Reviewerly/exHarmony huggingface dataset:
Towards interoperable research site documentation – Recommendations for information models and data provision

Information related to research sites is essential when describing the context of observations. It is a key element of site-based research infrastructures (RIs) and their catalogues. This paper is dedicated to the comparison of (meta)data models describing research sites in the ecosystem domain. A special focus is on sites in terrestrial and freshwater ecosystems. This should provide the basis for a common site-based data model to exchange data from different site-based catalogue services. This should substantially improve interoperability of site-level information between different RIs. For this purpose, we selected well-established site catalogues that feature web-accessible documentation of the sites and means to export this information. Using either dedicated descriptions of the data models, or, if these were not available, by deriving the data model from the records extracted from the catalogues, we identified the commonalities, differences and gaps of the underlying data models. Based on the findings, we define a set of mandatory core fields to be used in every site catalogue in the ecosystem domain. A set of additional fields are recommended to be implemented. In addition, we formulate general recommendations on how to best serve site data, considering the technical interface, the data format and the data license. The aim of these recommendations is to increase interoperability between site catalogues in general and the selected catalogues in particular. This fosters analyses of the existing ecosystem research infrastructures on a national, regional and global scale and increase the ability of these infrastructures to answer large-scale environmental questions.
Cauchy noise removal by nonlinear diffusion equations

This paper focuses on the problem of image restoration under Cauchy noise. The variational method, which constructs the data fidelity term involving the Cauchy distribution by MAP estimator, has been proven to be a successful approach. In this paper, a nonlinear diffusion equation is proposed to deal with it. The main ingredients of the proposed equation are a gray level based diffusivity that estimates the amplitude of the noise and a classical gradient based diffusivity that controls the anisotropic diffusion according to the image's local structure. The proposed equation has the nondivergence form, and its properties, including the existence, uniqueness, and stability of solutions, are established by the notion of viscosity solution. Experimental results show the superiority of the proposed equation over variational methods in restoring small details of images.
Assessing Ontology Mappings on a Level of Concepts and Instances

In recent years, the number of domain ontologies on the Internet has been steadily increasing. Many ontologies describe overlapping universes of discourse in various ways, therefore, the need for an efficient ontology alignment method is required. Currently, there are many solutions for this problem. However, the only known way to evaluate their output is to confront it with some pre-prepared reference alignment, therefore making it impossible to incorporate in real-world applications where no reference alignment is given. This paper presents some innovative methods of evaluating ontology alignments which allows assessing their quality without the aforementioned reference alignment. The main contribution are formal foundations of such methods, algorithms developed based on those foundations, and an experimental verification of their usefulness.
CMOS Fixed Pattern Noise Elimination Based on Sparse Unidirectional Hybrid Total Variation

With the improvement of semiconductor technology, the performance of CMOS Image Sensor has been greatly improved, reaching the same level as that of CCD in dark current, linearity and readout noise. However, due to the production process, CMOS has higher fix pattern noise than CCD at present. Therefore, the removal of CMOS fixed pattern noise has become the research content of many scholars. For current fixed pattern noise (FPN) removal methods, the most effective one is based on optimization. Therefore, the optimization method has become the focus of many scholars. However, most optimization models only consider the image itself, and rarely consider the structural characteristics of FPN. The proposed sparse unidirectional hybrid total variation (SUTV) algorithm takes into account both the sparse structure of column fix pattern noise (CFPN) and the random properties of pixel fix pattern noise (PFPN), and uses adaptive adjustment strategies for some parameters. From the experimental values of PSNR and SSM as well as the rate of change, the SUTV model meets the design expectations with effective noise reduction and robustness.
Fast Recursive Computation of Sliding DHT with Arbitrary Step

Short-time (sliding) transform based on discrete Hartley transform (DHT) is often used to estimate the power spectrum of a quasi-stationary process such as speech, audio, radar, communication, and biomedical signals. Sliding transform calculates the transform coefficients of the signal in a fixed-size moving window. In order to speed up the spectral analysis of signals with slowly changing spectra, the window can slide along the signal with a step of more than one. A fast algorithm for computing the discrete Hartley transform in windows that are equidistant from each other is proposed. The algorithm is based on a second-order recursive relation between subsequent equidistant local transform spectra. The performance of the proposed algorithm with respect to computational complexity is compared with the performance of known fast Hartley transform and sliding algorithms.
Modulation format identification based on phase statistics in Stokes space

