Reviewerly/exHarmony huggingface dataset:
A novel framework for policy based on-chain governance of blockchain networks

Although blockchains are systems that are intended to survive on their own, they still may need human intervention like other Parallel and Distributed Computing (PDC) architectures. The reasons for this intervention are twofold: management of blockchain nodes and governance of blockchain. The second focuses on sustaining blockchain unlike the first one concerning with operation of individual nodes. Proposed blockchain governance models so far include cumbersome processes in which deciding and deployment of changes rely on the involvement of multiple actors. They have been criticized from various aspects such as reducing the decentralized nature of blockchain. In this paper, we propose a novel on-chain governance model exploiting both policy-based management and decentralized identity technologies. Since it allows controlling the behavior of the system via higher level expressions that are easy to understand, the policy based management concept will fit in well with blockchain ecosystems having stakeholders from different knowledge levels. This model is not only more effective regarding existing off-chain governance models but also is simpler and more user friendly than other on-chain governance models. Besides, the emerging decentralized identity concept has the potential for solving many problems related to decision-making process of blockchain governance. By employing this concept, our approach fortifies decentralization, security and fairness while providing well-directed governance. The smart contract based modular design of our framework opens the door for adoption of its components by other governance models as well.
AWNet: Attentive Wavelet Network for Image ISP

As the revolutionary improvement being made on the performance of smartphones over the last decade, mobile photography becomes one of the most common practices among the majority of smartphone users. However, due to the limited size of camera sensors on phone, the photographed image is still visually distinct to the one taken by the digital single-lens reflex (DSLR) camera. To narrow this performance gap, one is to redesign the camera image signal processor (ISP) to improve the image quality. Owing to the rapid rise of deep learning, recent works resort to the deep convolutional neural network (CNN) to develop a sophisticated data-driven ISP that directly maps the phone-captured image to the DSLR-captured one. In this paper, we introduce a novel network that utilizes the attention mechanism and wavelet transform, dubbed AWNet, to tackle this learnable image ISP problem. By adding the wavelet transform, our proposed method enables us to restore favorable image details from RAW information and achieve a larger receptive field while remaining high efficiency in terms of computational cost. The global context block is adopted in our method to learn the non-local color mapping for the generation of appealing RGB images. More importantly, this block alleviates the influence of image misalignment occurred on the provided dataset. Experimental results indicate the advances of our design in both qualitative and quantitative measurements. The source code is available at https://github.com/Charlie0215/AWNet-Attentive-Wavelet-Network-for-Image-ISP .
Triplet Loss With Multistage Outlier Suppression and Class-Pair Margins for Facial Expression Recognition

Deep metric based triplet loss has been widely used to enhance inter-class separability and intra-class compactness of network features. However, the margin parameters in the triplet loss for current approaches are usually fixed and not adaptive to the variations among different expression pairs. Meanwhile, outlier samples like faces with confusing expressions, occlusion and large head poses may be introduced during the selection of the hard triplets, which may deteriorate the generalization performance of the learned features for normal testing samples. In this work, a new triplet loss based on class-pair margins and multistage outlier suppression is proposed for facial expression recognition (FER). In this approach, each expression pair is assigned with an order-insensitive or two order-aware adaptive margin parameters. While expression samples with large head poses or occlusion are firstly detected and excluded, abnormal hard triplets are discarded if their feature distances do not fit the model of normal feature distance distribution. Extensive experiments on seven public benchmark expression databases show that the network using the proposed loss achieves much better accuracy than that using the original triplet loss and the network without using the proposed strategies, and the most balanced performances among state-of-the-art algorithms in the literature.
Occupancy Map Guided Fast Video-Based Dynamic Point Cloud Coding

In video-based dynamic point cloud compression (V-PCC), 3D point clouds are projected into patches, and then the patches are padded into 2D images suitable for the video compression framework. However, the patch projection-based method produces a large number of empty pixels; the far and near components are projected to generate different 2D images (video frames), respectively. As a result, the generated video is with high resolutions and double frame rates, so the V-PCC has huge computational complexity. This paper proposes an occupancy map guided fast V-PCC method. Firstly, the relationship between the prediction coding and block complexity is studied based on a local linear image gradient model. Secondly, according to the V-PCC strategies of patch projection and block generation, we investigate the differences of rate-distortion characteristics between different types of blocks, and the temporal correlations between the far and near layers. Finally, by taking advantage of the fact that occupancy maps can explicitly indicate the block types, we propose an occupancy map guided fast coding method, in which coding is performed on the different types of blocks. Experiments have tested typical dynamic point clouds, and shown that the proposed method achieves an average 43.66% time-saving at the cost of only 0.27% and 0.16% Bjontegaard Delta (BD) rate increment under the geometry Point-to-Point (D1) error and attribute Luma Peak-Signal-Noise-Ratio (PSNR), respectively.
Sentiment Analysis on COVID-19 Twitter Data

