Reviewerly/exHarmony huggingface dataset:
Spherical Convolution Empowered Viewport Prediction in 360 Video Multicast with Limited FoV Feedback

Field of view (FoV) prediction is critical in 360-degree video multicast, which is a key component of the emerging virtual reality and augmented reality applications. Most of the current prediction methods combining saliency detection and FoV information neither take into account that the distortion of projected 360-degree videos can invalidate the weight sharing of traditional convolutional networks nor do they adequately consider the difficulty of obtaining complete multi-user FoV information, which degrades the prediction performance. This article proposes a spherical convolution-empowered FoV prediction method, which is a multi-source prediction framework combining salient features extracted from 360-degree video with limited FoV feedback information. A spherical convolutional neural network is used instead of a traditional two-dimensional convolutional neural network to eliminate the problem of weight sharing failure caused by video projection distortion. Specifically, salient spatial-temporal features are extracted through a spherical convolution-based saliency detection model, after which the limited feedback FoV information is represented as a time-series model based on a spherical convolution-empowered gated recurrent unit network. Finally, the extracted salient video features are combined to predict future user FoVs. The experimental results show that the performance of the proposed method is better than other prediction methods.
Advancing simulation tools specific to floating solar photovoltaic systems – Comparative analysis of field-measured and simulated energy performance

The land-use intensity and performance-related issues in the solar energy sector have led to the development of floating photovoltaic (FPV) systems that allow solar photovoltaic (PV) installation on water bodies. The FPV systems present two-fold benefits in terms of mitigation of land-use conflicts and improved energy performances. While such experimental studies exist in the literature, the critical apprehensions related to performance feasibility assessments during the project planning are still unclear due to the lack of simulation tools specific to the FPV. Also, given the high uncertainty in solar PV performance, which varies from location to location, the existing experimental studies may not help in benchmarking and revealing the need for simulations specific to FPV. In reality, most installers/researchers are still using simulation tools that apply to conventional PV installations, and that do not account for the PV-water interaction and cooling effect caused. Until now, without realising this, the service providers are providing inaccurate performance feasibility reports to clients who wish to deploy FPV. Hence, this study investigates whether the existing simulation tools can be applied for FPV planning by taking a 2 MWp FPV power plant installed on the urban water reservoir in Southern India as a case study. First, the field measured data of the 2 MWp FPV plant is monitored. Second, the same is simulated in three different software tools: PVsyst, System Advisory Model (SAM) by National Renewable Energy Laboratory (NREL), and Helioscope. Third, error metrics-based methodology is applied to understand the deviation in energy output, capacity utilization factor (CUF), and performance ratio (PR). The results advocate that deviation in energy from the measured to simulated varies significantly from 18.43 to 38.55%, suggesting that none of the considered simulation tools suitable for FPV project planning. Similarly, other performance indicators are analysed, followed by a discussion and data-driven recommendations leading to the conceptual simulation tool development for FPV.
Old Photo Restoration via Deep Latent Space Translation

We propose to restore old photos that suffer from severe degradation through a deep learning approach. Unlike conventional restoration tasks that can be solved through supervised learning, the degradation in real photos is complex and the domain gap between synthetic images and real old photos makes the network fail to generalize. Therefore, we propose a novel triplet domain translation network by leveraging real photos along with massive synthetic image pairs. Specifically, we train two variational autoencoders (VAEs) to respectively transform old photos and clean photos into two latent spaces. And the translation between these two latent spaces is learned with synthetic paired data. This translation generalizes well to real photos because the domain gap is closed in the compact latent space. Besides, to address multiple degradations mixed in one old photo, we design a global branch with a partial nonlocal block targeting the structured defects, such as scratches and dust spots, and a local branch targeting the unstructured defects, such as noises and blurriness. We also extend the global branch with a more memory-efficient scheme, named multi-scale patch-based attention to processing high-resolution photos. Two branches are fused in the latent space, leading to improved capability to restore old photos from multiple defects. Furthermore, we apply another face refinement network to recover fine details of faces in the old photos, thus ultimately generating photos with enhanced perceptual quality. With comprehensive experiments, the proposed pipeline demonstrates superior performance over state-of-the-art methods as well as existing commercial tools in terms of visual quality for old photos restoration. Both code and models could be found at https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life.
A Multi-User Cost-Efficient Crowd-Assisted VR Content Delivery Solution in 5G-and-Beyond Heterogeneous Networks