In this work, a high-performance, low-complexity modulation format identification (MFI) technique is proposed and experimentally demonstrated for coherent optical receivers using features extracted from phase statistics in Stokes space. Numerical simulations as well as long-haul experiments are carried out to validate the proposed MFI scheme. Successful identification is demonstrated among polarization-multiplexed (PM) QPSK, 8PSK, 8QAM, 16QAM, 32QAM and 64QAM signals. Compared with existing modulation identification schemes, the proposed MFI method has an excellent recognition performance with a low computational complexity.
Cardinal Graph Convolution Framework for Document Information Extraction

Graph Convolutional Networks (GCN) have been recognized as successful for processing pseudo-spatial graph representations of the underlying structure of documents. We present Cardinal Graph Convolutional Networks (CGCN), an efficient and flexible extension of GCNs with cardinal-direction awareness of spatial node arrangement. The formulation of CGCNs retains the traditional GCN permutation invariance, ensuring directional neighbors are involved in learning abstract representations, even in the absence of a proper ordering of the nodes. We show that CGCNs achieve state of the art results on an invoice information extraction task, jointly learning a word-level tagging as well as document meta-level classification and regression. We also present a new multiscale Inception-like CGCN block-layer, as well as Conv-Pool-DeConv-DePool UNet-like architecture, which increase the receptive field. We demonstrate the utility of CGCNs on private and public datasets, with respect to several baseline models: sequential LSTM, transformer classifier, non-cardinal GCNs, and an image-convolutional approach.
Method for reconstructing a high dynamic range image based on a single-shot filtered low dynamic range image

Traditional cameras are limited by sensors and cannot directly capture single-shot high dynamic range (HDR) images. We propose an improved single-shot HDR image reconstruction method that uses a single-exposure filtered low dynamic range (FLDR) image. First, by adding an optical filter in front of the camera lens, a FLDR image with different RGB channel exposure states and luminance ranges can be captured in a single-shot, unlike the traditional LDR image. Second, a deep inverse tone mapping network (DITMnet) with multibranch features extraction and multioutput images synthesis is designed to reconstruct an HDR image from a single FLDR image. Experimentally, under different exposure states and color spaces, our method outperforms similar algorithms.
Multi-dimensional Interpretations of Presburger Arithmetic in Itself

Abstract Presburger arithmetic is the true theory of natural numbers with addition. We study interpretations of Presburger arithmetic in itself. The main result of this paper is that all self-interpretations are definably isomorphic to the trivial one. Here we consider interpretations that might be multi-dimensional. We note that this resolves a conjecture by Visser (1998, An overview of interpretability logic. Advances in Modal Logic, pp. 307–359). In order to prove the result, we show that all linear orderings that are interpretable in $({\mathbb{N}},+)$ are scattered orderings with the finite Hausdorff rank and that the ranks are bounded in the terms of the dimensions of the respective interpretations.
Multiple depth-levels features fusion enhanced network for action recognition

As a challenging task of video classification, action recognition has become a significant topic of computer vision community. The most popular methods based on two-stream architecture up to now are still simply fusing the prediction scores of each stream. In that case, the complementary characteristics of two streams cannot be fully utilized and the effect of shallower features is often overlooked. In addition, the equal treatment to features may weaken the role of the feature contributing significantly to the classification. Accordingly, a novel network called Multiple Depth-levels Features Fusion Enhanced Network (MDFFEN) is proposed. It improves on two aspects of two-stream architecture. In terms of the two-stream interaction mechanism, multiple depth-levels features fusion (MDFF) is formed to aggregate spatial–temporal features extracted from several sub-modules of original two streams by spatial–temporal features fusion (STFF). And with respect to further refining the spatiotemporal features, we propose a group-wise spatial-channel enhance (GSCE) module to highlight the meaningful regions and expressive channels automatically by priority assignment. The competitive results are achieved after we validate MDFFEN on three public challenging action recognition datasets, HDMB51, UCF101 and ChaLearn LAP IsoGD.
A Blockchain based Privacy-Preserving System for Electric Vehicles through Local Communication

In this study, we propose a privacy preservation and efficient distributed searching and matching of Electric Vehicles (EVs) charging demander with suppliers based on reputation. Partially homomorphic encryption-based on reputation computation using local communication is used in the implementation, while hiding EVs users' location. A private blockchain is incorporated in the system to verify and permit secure trading of energy among the EVs' demander and suppliers. The results of the simulation show that the proposed privacy preserved algorithm converges more faster as compared to Bichromatic Mutual Nearest Neighbor (BMNN) algorithm.
Health is the Motive and Digital is the Instrument

