Reviewerly/exHarmony huggingface dataset:
Suboptimal Bayesian state estimators for linear high-dimensional dynamic processes

This paper presents a new state estimation method to ease the heavy computational loads of the Kalman filter (KF) when applied for the processes of large dimensions. The key idea of the proposed methodology is to divide the whole high-dimensional state vector into multiple low-dimensional blocks and suppress the errors introduced by minimizing the corresponding Kullback–Leibler (KL) divergence. Without losing generality, two different scenarios depending on the state dynamics are considered. One is that the state transition matrix is block-diagonal, and the other is not. By doing these, prior knowledge about the processes can be incorporated into, and more importantly, a satisfying trade-off between computational cost and estimation accuracy can be built-in. Simulations results on a numerical model, and a practice-oriented example demonstrate that the proposed method costs much less computational resources than the KF for high-dimensional processes and yields significant improvements than the existing fast Kalman-like estimators, including the ensemble KF (EnKF).
REM: A simplified revised entropy image reconstruction for photonics integrated interference imaging system

The photonics integrated interference imaging system (PIIIS) is a recently developed far-field imaging approach aimed at compacting structures and acquiring high imaging performance. In this regard, to acquire higher imaging quality, several lens patterns have been developed, providing low-frequency intensive but high-frequency sparse measurements of the visibility function. However, sparse measurements at high frequency produce Gibbs-ringing artifacts in the image reconstruction, severely damaging the PIIIS imaging quality. In this study, a new data penalty for revised entropy is proposed for PIIIS, and a simple reconstruction algorithm using the Newton method is developed. A reconstruction experiment adopting the hierarchical multistage pattern is implemented on resolution board (USAF1951) targets with bright and black backgrounds. The outstanding improvement in the experimental results proves the feasibility of suppressing the ringing artifacts in the images reconstructed in PIIIS. • PIIIS lenslet configurations introduce Gibbs-ringing artifacts in reconstructions. • A new revised entropy penalty is proposed to suppress Gibbs-ringing artifacts. • A simple REM is proposed to evaluate the features of revised entropy.
Deep Fusion of Appearance and Frame Differencing for Motion Segmentation

Motion segmentation is a technique to detect and localize class-agnostic motion in videos. This motion is assumed to be relative to a stationary background and usually originates from objects such as vehicles or humans. When the camera moves, too, frame differencing approaches that do not have to model the stationary background over minutes, hours, or even days are more promising compared to background subtraction methods. In this paper, we propose a Deep Convolutional Neural Network (DCNN) for multi-modal motion segmentation: the current image contributes with appearance information to distinguish between relevant and irrelevant motion and frame differencing captures the temporal information, which is the scene’s motion in-dependent of the camera motion. We fuse this information to receive an effective and efficient approach for robust motion segmentation. The effectiveness is demonstrated using the multi-spectral CDNet-2014 dataset that we re-labeled for motion segmentation. We specifically show that we can detect tiny moving objects significantly better compared to methods based on optical flow.
A Saliency Detection and Gram Matrix Transform-Based Convolutional Neural Network for Image Emotion Classification

Using the convolutional neural network (CNN) method for image emotion recognition is a research hotspot of deep learning. Previous studies tend to use visual features obtained from a global perspective and ignore the role of local visual features in emotional arousal. Moreover, the CNN shallow feature maps contain image content information; such maps obtained from shallow layers directly to describe low-level visual features may lead to redundancy. In order to enhance image emotion recognition performance, an improved CNN is proposed in this work. Firstly, the saliency detection algorithm is used to locate the emotional region of the image, which is served as the supplementary information to conduct emotion recognition better. Secondly, the Gram matrix transform is performed on the CNN shallow feature maps to decrease the redundancy of image content information. Finally, a new loss function is designed by using hard labels and probability labels of image emotion category to reduce the influence of image emotion subjectivity. Extensive experiments have been conducted on benchmark datasets, including FI (Flickr and Instagram), IAPSsubset, ArtPhoto, and Abstract. The experimental results show that compared with the existing approaches, our method has a good application prospect.
Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representations

