Reviewerly/exHarmony huggingface dataset:
What, How, and Why are Visual Assets Used in Industrial Augmented Reality? A Systematic Review and Classification in Maintenance, Assembly, and Training (From 1997 to 2019)

Industrial Augmented Reality (iAR) has demonstrated its advantages to communicate technical information in the fields of maintenance, assembly, and training. However, literature is scattered among different visual assets (i.e., AR visual user interface elements associated with a real scene). In this work, we present a systematic literature review of visual assets used in these industrial fields. We searched five databases, initially finding 1757 papers. Then, we selected 122 iAR papers from 1997 to 2019 and extracted 348 visual assets. We propose a classification for visual assets according to (i) what is displayed, (ii) how it conveys information (frame of reference, color coding, animation), and, (iii) why it is used. Our review shows that product models, text and auxiliary models are, in order, the most common, with each most often used to support operating, checking and locating tasks respectively. Other visual assets are scarcely used. Product and auxiliary models are commonly rendered world-fixed, color coding is not used as often as expected, while animations are limited to product and auxiliary model. This survey provides a snapshot of over 20 years of literature in iAR, useful to understand established practices to orientate in iAR interface design and to present future research directions.
Detecting the shuttlecock for a badminton robot: A YOLO based approach

The ability to identify objects of interest from digital visual signals is critical for many applications of intelligent systems. For such object detection task, accuracy and computational efficiency are two important aspects, especially for applications with real-time requirement. In this paper, we study shuttlecock detection problem of a badminton robot, which is very challenging since the shuttlecock often moves fast in complex contexts, and must be detected precisely in real time so that the robot can plan and execute its following movements. To this end, we propose two novel variants of Tiny YOLOv2, a well-known deep learning based detector. We first modify the loss function to adaptively improve the detection speed for small objects such as shuttlecock. We then modify the architecture of Tiny YOLOv2 to retain more semantic information of small objects, so as to further improve the performance. Experimental results show that the proposed networks can achieve high detection accuracy with the fastest speed, compared with state-of-the-art deep detectors such as Faster R-CNN, SSD, Tiny YOLOv2, and YOLOv3. Our methods could be potentially applied to other tasks of detecting high-speed small objects.
Neural supersampling for real-time rendering

Due to higher resolutions and refresh rates, as well as more photorealistic effects, real-time rendering has become increasingly challenging for video games and emerging virtual reality headsets. To meet this demand, modern graphics hardware and game engines often reduce the computational cost by rendering at a lower resolution and then upsampling to the native resolution. Following the recent advances in image and video superresolution in computer vision, we propose a machine learning approach that is specifically tailored for high-quality upsampling of rendered content in real-time applications. The main insight of our work is that in rendered content, the image pixels are point-sampled, but precise temporal dynamics are available. Our method combines this specific information that is typically available in modern renderers (i.e., depth and dense motion vectors) with a novel temporal network design that takes into account such specifics and is aimed at maximizing video quality while delivering real-time performance. By training on a large synthetic dataset rendered from multiple 3D scenes with recorded camera motion, we demonstrate high fidelity and temporally stable results in real-time, even in the highly challenging 4 × 4 upsampling scenario, significantly outperforming existing superresolution and temporal antialiasing work.
Distributed Learning for Automatic Modulation Classification in Edge Devices

Automatic modulation classification (AMC) is a typical technology for identifying different modulation types, which has been widely applied into various scenarios. Recently, deep learning (DL), one of the most advanced classification algorithms, has been applied into AMC. However, these previously proposed AMC methods are centralized in nature, i.e., all training data must be collected together to train the same neural network. In addition, they are generally based on powerful computing devices and may not be suitable for edge devices. Thus, a distributed learning-based AMC (DistAMC) method is proposed, which relies on the cooperation of multiple edge devices and model averaging (MA) algorithm. When compared with the centralized AMC (CentAMC), there are two advantages of the DistAMC: the higher training efficiency and the lower computing overhead, which are very consistent with the characteristics of edge devices. Simulation results show that there are slight performance gap between the DistAMC and the CentAMC, and they also have similar convergence speed, but the consumed training time per epoch in the former method will be shorter than that on the latter method, if the low latency and the high bandwidth are considered in model transmission process of the DistAMC. Moreover, the DistAMC can combine the computing power of multiple edge devices to reduce the computing overhead of a single edge device in the CentAMC.
Efficient and secure opto-cryptosystem for color images using 2D logistic-based fractional Fourier transform