The latest evolution of wireless communications enables user access rich Virtual Reality (VR) services via the Internet, including while on the move. However, providing a premium immersive experience for massive number of concurrent users with various device configurations is a significant challenge due to the ultra-high data rate and ultra-low delay requirements of live VR services. This paper introduces an innovative multi-user cost-efficient crowd-assisted delivery and computing (MEC-DC) framework, which leverages mobile edge computing and end-user resources to support high performance VR content delivery over 5G-and-beyond heterogeneous networks (5G-HetNets). The proposed MEC-DC framework is based on three main solutions. First is a novel buffer-nadir-based multicast (BNM) mechanism for VR transmissions over 5G-HetNets. BNM ensures smooth and synchronized user viewing experience by maximizing the average playback buffer-nadir of all participants with stochastic optimization. Second and third are practical distributed algorithms: the cost-efficient multicast-aware transcoding offloading (MATO) and crowd-assisted delivery algorithm (CAD) which optimize jointly multicast delivery and video transcoding. The algorithms optimality and complexity were investigated. The proposed MATO-CAD solution was evaluated with real datasets, trace-driven numerical simulations, and prototype-based experiments. The trace-driven experimental results showed how the proposed solution provides 18% throughput improvement, lowest delay and best playback freeze ratio in comparison with three other state-of-the-art solutions.
A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning

The histopathological diagnosis of prostate adenocarcinoma in needle biopsy specimens is of pivotal importance for determining optimum prostate cancer treatment. Since diagnosing a large number of cases containing 12 core biopsy specimens by pathologists using a microscope is time-consuming manual system and limited in terms of human resources, it is necessary to develop new techniques that can rapidly and accurately screen large numbers of histopathological prostate needle biopsy specimens. Computational pathology applications that can assist pathologists in detecting and classifying prostate adenocarcinoma from whole-slide images (WSIs) would be of great benefit for routine pathological practice. In this paper, we trained deep learning models capable of classifying needle biopsy WSIs into adenocarcinoma and benign (non-neoplastic) lesions. We evaluated the models on needle biopsy, transurethral resection of the prostate (TUR-P), and The Cancer Genome Atlas (TCGA) public dataset test sets, achieving an ROC-AUC up to 0.978 in needle biopsy test sets and up to 0.9873 in TCGA test sets for adenocarcinoma.
SIFT Matching by Context Exposed

This paper investigates how to step up local image descriptor matching by exploiting matching context information. Two main contexts are identified, originated respectively from the descriptor space and from the keypoint space. The former is generally used to design the actual matching strategy while the latter to filter matches according to the local spatial consistency. On this basis, a new matching strategy and a novel local spatial filter, named respectively blob matching and Delaunay Triangulation Matching (DTM) are devised. Blob matching provides a general matching framework by merging together several strategies, including rank-based pre-filtering as well as many-to-many and symmetric matching, enabling to achieve a global improvement upon each individual strategy. DTM alternates between Delaunay triangulation contractions and expansions to figure out and adjust keypoint neighborhood consistency. Experimental evaluation shows that DTM is comparable or better than the state-of-the-art in terms of matching accuracy and robustness. Evaluation is carried out according to a new benchmark devised for analyzing the matching pipeline in terms of correct correspondences on both planar and non-planar scenes, including several state-of-the-art methods as well as the common SIFT matching approach for reference. This evaluation can be of assistance for future research in this field.
Automated machine learning for secure key rate in discrete-modulated continuous-variable quantum key distribution

Continuous-variable quantum key distribution (CV QKD) with discrete modulation has attracted increasing attention due to its experimental simplicity, lower-cost implementation and compatibility with classical optical communication. Correspondingly, some novel numerical methods have been proposed to analyze the security of these protocols against collective attacks, which promotes key rates over one hundred kilometers of fiber distance. However, numerical methods are limited by their calculation time and resource consumption, for which they cannot play more roles on mobile platforms in quantum networks. To improve this issue, a neural network model predicting key rates in nearly real time has been proposed previously. Here, we go further and show a neural network model combined with Bayesian optimization. This model automatically designs the best architecture of neural network computing key rates in real time. We demonstrate our model with two variants of CV QKD protocols with quaternary modulation. The results show high reliability with secure probability as high as 99.15% - 99.59%, considerable tightness and high efficiency with speedup of approximately 107 in both cases. This inspiring model enables the real-time computation of unstructured quantum key distribution protocols' key rate more automatically and efficiently, which has met the growing needs of implementing QKD protocols on moving platforms.
Piezo-optomechanical cantilever modulators for VLSI visible photonics