Large-scale pretraining of visual representations has led to state-of-the-art performance on a range of benchmark computer vision tasks, yet the benefits of these techniques at extreme scale in complex production systems has been relatively unexplored. We consider the case of a popular visual discovery product, where these representations are trained with multi-task learning, from use-case specific visual understanding (e.g. skin tone classification) to general representation learning for all visual content (e.g. embeddings for retrieval). In this work, we describe how we (1) generate a dataset with over a billion images via large weakly-supervised pretraining to improve the performance of these visual representations, and (2) leverage Transformers to replace the traditional convolutional backbone, with insights into both system and performance improvements, especially at 1B+ image scale. To support this backbone model, we detail a systematic approach to deriving weakly-supervised image annotations from heterogenous text signals, demonstrating the benefits of clustering techniques to handle the long-tail distribution of image labels. Through a comprehensive study of offline and online evaluation, we show that large-scale Transformer-based pretraining provides significant benefits to industry computer vision applications. The model is deployed in a production visual shopping system, with 36% improvement in top-1 relevance and 23% improvement in click-through volume. We conduct extensive experiments to better understand the empirical relationships between Transformer-based architectures, dataset scale, and the performance of production vision systems.
DAG-FL: Direct Acyclic Graph-based Blockchain Empowers On-Device Federated Learning

Due to the distributed characteristics of Federated Learning (FL), the vulnerability of global model and coordination of devices are the main obstacle. As a promising solution of decentralization, scalability and security, leveraging blockchain in FL has attracted much attention in recent years. However, the traditional consensus mechanisms designed for blockchain like Proof of Work (PoW) would cause extreme resource consumption, which reduces the efficiency of FL greatly, especially when the participating devices are wireless and resource-limited. In order to address device asynchrony and anomaly detection in FL while avoiding the extra resource consumption caused by blockchain, this paper introduces a framework for empowering FL using Direct Acyclic Graph (DAG)-based blockchain systematically (DAG-FL). Accordingly, DAG-FL is first introduced from a three-layer architecture in details, and then two algorithms DAG-FL Controlling and DAG-FL Updating are designed running on different nodes to elaborate the operation of DAG-FL consensus mechanism. The extensive simulations show that DAG-FL can achieve the better performance in terms of training efficiency and model accuracy compared with the typical existing on-device federated learning systems as the benchmarks.
Named Entity Correction in Neural Machine Translation Using the Attention Alignment Map

Neural machine translation (NMT) methods based on various artificial neural network models have shown remarkable performance in diverse tasks and have become mainstream for machine translation currently. Despite the recent successes of NMT applications, a predefined vocabulary is still required, meaning that it cannot cope with out-of-vocabulary (OOV) or rarely occurring words. In this paper, we propose a postprocessing method for correcting machine translation outputs using a named entity recognition (NER) model to overcome the problem of OOV words in NMT tasks. We use attention alignment mapping (AAM) between the named entities of input and output sentences, and mistranslated named entities are corrected using word look-up tables. The proposed method corrects named entities only, so it does not require retraining of existing NMT models. We carried out translation experiments on a Chinese-to-Korean translation task for Korean historical documents, and the evaluation results demonstrated that the proposed method improved the bilingual evaluation understudy (BLEU) score by 3.70 from the baseline.
Unsupervised fuzzy multivariate symmetric uncertainty feature selection based on constructing virtual cluster representative

The data readability, complexity reduction of learning algorithms, and enhancing predictability are the most important reasons for using feature selection methods, especially when there exist lots of features. In recent years, unsupervised feature selection techniques are well explored. Among the various methods of feature selection, algorithms based on information theory are effective in selecting useful attributes and eliminating useless ones. Mutual information and symmetric uncertainty based methods can effectively estimate feature relevancy, but the use of bivariate measures ignores possible dependencies among more than two features. To address this limitation, this research introduces a novel unsupervised feature selection method, called Fuzzy Multivariate Symmetric Uncertainty-Feature Selection (FMSU-FS). The proposed method also overcomes the need for discretization for continuous features that causes information loss. To evaluate the effectiveness of the proposed method, FMSU is compared with conventional methods based on information theory. The experimental results on benchmark datasets show improvement of our approach in measuring clustering performance.
Unsupervised single-image dehazing using the multiple-scattering model

An unsupervised single-image dehazing method using a multiple scattering model is proposed. The method uses an undegraded atmospheric multiple scattering model and unsupervised learning to implement dehazing on single real-world image. The atmospheric multiple scattering model can avoid the influence of multiple scattering on the image and the unsupervised neural network can avoid the intensive operation on the data set. In this method, three unsupervised learning branches and a blur kernel estimation module estimate the scene radiation layer, transmission layer, atmospheric light layer, and blur kernel layer, respectively. In addition, the unsupervised loss function is constructed by prior knowledge to constrain the unsupervised branches. Finally, the output of the three unsupervised branches and the blur kernel estimation module synthesizes the haze image in a self-supervised way. A large number of experiments show that the proposed method has good performance in image dehazing compared with the six most advanced dehazing methods.
Exploring information systems security implications posed by BYOD for a financial services firm

