD algorithm: 1) distinguishes between normal and novel concepts more accurately than state-of-the-art algorithms, 2) provides information about why specific novel concepts are misdetected, and 3) is more robust to the complexity, drift, and noise in the problem than other algorithms.
A Service Sustainable Live Migration Strategy for Multiple Virtual Machines in Cloud Data Centers

Virtual machine (VM) migration is an indispensable aspect of a virtualized cloud environment. It assists in resource management by dynamically relocating VMs from one physical machine to another. This is an essential aspect especially for big data applications that are prone to variable workloads and often demand relocation of resources in terms of VMs. However, such applications not only experience stochastic workloads but also have stringent requirements on the maximum tolerable latency. To address such issues, VMs are often relocated using live VM migration. VM migration is associated with overheads, hence, in this paper, we propose a modified serial migration strategy to migrate multiple VMs based on the pre-copy live migration technique. We propose to interleave the pre-copy stages in such a way that a balance is achieved between the migration time and downtime overheads. The proposed technique is compared with the state-of-the-art serial, parallel, and improved serial migration strategies. Concerning downtime, the proposed approach performs exceptionally well compared to both serial and improved serial methods. The downtime of the proposed scheme and parallel are comparative for read-intensive applications (low dirtying rates). However, for write-intensive applications (high dirtying rates) the former significantly outperforms the latter. The migration time performance of the proposed scheme is observed to be much better than that of the parallel technique and is slightly higher than those of serial and improved serial techniques.
Towards Locality Similarity Preserving to 3D Human Pose Estimation

Estimating 3D human pose from an annotated or detected 2D pose in a single RGB image is a challenging problem. A successful way to address this problem is the example-based approach. The existing example-based approaches often calculate a global pose error to search a single match 3D pose from the source library. This way fails to capture the local deformations of human pose and highly dependent on a large training set. To alleviate these issues, we propose a simple example-based approach with locality similarity preserving to estimate 3D human pose. Specifically, first of all, we split an annotated or detected 2D pose into 2D body parts with kinematic priors. Then, to recover the 3D pose from these 2D body parts, we recombine a 3D pose by using 3D body parts that are split from the 3D pose candidates. Note that joints in the combined 3D parts are refined by a weighted searching strategy during the inference. Moreover, to increase the search speed, we propose a candidate selecting mechanism to narrow the original source data. We evaluate our approach on three well-design benchmarks, including Human3.6M, HumanEva-I, and MPII. The extensive experimental results show the effectiveness of our approach. Specifically, our approach achieves better performance than compared approaches while using fewer training samples.
SE–RWNN: an synergistic evolution and randomly wired neural network‐based model for adaptive underwater image enhancement

Under water images are likely to suffer from severe degradation such as colour distortion, low contrast, and fuzz content, caused by the absorption and scattering effects of the water. To improve the visual appearance of the image, the authors present an adaptive algorithm for effective underwater image enhancement using a randomly wired neural network (RWNN) and synergistic evolution (SE). In doing so, they sequentially conduct colours adjustment, contrast improvement and luminance enhancement while enhancing details by an edge-preserving technique. To set up the system, they develop a multi-strategy cooperating evolution algorithm to figure out the optimal parameter values. Extensive experimental results show that the proposed model improves both subjectively and quantitatively the quality of underwater images.
Edge‐guided single facial depth map super‐resolution using CNN

In recent years, consumer depth cameras have been widely used in digital entertainment and human-machine interaction due to the advantages of real-time performance and low cost. Facial depth maps have shown great potential in 3D-face-related studies. However, disadvantages of low resolution and precision limit its further applications. In this work, the authors propose an edge-guided convolutional neural network for single facial depth map super-resolution. It consists of two parts: an edge prediction sub-network and a depth reconstruction sub-network. The edge prediction sub-network generates an edge guidance map to guide the depth reconstruction sub-network to recover sharp edges and fine structures. Effective data augmentation methods are proposed as well. The network is patch-based and able to cope with any size of the input depth maps. In addition, it is insensitive to the face pose since the synthetic training dataset they generated covers a wide range of face poses. The proposed method is validated with three datasets including a synthetic facial depth data set, a real Kinect V2 facial depth data set and Middlebury Stereo Data set. Experimental results show that it outperforms the state-of-the-art methods on all the three data sets.
ACT-Detector: Adaptive channel transformation-based light-weighted detector for adversarial attacks