The coronavirus crisis has seen an unprecedented response from India and the world. If the viral outbreak has exposed gross inadequacies in the healthcare systems of nations both rich and poor, it has stirred a digital healthcare revolution that has been building since the past decade. We have seen how this new era of digital health evolved over the years since healthcare started getting increasingly unaffordable in the western countries forcing a relook in their strategies to explosion of digital innovations in mobile telephony and applications, internet, wearable devices, artificial intelligence, robotics, big data and genomics. The single biggest trigger for the digital shift has indeed been the COVID-19 pandemic this year, more so in India with astonishing response from the private enterprise and the proactive push from the government so evident. However, the full potential of this digital revolution cannot be realized as long as core structural reforms in public healthcare do not take place along with significant boost in digital infrastructure. The way digital technologies have helped facilitate strategy and response to the global pandemic and with predictions of more zoonotic outbreaks impending in the coming years, it has become imperative for the world to increasingly adopt and integrate digital innovations to make healthcare more accessible, interconnected and affordable.
Working with Nature's Lag: Initial Design Lessons for Slow Biotic Games

One of the most fundamental features of living organisms is their growth, a biological phenomenon that can be considered as a type of slow, tangible output responding to an environmental stimulus or an input. Given the relative slowness of growth, once it becomes part of game mechanics, the feature can lead to slow interactivity and slow gameplay in biotic games – a relatively new type of bio-digital game that enables playful human-microbe interactions. Currently, there is a lack of annotations on existing biotic game design guidelines, that 1) recognise biological slowness as a potentially beneficial feature in game design, and 2) provide specific advice on how organism's slow response time can be effectively incorporated in biotic games. To start addressing these limitations, we report on an initial set of design lessons learnt from our research on slow biotic games. Through these lessons, we have formulated and outlined a set of practical recommendations for prospective designers of slow biotic games.
C-14

Inexpensive and highly capable unmanned aerial vehicles (aka drones) have enabled people to contribute high-quality videos at a global scale. However, a key challenge exists for accepting videos from untrusted sources: establishing when a particular video was taken. Once a video has been received or posted publicly, it is evident that the video was created before that time, but there are no current methods for establishing how long it was made before that time.
Learning Classifiers under Delayed Feedback with a Time Window Assumption

We consider training a binary classifier under delayed feedback (DF learning). For example, in the conversion prediction in online ads, we initially receive negative samples that clicked the ads but did not buy an item; subsequently, some samples among them buy an item then change to positive. In the setting of DF learning, we observe samples over time, then learn a classifier at some point. We initially receive negative samples; subsequently, some samples among them change to positive. This problem is conceivable in various real-world applications such as online advertisements, where the user action takes place long after the first click. Owing to the delayed feedback, naive classification of the positive and negative samples returns a biased classifier. One solution is to use samples that have been observed for more than a certain time window assuming these samples are correctly labeled. However, existing studies reported that simply using a subset of all samples based on the time window assumption does not perform well, and that using all samples along with the time window assumption improves empirical performance. We extend these existing studies and propose a method with the unbiased and convex empirical risk that is constructed from all samples under the time window assumption. To demonstrate the soundness of the proposed method, we provide experimental results on a synthetic and open dataset that is the real traffic log datasets in online advertising.
Internet of Things (IoT) for Healthcare Application

People with health conditions such as dementia often face problems sleeping, experience wake-rest routine changes and suffer from emotional disturbances amid sleep disorders. Caregivers, such as family members, of dementia sufferers face great challenge in taking care of such patients at night as it takes a toll on their own sleep quality resulting in sleep deprivation and other issues. The goal of this work, presented in this paper, is to develop a wearable body position monitor that can detect user's body position and keep online records during sleep; provide light when data shows the user is not asleep, aid user to fall asleep with audio assist feature, and if necessary, activate emergency alert call to caregivers when the patient remains seated or stands for longer durations (e.g. more than 20 minutes) at night. Main system components of the developed prototype include MySignals HW Complete Kit (e-health medical development platform), Arduino Uno microcontroller, LEDs, speakers, micro SD card, micro SD card reader, SPI interface and esp8266 module. Real-time transmission, data analysis and visualization and remote data storage has been realized. The plan for the next phase of this work will include application of sleep pattern recognition and machine learning techniques on large datasets and real biometric measurements.
Content and Privacy Protection in JPEG Images by Reversible Visual Transformation