Nowadays, secure multimedia applications become a hot research issue because of rapid evolution in the application of multimedia over the Internet and wireless communications networks. Several researchers have introduced many chaos-based encryption methods to achieve data robustness and security communication. But, there exist several issues encountered in those methods regarding their fragility against differential attacks, delayed throughput, and inadmissible data expansibility. From this perspective, the paper presents an effective and secure opto color image cryptosystem using 2D Fractional Fourier Transform (2D FrFT) and 2D logistic mapping (2D LM). The proposed opto 2D LM-based 2D FrFT color image cryptosystem employed two confusion stages in the time and optical-based 2D FrFT using the 2D LM for color RGB images. Initially, the 2D LM is employed to shuffle the pixel positions of the RGB plainimage. After that, the optical-based FrFT is employed on the 2D LM-based shuffled RGB image. The complex transformed RGB image is again shuffled using the 2D LM, and finally, optically transformed using the FrFT. Furthermore, the encryption characteristics are enhanced by utilizing the 2D-FrFT angles and orders as extra encryption keys in addition to double random phase masks. The proposed opto 2D LM-based 2D FrFT color image cryptosystem is tested through the known security key indicators such as visual inspecting, entropy testing, histogram testing, cipher quality measures, noise testing, computational time analysis, occlusion attack analysis, and differential testing. The simulation results illustrated a superiority improvement in the proposed opto 2D LM-based 2D FrFT color image cryptosystem compared to the recent state-of-the-art related works.
Effects of a formative assessment-based contextual gaming approach on students’ digital citizenship behaviours, learning motivations, and perceptions

Several previous studies have indicated that secondary school students face problems in digital situations. Digital citizenship is therefore an essential conception for fostering students' correct behaviour and safe daily life habits while using computer and communication technologies. However, scholars have indicated the challenges of promoting students' digital citizenship owing to the lack of opportunities to situate them in decision-making situations. The advancement of computer and multimedia technologies has encouraged an increasing number of studies on digital game-based learning, which provides students with opportunities to experience various situations, and to cope with problems encountered in daily life. Several previous studies have indicated that digital game-based learning environments with proper learning strategies might help students realize the importance and meaning of digital citizenship. To address this issue, this study proposes a formative assessment-based contextual gaming approach to guide students to make decisions and to monitor their learning during the gaming process. A digital game-based learning environment was developed based on this approach, and a quasi-experiment on the digital citizenship topic was conducted in northern Thailand to evaluate its effectiveness. The experimental results show that the proposed approach not only enhanced the students’ digital citizenship behaviours, but also promoted their motivations and perceptions.
Cyberphysical Blockchain-Enabled Peer-to-Peer Energy Trading

Scalability and security problems with centralized architecture models in cyberphysical systems have provided opportunities for blockchain-based distributed models. A decentralized energy-trading system takes advantage of various sources and effectively coordinates the energy to ensure the optimal utilization of available resources. Three blockchainbased energy-trading models are proposed to overcome the technical challenges and market barriers as well as enhance the adoption of this disruptive technology.
Deep Learning Applied for Histological Diagnosis of Breast Cancer

Deep learning, as one of the currently most popular computer science research trends, improves neural networks, which has more and deeper layers allowing higher abstraction levels and more accurate data analysis. Although deep convolutional neural networks, as a deep learning algorithm, has recently achieved promising results in data analysis, the requirement for a large amount of data prevents its use in medical data analysis since it is challenging to obtain data from the medical field. Breast cancer is a common cancer in women. To diagnose this kind of cancer, breast cell shapes in histopathology images should be examined by senior pathologists. The number of pathologists per population in the world is not enough, especially in Africa, and human mistake may occur in diagnosis procedure. After the evaluation of deep learning methods and algorithms in breast histological data processing, we tried to improve the current systems' accuracy. As a result, this study proposes two effective deep transfer learning-based models, which rely on pre-trained DCNN using a large collection of ImageNet dataset images that improve current state-of-the-art systems in both binary and multiclass classification. We transfer pre-trained weights of the ResNet50 and DesneNet121 on the Imagenet as initial weights and fine-tune these models with a deep classifier with data augmentation to detect various malignant and benign samples tissues in the two categories of binary classification and multiclass classification. The proposed models have been examined with optimized hyperparameters in magnification-dependent and magnification-independent classification modes. In the multiclass classification, the proposed system achieved up to 98% accuracy. As for binary classification, the proposed system provides up to 100% accuracy. The results outperform previous studies accuracies in all defined performance metrics in breast cancer CAD systems from histological images.
Multi-objective adaptive differential evolution for SVM/SVR hyperparameters selection