With the extensive application of deep neural networks (DNNs) in computer vision tasks, the vulnerability of such systems to carefully crafted adversarial examples has attracted increasing attention. Although various adversarial defense methods have been proposed to improve the robustness of DNNs, the detection of adversarial examples remains challenging. Previous studies have demonstrated that adversarial examples are sensitive to channel transformation operations, such as rotate and resize, whereas clean examples are immune to them. The detection efficiency heavily relies on the numbers and types of transformation operations. Thus, we propose an adaptive channel transformation-based light-weighted detector known as the ACT-Detector, which selects approximately optimal channel transformation types and the minimal channel transformation number through a cuckoo search. The ACT-Detector can not only detect adversarial and clean examples but can also identify the type of attack, such as white-box and black-box attacks. Comprehensive experiments were performed on the MNIST, CIFAR10, and ImageNet datasets to verify the detection efficiency of the ACT-Detector. The ACT-Detector outperformed a detector containing 45 channel transformations, using only five channel transformations to achieve 99.05% and 98.8% detection rates on the MNIST and CIFAR10 datasets, respectively. This is because the ACT-Detector could select channels with different features, whereas the features in the 45 channels were redundant. By reducing the channel number, the total time required for the ACT-Detector to detect one example was approximately one-quarter that required for the detector with 45 channels during testing. Thus, the proposed detector is proven to be effective and efficient, which is valuable for the detection of adversarial examples.
Automatic Object Recoloring Using Adversarial Learning

We propose a novel method for automatic object recoloring based on Generative Adversarial Networks (GANs). The user can simply give commands of the form recolor <;object> to <;color> which will be executed without any need of manual edit. Our approach takes advantage of pre-trained object detectors and saliency mask segmentation networks. The segmented mask of the given object along with the target color and the original image form the input to the GAN. The use of cycle consistency loss ensures the realistic look of the results. To our best knowledge, this is the first algorithm where the automatic recoloring is only limited by the ability of the mask extractor to map a natural language tag to a specific object in the image (several hundred object types at the time of this writing). For a performance comparison, we also adapted other state of the art methods to perform this task. We found that our method had consistently yielded qualitatively better recoloring results.
Robust and efficient swarm communication topologies for hostile environments

Swarm Intelligence-based optimization techniques combine systematic exploration of the search space with information available from neighbors and rely strongly on communication among agents. These algorithms are typically employed to solve problems where the function landscape is not adequately known and there are multiple local optima that could result in premature convergence for other algorithms. Applications of such algorithms can be found in communication systems involving design of networks for efficient information dissemination to a target group, targeted drug-delivery where drug molecules search for the affected site before diffusing, and high-value target localization with a network of drones. In several of such applications, the agents face a hostile environment that can result in loss of agents during the search. Such a loss changes the communication topology of the agents and hence the information available to agents, ultimately influencing the performance of the algorithm. In this paper, we present a study of the impact of loss of agents on the performance of such algorithms as a function of the initial network configuration. We use particle swarm optimization to optimize an objective function with multiple sub-optimal regions in a hostile environment and study its performance for a range of network topologies with loss of agents. The results reveal interesting trade-offs between efficiency, robustness, and performance for different topologies that are subsequently leveraged to discover general properties of networks that maximize performance. Moreover, networks with small-world properties are seen to maximize performance under hostile conditions.
Event Detection in Soccer Video Based on Self-Attention

Sports big data technology has become an important technological means of modern sports competition. This paper realizes a novel soccer video event detection algorithm based on self-attention. It extracts key frames through self-attention mechanism, and then obtains the characteristics of time window level through NetVLAD network. Finally, each video clip is classified into 4 types of events (goals, red/yellow card, substitutions and others). The experimental results show that with the introduction of the self-attention mechanism, the classification accuracy on the SoccerNet data set has improved from 67.2% to 74.3%.
Self-Attention-Based Convolutional Neural Networks for Sentence Classification

Sentence classification is a challenging task. The research on convolutional neural networks combined with the attention mechanism for sentence classification is not yet complete, especially the performance of multi-classification tasks needs to be improved. In this paper, we propose a self-attention-based convolutional neural network (SACNN) for sentence classification, which consists of two self-attention layers and a convolutional neural network. We conducted multiple experiments on seven benchmark datasets. Experimental results show that the proposed model can achieve up to 0.4%-1.4% higher accuracy than other CNN-based models, and outperform other CNN-based models on five out of seven tasks.
OmniSoft: A Design Tool for Soft Objects by Example

Softness is one of the most important factors in human tactile perception. With recent advances in 3Dprinting, there has been significant progress in fabricating compliant objects. However, existing methods typically remain inaccessible to end-users, mainly due to the separation between designing shapes and setting printing parameters to achieve desired softness, resulting in the exclusion of its customization in early design processes. In this work, we contribute an end-to-end design tool that takes a design-by-example approach: given a 3D model, a user can specify the region of interest and a level of softness, by shopping everyday objects as a reference. The tool then generates both geometry and 3D printing parameters to reproduce the desired softness, which can be fabricated using low-cost FDM 3D printing and materials for it. We also provide a data-driven pipeline to enable other compliance modeling methods to be generalized within our design tool. In two user studies, we demonstrated that users could easily locate existing reference objects’ softness to a 3D printed object. In a design session, end-users successfully used OmniSoft to design augmented functions.
The Selection of Bayesian Polynomial Regression with INLA by using DIC, WAIC and CPO