This study explores information systems security implications posed by Bring Your Own Device concept in financial services firms. Thus, the findings and recommendations from this study will help financial services and other organisations to be cognisant of the importance of BYOD policy formulation. The use of BYOD has become prevalent in the workplace due to the increased dependence on the Internet and advancements in technologies. It is beneficial to the organisation in that employees buy, use and insure their own devices, thus, the organisation does not bear these costs. However, there is a huge cost to the company if the use and connection of BYODs to the company’s Information Technology infrastructure is not regulated and monitored. BYODs expose information and information systems assets to threat actors. Financial institutions handle very sensitive information, making them a target for data breach and the adoption of BYODs more hazardous. A qualitative research method was conducted with eight (8) purposefully selected participants working in the Risk, IT and Information Systems Security departments of the financial institution. Telephonic interviews were conducted in line with the national protocols of the global Corona Virus Disease-2019 (COVID-19) pandemic. The study revealed the absence of a BYOD policy and employees could use any number of personal devices without restrictions. Users were aware of information systems security policies and protocols because of the annual training and awareness programmes.
Research on Intelligent Algorithm of Identity Authentication Based on Facial Features

Identity recognition is a research hotspot in the information age. Nowadays, more and more occasions require identity recognition, especially in smart home. Identity recognition of the head of the household can avoid many troubles, such as home identification and network information authentication. Nowadays, in smart home identification, especially based on face recognition, system authentication is basically through feature matching. Although this method is convenient and quick to use, it lacks intelligence. Nowadays, for the make-up, facelift, posture, and other differences, the accuracy of the system is greatly reduced. In this paper, the face recognition method is used for identity authentication. Firstly, the AdaBoost learning algorithm is used to construct the face detection and eye detection classifier to realize the detection and localization of the face and eyes. Secondly, the two-dimensional discrete wavelet transform is used to extract facial features and construct a personal face dynamic feature database. Finally, an improved elastic template matching algorithm is used to establish an intelligent classification method for dynamic face elasticity models. The simulation shows that the proposed method can intelligently adapt to various environments without reducing the accuracy.
Distance constraint between features for unsupervised domain adaptive person re-identification

Many superior person re-identification (re-ID) approaches face a challenge: their performance show disastrous when all supervised models are generalized to a new domain. Some researchers have recently proposed domain adaptive person re-ID methods to address this difficulty, whereas they directly leveraged the target-domain data that would generate lots of noisy pseudo labels. Hence, this paper proposes a distance constraint between features (DCF) method, which clusters the feature distribution fitted the real target-domain data. We assemble different parts of one person for multi-scale self-supervised learning. After introducing domain invariance and designing the inter-image and inter-class distance constraint to regulate distances between target samples, the feature distribution extracted from the encoder trained by the source data can fit the real target data distribution, which leads our domain adaptive model to enjoy the more reliable clustering results and thus obtain a great identification performance in target domain. Extensive experiments demonstrate our approach outperforms state-of-the-art methods on three large-scale released datasets.
An examination of skill requirements for augmented reality and virtual reality job advertisements

The fields of augmented reality (AR) and virtual reality (VR) have seen massive growth in recent years. Numerous degree programs have started to redesign their curricula to meet the high market demand for people qualified to fill related job positions. In this paper, the authors perform a content analysis of online job postings hosted on Indeed.com and provide a skill classification framework for AR/VR job positions. Furthermore, they present a ranking of the relevant skills for such positions. The paper contributes to the extant literature on curriculum design in degree programs by presenting the popular skills in the AR/VR domain.
Genetic Algorithms for Error Mitigation in Quantum Measurement

Noisy Intermediate Scale Quantum (NISQ) devices are expected to demonstrate the real potential of quantum computing in solving hard problems. However, quantum noise that characterizes this kind of devices still remains an obstacle for their practical exploitation in real world scenarios. As a consequence, there is a strong emergence for error correction techniques aimed at making NISQ devices stable and fully operative. Unfortunately, current approaches for quantum error correction are prohibitive for NISQ devices because of the enormous multiplicative cost in resources that they require. For this reason, so-called quantum error mitigation methods are emerging as alternative approaches able to attenuate the quantum error as much as possible, without requiring a strong additional computational effort. Among the most error-prone operations, there is surely the quantum measurement. Conventionally, mitigation methods for quantum measurement error compute a so-called mitigation matrix capable of correcting results outputted by a quantum processor. In this paper, a new measurement error mitigation approach based on genetic algorithms is proposed to learn an appropriate mitigation matrix. As shown in the experimental session, the proposed measurement error mitigation method is comparable with or better than a conventional algebraic approach in terms of the Hellinger fidelity.
An Improved YOLO Algorithm for Rotated Object Detection in Remote Sensing Images