Visible-wavelength very large-scale integration (VLSI) photonic circuits have potential to play important roles in quantum information and sensing technologies. The realization of scalable, high-speed, and low-loss photonic mesh circuits depends on reliable and well-engineered visible photonic components. Here we report a low-voltage optical phase shifter based on piezo-actuated mechanical cantilevers, fabricated on a CMOS compatible, 200 mm wafer-based visible photonics platform. We show linear phase and amplitude modulation with 6 V$_{\pi}$-cm in differential operation, -1.5 dB to -2 dB insertion loss, and up to 40 dB contrast in the 700 nm - 780 nm range. By adjusting selected cantilever parameters, we demonstrate a low-displacement and a high-displacement device, both exhibiting a nearly flat frequency response from DC to a peak mechanical resonance at 23 MHz and 6.8 MHz respectively, which through resonant enhancement of Q~40, further decreases the operating voltage down to 0.15 V$_{\pi}$-cm.
Rethinking Minimal Sufficient Representation in Contrastive Learning

Contrastive learning between different views of the data achieves outstanding success in the field of self-supervised representation learning and the learned representations are useful in broad downstream tasks. Since all supervision information for one view comes from the other view, contrastive learning approximately obtains the minimal sufficient representation which contains the shared information and eliminates the non-shared information between views. Considering the diversity of the downstream tasks, it cannot be guaranteed that all task-relevant information is shared between views. Therefore, we assume the non-shared task-relevant information cannot be ignored and theoretically prove that the minimal sufficient representation in contrastive learning is not sufficient for the downstream tasks, which causes performance degradation. This reveals a new problem that the contrastive learning models have the risk of overfitting to the shared information between views. To alleviate this problem, we propose to increase the mutual information between the representation and input as regularization to approximately introduce more task-relevant information, since we cannot utilize any downstream task information during training. Extensive experiments verify the rationality of our analysis and the effectiveness of our method. It significantly improves the performance of several classic contrastive learning models in downstream tasks. Our code is available at https://github.com/Haoqing-Wang/InfoCL.
The effectiveness of using augmented reality (AR) to enhance student performance: using quick response (QR) codes in student textbooks in the Saudi education system

Augmented reality (AR) is a new way to integrate virtual reality into the real world, and integrating AR into education offers opportunities for increasing student performance. The Saudi Ministry of Education integrated technology into its educational system by building an educational portal called iEN, which offers many technologies that support education, such as AR experiments, e-textbooks, learning games, video clips, and TV channels. This initiative made Saudi Arabia better prepared for the transition to remote education, which offered an easy and prompt shifting of the education system during the Coronavirus pandemic (COVID-19). The current study examined the effects of using QR codes as an AR to enhance student performance in Saudi education. The findings show that students who utilized QR codes in their education performed at higher levels than those who did not and demonstrated that students did not face any technical issues in integrating technology into their learning processes. However, that could be based on their generation of using technology (alpha generation), which became part of their lives.
A survey of privacy-preserving offloading methods in mobile-edge computing

By moving computing resources to the network logical edge, mobile edge computing is promising for providing low-latency computing services to mobile users, and task offloading is one of its key enabling techniques. However, serious privacy concerns come along with offloading, due to the vulnerability of edge servers and the wireless transmission feature. Although many research efforts have been devoted to privacy-preserving offloading in recent years, there still lacks a comprehensive survey to systematically review these pioneering works. With this consideration, a thorough review for the state-of-the-art on this topic is provided in this work. Specifically, the privacy issues in offloading as well as the related metrics and application scenarios are discussed first. Then, based on their associated offloading phases, existing privacy-preserving offloading methods are classified into three categories: managing offloading data and pattern, secure transmission of offloaded data, and offloading destination selection. Finally, potential future directions of privacy-preserving offloading are discussed.
Geoscience-aware deep learning: A new paradigm for remote sensing

Information extraction is a key activity for remote sensing images. A common distinction exists between knowledge-driven and data-driven methods. Knowledge-driven methods have advanced reasoning ability and interpretability, but have difficulty in handling complicated tasks since prior knowledge is usually limited when facing the highly complex spatial patterns and geoscience phenomena found in reality. Data-driven models, especially those emerging in machine learning (ML) and deep learning (DL), have achieved substantial progress in geoscience and remote sensing applications. Although DL models have powerful feature learning and representation capabilities, traditional DL has inherent problems including working as a black box and generally requiring a large number of labeled training data. The focus of this paper is on methods that integrate domain knowledge, such as geoscience knowledge and geoscience features (GK/GFs), into the design of DL models. The paper introduces the new paradigm of geoscience-aware deep learning (GADL), in which GK/GFs and DL models are combined deeply to extract information from remote sensing data. It first provides a comprehensive summary of GK/GFs used in GADL, which forms the basis for subsequent integration of GK/GFs with DL models. This is followed by a taxonomy of approaches for integrating GK/GFs with DL models. Several approaches are detailed using illustrative examples. Challenges and research prospects in GADL are then discussed. Developing more novel and advanced methods in GADL is expected to become the prevailing trend in advancing remotely sensed information extraction in the future.
LFANet: Lightweight feature attention network for abnormal cell segmentation in cervical cytology images