Abstract The polynomial regression model is extended the multiple linear regression. The selection of Bayesian polynomial regression model with INLA required Criterion. Criterion is using the measure fit model with the available data. There are three criteria, namely DIC, WAIC and CPO. The smaller criterion value from DIC, WAIC and CPO on a model show the best Bayesian polynomial regression model with INLA.
Research on Physical Education and Training Based on the Theoretical Teaching of Computer Three-Dimensional Animation Technology

Abstract At present, the teaching methods of physical education in China are still based on the traditional face-to-face skills. However, with the rapid development of the computer field, computer-based three-dimensional animation technology is considered to be a better teaching method. This study discusses the advantages, current situation and specific application methods of three-dimensional animation technology in the field of physical education at the present stage. The results show that the establishment of three-dimensional animation simulation demonstration teaching is of practical significance and is worthy of great development.
Internet of things for flood and landslide early warning

Abstract The purpose of this study is to improve the use of technology for the occurrence of a disaster as a form of anticipation in a disaster-prone area and tourist area. The method used in this research was a descriptive research method where the data obtained from events that occur in a particular region. The results of this study indicate that technological innovation is needed to monitor floods and landslides because based on the data from National Disaster Management Agency (BNPB) on the occurrence of disasters in Indonesia. There are still many areas that prone to natural disasters especially floods and landslides. The important role of the technology is to increase the level of confidence and security of tourists towards the potential for a disaster that occurs and can improve the tourism industry economy.
Real-time dual-channel speckle interferometry based on an improved dual-observation configuration with spatial phase-shifting

This paper develops a real-time dual-channel speckle interferometry system based on an improved dual-observation configuration to address the issue of simultaneous measurement of the out-of-plane displacement and slope. A dual-observation configuration combined with an additional light path is used to provide the possibility to create two measurement channels corresponding to the out-of-plane displacement and slope, respectively. In the system, two symmetric observation light paths are slightly displaced from each other by a deliberate tilt of the plane mirror to introduce an adjustable lateral shear. One of the two observation beams is shared by both measurement channels to improve the system's stability. Besides, using the spatial carrier phase-shifting technique induced by multi-aperture, the real-time phase retrieval is achieved for simultaneous measurement of the dynamic out-of-plane displacement and slope. Both the experimental investigation and the non-destructive testing (NDT) application are finally presented to demonstrate the feasibility and effectiveness of the proposed system.
Algebraic Decoding of Quasi-Reversible BCH Codes Using Band Matrices

A Bose-Chaudhuri-Hocquenghem (BCH) is called quasi-reversible if there are consecutive elements a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ,⋯,a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> in the defining set, where a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> is a negative integer and a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> is a positive integer. A matrix in band form presented in this article involves the use of the Newton identities and symmetric polynomial identities. This matrix is the coefficient matrix of a linear system. The solution to such a linear system is the coefficients of an error-locator polynomial for quasi-reversible BCH codes. Its computational complexity is significantly lower than BCH decoding using the linear system with coefficient matrix in column echelon form. This article also proposes a new partial syndrome matrix with radical locators and a lot of zeros. Such a matrix is still in band form. The expansion of its matrix determinant is exactly a radical-locator polynomial. Compared to the former radical-locator polynomials in the literature, the newly discovered radical-locator polynomial not only requires less storage memory but also results in computational benefits. Surprisingly, the resulting polynomials are much sparser than those for the narrow-sense BCH codes. Finally, a complete algebraic decoding algorithm for quasi-reversible BCH codes is provided. The error-locator and radical-locator polynomials of degree less than or equal to 5 are listed.
Sketch-specific data augmentation for freehand sketch recognition

Sketch recognition remains a significant challenge due to the limited training data and the substantial intra-class variance of freehand sketches for the same object. Conventional methods for this task often rely on the availability of the temporal order of sketch strokes, additional cues acquired from different modalities and supervised augmentation of sketch datasets with real images, which also limit the applicability and feasibility of these methods in real scenarios. In this paper, we propose a novel sketch-specific data augmentation (SSDA) method that leverages the quantity and quality of the sketches automatically. From the aspect of quantity, we introduce a Bezier pivot based deformation (BPD) strategy to enrich the training data. Towards quality improvement, we present a mean stroke reconstruction (MSR) approach to generate a set of novel types of sketches with smaller intra-class variances. Both of these solutions are unrestricted from any multi-source data and temporal cues of sketches. Furthermore, we show that some recent deep convolutional neural network models that are trained on generic classes of real images can be better choices than most of the elaborate architectures that are designed explicitly for sketch recognition. As SSDA can be integrated with any convolutional neural networks, it has a distinct advantage over the existing methods. Our extensive experimental evaluations demonstrate that the proposed method achieves the state-of-the-art results (84.27%) on the TU-Berlin dataset, outperforming the human performance by a remarkable 11.17% increase. Finally, more experiments show the practical value of our approach for the task of sketch-based image retrieval.