Object detection has a great significance to remote sensing image recognition. Conventional object detection methods by using horizontal bounding box have shown good performance in general images. However, because of looking down perspective of remote sensing images, rotation bounding box is more suitable and precise for object detection. Although rotation bounding box has been researched by some works for remote sensing images, most of them are still two stages. In this paper, by improvement of YOLO architecture, we propose an one-stage object detection method to predict rotation bounding box for remote sensing images. Firstly, in the data preprocessing stage, an image cropping operation is utilized instead of zooming operation to generate suitable input data for network. Meanwhile, in order to avoid an object from being divided, an overlapping band is set during cropping. Secondly, a novel rotation bounding box representation method is introduced, and a corresponding loss function is designed in training process. Experiments on the Dota dataset demonstrate that our method outperforms state-of-the-art rotation objects detection methods, in terms of mAP, our method achieves 73.50%mAP, which is higher than methods based on rotation bounding box by 2.0%.
A Mathematical Analysis and Implementation of Residual Interpolation Demosaicking Algorithms

Demosaicking is the process of reconstructing the full color image from its mosaic version on a Bayer pattern.It is an integral part of the image processing pipeline for single sensor digital color cameras.Demosaicking algorithms based on residual interpolation are interesting because they produce competitive results with a low computational complexity.In this article, we provide an analysis and careful implementation of the most relevant residual based demosaicking algorithms.Our contribution is twofold.First, we present an analysis of the mathematical principles of demosaicking algorithms from the Hamilton-Adams interpolation to the recent "adaptive residual interpolation".Our analysis untangles the relations of these algorithms and how each is improving on the preceding ones.Lastly, we provide a comparison between most recent state of the art methods on several image data sets and discuss their performances.
Supporting Decision Making in Engineering Design Using Parallel Coordinates in Virtual Reality

Computational engineering design methods and tools are common practices in the modern industry. Such approaches are integral in enabling designers to efficiently explore large and complex design spaces. However, they also tend to dramatically increase the number of candidate solutions that decision makers must correctly interpret. Because all candidate solutions can be represented in a digital form together with their assessment criteria, a natural way to explore and understand the complexities of the design problem is to visualize their multidimensional nature. The task now involves the discovery of patterns and trends within a multidimensional design space. This work aims to enhance the design decision-making process with immersive Parallel Coordinates Plot (IPCP) in virtual reality. This paper presents the design of this system, which allows representation and exploration of multidimensional scientific datasets. A qualitative validation with two surrogate expert users demonstrated that the system can be used successfully to detect both known and previously unknown patterns and support learning the decision-making process in a shorter time. The results serve as a promising indication of how immersive parallel coordinate plots can enhance decision support in complex engineering design processes.
Interdisciplinary Design of an Educational Applications Development Platform in a 3D Environment Focused on Cultural Heritage Tourism

This paper is intertwining the technology, education and public archaeology sectors in a case study approach of the archaeological site of the Sanctuary of Amyklaion, an important center of human activity in ancient Sparta, Greece. The creation of an innovative digital platform for the development of educational applications focused on cultural heritage tourism with the use of extended reality and gamification elements is presented. The applications are designed to be managed by a central documentation platform in a 3D environment produced by the digitization of the archaeological site, the excavation and archaeological finds. The main goal in the creation of this platform is to enhance the experiential learning of visitors of the archaeological site and the involvement of communities in the production and consumption of knowledge through new media, using educational methodologies and a human-centered ambient intelligence approach.
A Differential Particle Scheme with Successful Parent Selection and its Application to PID Control Tuning

Proportional-integral-derivative (PID) control is ubiquitous in industrial automation tasks, and the parameter tuning of the gains is challenging due to nonlinearity and stagnation in local optima. In this paper we present a differential particle scheme based on stagnation-based selection mechanism, and evaluate its effectiveness in the stabilization of a nonlinear inverted pendulum and a magnetic levitation system. Our computational experiments show the feasibility to avoid stagnation, the lower variability of convergence over independent runs, and the feasibility to converge to significantly better fitness values compared to relevant heuristics in the literature. We believe our approach offers the building blocks to build stagnation-free nature inspired optimization algorithms useful for adaptive control and tuning.