With the widely applied computer-aided diagnosis techniques in cervical cancer screening, cell segmentation has become a necessary step to determine the progression of cervical cancer. Traditional manual methods alleviate the dilemma caused by the shortage of medical resources to a certain extent. Unfortunately, with their low segmentation accuracy for abnormal cells, the complex process cannot realize an automatic diagnosis. In addition, various methods on deep learning can automatically extract image features with high accuracy and small error, making artificial intelligence increasingly popular in computer-aided diagnosis. However, they are not suitable for clinical practice because those complicated models would result in more redundant parameters from networks. To address the above problems, a lightweight feature attention network (LFANet), extracting differentially abundant feature information of objects with various resolutions, is proposed in this study. The model can accurately segment both the nucleus and cytoplasm regions in cervical images. Specifically, a lightweight feature extraction module is designed as an encoder to extract abundant features of input images, combining with depth-wise separable convolution, residual connection and attention mechanism. Besides, the feature layer attention module is added to precisely recover pixel location, which employs the global high-level information as a guide for the low-level features, capturing dependencies of channel features. Finally, our LFANet model is evaluated on all four independent datasets. The experimental results demonstrate that compared with other advanced methods, our proposed network achieves state-of-the-art performance with a low computational complexity.
Real‐Time Earthquake Detection and Magnitude Estimation Using Vision Transformer

Abstract We design a fully automated system for real‐time magnitude estimation based on a vision transformer (ViT) network. ViT is an attention mechanisms, which guides the proposed network to extract the significant features from the input seismic data, leading to robust magnitude estimation performance. We propose to design two separate ViT networks, that is, one for picking the P‐wave arrival time and the other for predicting the earthquake magnitude using a single station. For real‐time application, we pick the P‐wave arrival times and consider them as the reference, based on which the non‐normalized 30‐s (i.e., 1 s before and 29 s after the reference time) three‐component seismograms are used to predict the magnitudes of the corresponding earthquakes. The ViT picking network is first trained and tested using the STanford EArthquake Data set (STEAD) and shows robust picking performance, achieving an average picking error of less than 0.2 s compared to the manual picks. Then, the ViT magnitude estimation network is evaluated using several data sets, including those from California, STEAD repository, and Texas. The ViT demonstrates robust magnitude estimation performance in all these test cases as compared with the benchmark methods. For magnitude estimation, the mean absolute error (MAE) and the standard deviation error ( σ ) for the testing set of the STEAD data set are 0.112 and 0.164 (as compared with 0.141 and 0.219 for the state‐of‐the‐art MagNet method), respectively. The MAE and σ for the California testing set are 0.079 and 0.120 (as compared with 0.089 and 0.138 for the Magnet method), respectively. As a case study, the new ViT networks are applied to the 24‐hr continuous seismic data of the TexNet‐PB05 station recorded on September 20th. The network successfully picks all the events in the TexNet catalog with a small (&lt;=0.42) magnitude error. The ViT network shows promising magnitude prediction results when tested with 4‐s long seismograms. This highlights its potential in the earthquake early warning (EEW) system for fast and reliable decisions.
An Effective Copyright Management Principle using Intelligent Wavelet Transformation based Water marking Scheme

Digital Image Processing technology provides lots of benefits to commercial and non-commercial organizations to process the image according to their needs. This paper provides a logical protection to the image by means of adding a watermarking content into it. Watermarking is a technique that embeds sensitive records into covered material to establish copyright. Infringement of copyright is a driving force behind the development of new cryptographic techniques. Digital-Image Watermarking is one such method and the proposed research effort will concentrate on the details of computerized image watermarking approaches and their implementations in a variety of disciplines. The notion of privacy preservation with respect to watermarking, its goal, methodology and forms of watermarking threats as well as its implications in image processing technique will be discussed in clear manner with this paper. Watermarking is used to protect researchers' materials as well as to conceal details within a pattern that is harder to identify by unauthorized individuals. A digital watermark is a sequence of information inserted in a data-frame that provides characteristics like as trademark protection and copyright security. In this paper, logic of watermarking is proposed by means of introducing a new approach called Intelligent Wavelet Transformation (IWT), in which it is derived by means of conventional Digital-Wavelet-Transform (DWT) method. The resulting section provides the proper proof of proposed approach efficiency and security in clear manner with proper specifications.