With the popularity of cloud computing and social networks, more and more JPEG images are stored and distributed. Consequently, how to protect privacy and content in JPEG images has become an important issue. Although traditional encryption schemes can be employed, the file format of JPEG images is changed so that their usage may be affected. In this paper, a reversible visual transformation algorithm is proposed to protect content in JPEG images. Specifically, the DC coefficient in each user-selected block is modified, while the information required to recover it is reversibly hidden into AC coefficients. Then the signs of AC coefficients in the selected blocks are flipped and the blocks are further scrambled with a secret key. By embedding the location information of the selected blocks in a transformed image, the original image can be exactly recovered when needed. Besides, regions to be protected can be arbitrarily chosen without substantially affecting the rest of the image. The experimental results on a set of JPEG images validate the efficacy and reversibility of the proposed algorithm. In addition, good performance is achieved in terms of invisibility of the protected content, image quality, file size preservation and security.
An Anomaly Detection Method for Outdoors Insulator in High-Speed Railway Traction Substation

The outdoors insulator is an important component of the high-speed railway traction substation, which is of great significance to maintain the stability of transmission line and ensure the normal operation of transmission network. Once there is a fault for the insulator, it will cause serious transmission failure and economic loss. Therefore, a method is proposed to detect the abnormal areas of outdoors insulator in high-speed railway traction substation based on object detection and generative adversarial networks. First, we employ Faster RCNN to locate the area of insulator from the input image of traction substation. Then, the image of insulator obtained from the first step is fed into our designed generative adversarial networks to generate fake image, which is a normal image of insulator. Finally, multi-scale structural similarity algorithm is used to realize the anomaly detection of insulator and visualize anomalous areas. Experiments results on Heishan traction substation show that the proposed method is effective.
Analysis of deep learning architectures for turbulence mitigation in long-range imagery

In long range imagery, the atmosphere along the line of sight can result in unwanted visual effects. Random variations in the refractive index of the air causes light to shift and distort. When captured by a camera, this randomly induced variation results in blurred and spatially distorted images. The removal of such effects is greatly desired. Many traditional methods are able to reduce the effects of turbulence within images, however they require complex optimisation procedures or have large computational complexity. The use of deep learning for image processing has now become commonplace, with neural networks being able to outperform traditional methods in many fields. This paper presents an evaluation of various deep learning architectures on the task of turbulence mitigation. The core disadvantage of deep learning is the dependence on a large quantity of relevant data. For the task of turbulence mitigation, real life data is difficult to obtain, as a clean undistorted image is not always obtainable. Turbulent images were therefore generated with the use of a turbulence simulator. This was able to accurately represent atmospheric conditions and apply the resulting spatial distortions onto clean images. This paper provides a comparison between current state of the art image reconstruction convolutional neural networks. Each network is trained on simulated turbulence data. They are then assessed on a series of test images. It is shown that the networks are unable to provide high quality output images. However, they are shown to be able to reduce the effects of spatial warping within the test images. This paper provides critical analysis into the effectiveness of the application of deep learning. It is shown that deep learning has potential in this field, and can be used to make further improvements in the future.
Verify a Valid Message in Single Tuple: A Watermarking Technique for Relational Database

The leakage of sensitive digital assets is a major problem which causes huge legal risk and economic loss. Service providers need to ensure data security for owners. Robust watermarking techniques play a critical role in ownership protection of relational databases. In this paper, we proposed a new Double-layer Ellipse Model called DEM that embeds a valid message in each candidates tuples. Each watermark can independently prove ownership. The main idea of DEM is to use watermarks itself to locate and make the most of contextual information to verify validity of watermarks. Under the framework of DEM, we propose a robust and semi-blind reversible watermarking scheme. Our scheme handles non-significant data for locating and embedding. The scheme generates watermark by exchanging data groups extracted from scattered attributes using Computation and Sort-Exchange step. Key information such as primary key, most significant bit (MSB) become a assistant feature for verifying the validity of embedded watermark. Our scheme can be applied on all type of numerical attributes (e.g. Integer, float, double, boolean). In robust experiments, the scheme is proved to be extremely resilient to insertion/detection/alteration attacks in both normal and hard situations. From a practical point of view, our solution is easy to implement and has good performance in statistics, incremental updates, adaptation.