Parameters Selection Problem (PSP) is a relevant and complex optimization issue in Support Vector Machine (SVM) and Support Vector Regression (SVR), looking for obtaining an optimal set of hyperparameters. In our case, the optimization problem is addressed to obtain models that minimize the number of support vectors and maximize generalization capacity. However, to obtain accurate and low complexity solutions, defining an adequate kernel function and the SVM/SVR’s hyperparameters are necessary, which currently represents a relevant research topic. To tackle this problem, this work proposes a multi-objective metaheuristic named Adaptive Parameter control with Mutant Tournament Multi-Objective Differential Evolution (APMT-MODE). Its performance is first tested in a series of benchmarks for classification and regression problems using simple kernels such as Gaussian and polynomial kernels. In both cases, the APMT-MODE is able to yield more precise and more straightforward solutions using simple kernels. Then, the approach is used on a real case study to create a welding bead depth and width SVR models for a Gas Metal Arc Welding (GMAW) process. Additionally, a study on kernel functions was developed in terms of computational effort, aiming to assess its performance for embedded systems applications.
Universally Composable Security

This work presents a general framework for describing cryptographic protocols and analyzing their security. The framework allows specifying the security requirements of practically any cryptographic task in a unified and systematic way. Furthermore, in this framework the security of protocols is preserved under a general composition operation, called universal composition. The proposed framework with its security-preserving composition operation allows for modular design and analysis of complex cryptographic protocols from simpler building blocks. Moreover, within this framework, protocols are guaranteed to maintain their security in any context, even in the presence of an unbounded number of arbitrary protocol sessions that run concurrently in an adversarially controlled manner. This is a useful guarantee, which allows arguing about the security of cryptographic protocols in complex and unpredictable environments such as modern communication networks.
FLIP

Image quality measures are becoming increasingly important in the field of computer graphics. For example, there is currently a major focus on generating photorealistic images in real time by combining path tracing with denoising, for which such quality assessment is integral. We present FLIP, which is a difference evaluator with a particular focus on the differences between rendered images and corresponding ground truths. Our algorithm produces a map that approximates the difference perceived by humans when alternating between two images. FLIP is a combination of modified existing building blocks, and the net result is surprisingly powerful. We have compared our work against a wide range of existing image difference algorithms and we have visually inspected over a thousand image pairs that were either retrieved from image databases or generated in-house. We also present results of a user study which indicate that our method performs substantially better, on average, than the other algorithms. To facilitate the use of FLIP, we provide source code in C++, MATLAB, NumPy/SciPy, and PyTorch.
Structure-invariant testing for machine translation

In recent years, machine translation software has increasingly been integrated into our daily lives. People routinely use machine translation for various applications, such as describing symptoms to a foreign doctor and reading political news in a foreign language. However, the complexity and intractability of neural machine translation (NMT) models that power modern machine translation make the robustness of these systems difficult to even assess, much less guarantee. Machine translation systems can return inferior results that lead to misunderstanding, medical misdiagnoses, threats to personal safety, or political conflicts. Despite its apparent importance, validating the robustness of machine translation systems is very difficult and has, therefore, been much under-explored.
FADNet: A Fast and Accurate Network for Disparity Estimation

Deep neural networks (DNNs) have achieved great success in the area of computer vision. The disparity estimation problem tends to be addressed by DNNs which achieve much better prediction accuracy in stereo matching than traditional hand-crafted feature based methods. On one hand, however, the designed DNNs require significant memory and computation resources to accurately predict the disparity, especially for those 3D convolution based networks, which makes it difficult for deployment in real-time applications. On the other hand, existing computation-efficient networks lack expression capability in large-scale datasets so that they cannot make an accurate prediction in many scenarios. To this end, we propose an efficient and accurate deep network for disparity estimation named FADNet with three main features: 1) It exploits efficient 2D based correlation layers with stacked blocks to preserve fast computation; 2) It combines the residual structures to make the deeper model easier to learn; 3) It contains multi-scale predictions so as to exploit a multi-scale weight scheduling training technique to improve the accuracy. We conduct experiments to demonstrate the effectiveness of FADNet on two popular datasets, Scene Flow and KITTI 2015. Experimental results show that FADNet achieves state-of-the-art prediction accuracy, and runs at a significant order of magnitude faster speed than existing 3D models. The codes of FADNet are available at https://github.com/HKBU-HPML/FADNet.
A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound

The automatic segmentation of breast tumors in ultrasound (BUS) has recently been addressed using convolutional neural networks (CNN). These CNN-based approaches generally modify a previously proposed CNN architecture or they design a new architecture using CNN ensembles. Although these methods have reported satisfactory results, the trained CNN architectures are often unavailable for reproducibility purposes. Moreover, these methods commonly learn from small BUS datasets with particular properties, which limits generalization in new cases. This paper evaluates four public CNN-based semantic segmentation models that were developed by the computer vision community, as follows: (1) Fully Convolutional Network (FCN) with AlexNet network, (2) U-Net network, (3) SegNet using VGG16 and VGG19 networks, and (4) DeepLabV3+ using ResNet18, ResNet50, MobileNet-V2, and Xception networks. By transfer learning, these CNNs are fine-tuned to segment BUS images in normal and tumoral pixels. The goal is to select a potential CNN-based segmentation model to be further used in computer-aided diagnosis (CAD) systems. The main significance of this study is the comparison of eight well-established CNN architectures using a more extensive BUS dataset than those used by approaches that are currently found in the literature. More than 3000 BUS images acquired from seven US machine models are used for training and validation. The F1-score (F1s) and the Intersection over Union (IoU) quantify the segmentation performance. The segmentation models based on SegNet and DeepLabV3+ obtain the best results with F1s>0.90 and IoU>0.81. In the case of U-Net, the segmentation performance is F1s=0.89 and IoU=0.80, whereas FCN-AlexNet attains the lowest results with F1s=0.84 and IoU=0.73. In particular, ResNet18 obtains F1s=0.905 and IoU=0.827 and requires less training time among SegNet and DeepLabV3+ networks. Hence, ResNet18 is a potential candidate for implementing fully automated end-to-end CAD systems. The CNN models generated in this study are available to researchers at https://github.com/wgomezf/CNN-BUS-segment, which attempts to impact the fair comparison with other CNN-based segmentation approaches for BUS images.
An adaptive particle swarm optimizer with decoupled exploration and exploitation for large scale optimization

As a form of evolutionary computation, particle swarm optimization is less effective in large scale optimization since it is unable to effectively balance exploration and exploitation. To address this problem, first, a learning structure decoupling exploration and exploitation is proposed. This helps simultaneously and independently managing exploration and exploitation in different components. Second, following the proposed learning structure, two novel learning strategies are developed. On the one hand, a local sparseness degree measurement in fitness landscape is proposed to estimate the congestion and distribution of particles, based on which an exploration strategy is built by guiding particles to sparse areas. On the other hand, an adaptive exploitation strategy is developed which can effectively adjust the fitness differences between exemplars and updated particles during the optimization process by employing a multi-swarm strategy and an adaptive sub-swarm size adjustment. Finally, by embedding the two learning strategies into the proposed learning structure, an adaptive particle swarm optimizer with decoupled exploration and exploitation is proposed. Thanks to the novel balancing strategy of exploration and exploitation, the two functions in the proposed algorithm can be independently and simultaneously managed. Furthermore, theoretical analyses are put forward to prove the convergence and computational complexity of the proposed algorithm. Comprehensive experiments are conducted based on the large scale optimization benchmarks from CEC 2010 and CEC 2013 and six state-of-the-art large scale optimization evolutionary algorithms, the results demonstrate the effectiveness of the proposed learning strategies and the competitive performance of the proposed algorithm.
PAIP 2019: Liver cancer segmentation challenge

Pathology Artificial Intelligence Platform (PAIP) is a free research platform in support of pathological artificial intelligence (AI). The main goal of the platform is to construct a high-quality pathology learning data set that will allow greater accessibility. The PAIP Liver Cancer Segmentation Challenge, organized in conjunction with the Medical Image Computing and Computer Assisted Intervention Society (MICCAI 2019), is the first image analysis challenge to apply PAIP datasets. The goal of the challenge was to evaluate new and existing algorithms for automated detection of liver cancer in whole-slide images (WSIs). Additionally, the PAIP of this year attempted to address potential future problems of AI applicability in clinical settings. In the challenge, participants were asked to use analytical data and statistical metrics to evaluate the performance of automated algorithms in two different tasks. The participants were given the two different tasks: Task 1 involved investigating Liver Cancer Segmentation and Task 2 involved investigating Viable Tumor Burden Estimation. There was a strong correlation between high performance of teams on both tasks, in which teams that performed well on Task 1 also performed well on Task 2. After evaluation, we summarized the top 11 team’s algorithms. We then gave pathological implications on the easily predicted images for cancer segmentation and the challenging images for viable tumor burden estimation. Out of the 231 participants of the PAIP challenge datasets, a total of 64 were submitted from 28 team participants. The submitted algorithms predicted the automatic segmentation on the liver cancer with WSIs to an accuracy of a score estimation of 0.78. The PAIP challenge was created in an effort to combat the lack of research that has been done to address Liver cancer using digital pathology. It remains unclear of how the applicability of AI algorithms created during the challenge can affect clinical diagnoses. However, the results of this dataset and evaluation metric provided has the potential to aid the development and benchmarking of cancer diagnosis and segmentation.