Corona Virus or COVID-19 first appeared in December, 2019 in Wuhan, China.People tweeted aggressively on twitter at that time.This paper analysed the tweets regarding COVID-19 from November, 2019 to May, 2020 in India and its affect.All tweets are categorized into 3 categories(Positive, Negative and Neutral).Multiple datasets are created Statewise, Month-wise, combined of all states to analyze the people's reactions towards Lockdown in June, 2020 and about everything related to COVID-19.Most people started having Negative tweets but with increasing time people shifted towards positive and neutral comments.In April, 2020 most comments were Positive and about winning against Corona virus.
High Capacity Reversible Data Hiding in Encrypted Image Based on Adaptive MSB Prediction

Reversible data hiding in encrypted image (RDHEI) is a technique that can be adopted by cloud sever to embed additional data into the encrypted image with no permanent distortion. For RDHEI, it remains a challenging task to improve the embedding capacity under the premise of real reversibility. In this paper, a novel RDHEI method based on adaptive most significant bit (MSB) prediction is proposed. The cover image is first encrypted in block-wise manner such that the correlation of pixels within block is preserved. Next, all blocks are permuted to fulfill the final encryption. During data embedding, the upper-left pixel within block is used to predict others such that the embedding room is vacated. Then, available blocks are selected and all blocks are rearranged so as to ensure reversibility. By fully exploiting the correlation of pixels within block via adaptive MSB prediction, the proposed method successes to achieve desirable improvement in capacity. Experimental results show that the proposed method significantly outperforms previous methods. Moreover, real reversibility and real separability are also guaranteed. In a word, the proposed method is a practical method that can be adopted by cloud storage.
Chasing Your Long Tails

Machine learning models in health care are often deployed in settings where it is important to protect patient privacy. In such settings, methods for differentially private (DP) learning provide a general-purpose approach to learn models with privacy guarantees. Modern methods for DP learning ensure privacy through the addition of calibrated noise. The resulting privacy-preserving models are unable to learn too much information about the tails of a data distribution, resulting in a loss of accuracy that can disproportionately affect small groups. In this paper, we study the effects of DP learning in health care. We use state-of-the-art methods for DP learning to train privacy-preserving models in clinical prediction tasks, including x-ray classification of images and mortality prediction in time series data. We use these models to perform a comprehensive empirical investigation of the tradeoffs between privacy, utility, robustness to dataset shift and fairness. Our results highlight lesser-known limitations of methods for DP learning in health care, models that exhibit steep tradeoffs between privacy and utility, and models whose predictions are disproportionately influenced by large demographic groups in the training data. We discuss the costs and benefits of differentially private learning in health care with open directions for differential privacy, machine learning and health care.
Bi-Directional Domain Adaptation for Sim2Real Transfer of Embodied Navigation Agents

Deep reinforcement learning models are notoriously data hungry, yet real-world data is expensive and time consuming to obtain. The solution that many have turned to is to use simulation for training before deploying the robot in a real environment. Simulation offers the ability to train large numbers of robots in parallel, and offers an abundance of data. However, no simulation is perfect, and robots trained solely in simulation fail to generalize to the real-world, resulting in a "sim-vs-real gap". How can we overcome the trade-off between the abundance of less accurate, artificial data from simulators and the scarcity of reliable, real-world data? In this paper, we propose Bi-directional Domain Adaptation (BDA), a novel approach to bridge the sim-vs-real gap in both directions -- real2sim to bridge the visual domain gap, and sim2real to bridge the dynamics domain gap. We demonstrate the benefits of BDA on the task of PointGoal Navigation. BDA with only 5k real-world (state, action, next-state) samples matches the performance of a policy fine-tuned with ~600k samples, resulting in a speed-up of ~120x.
Decoding Method of Information-Psychological Destructions in the Phonetic Space of Information Resources

The modern information space, as a collection of heterogeneous information resources, is a rather aggressive environment that has a negative destructive effect on the psyche of users, primarily adolescents. To identify such negative influences, a technique for decoding the phonetic display of the sound space of an information resource is needed to identify destructive modifications. Therefore, it is proposed to improve information technologies to counter threats to information and psychological security of adolescents.
Determination of Process Noise for Underwater Target Tracking with Forward Looking Sonar

Target tracking is a process that provides information about targets in a specific area and is one of the key issues affecting the safety of any vehicle navigating in water. The main sensor used for underwater target tracking is sonar, with one of the most popular configurations being forward looking sonar (FLS). The target tracking state vector is usually estimated with the use of numerical filter algorithms, such as the Kalman filter (KF) and its modification, or the particle filter (PF). This requires the definition of a process model, including process noise, and a measurement model. This study focused on process noise definition. It is usually implemented as Gaussian noise, with a covariance matrix defined by the author. An analytical and empirical analysis was conducted, including a verification of the existing approaches and a survey of the published literature. Additionally, a theoretical analysis of the factors influencing process noise was conducted, which was followed by an empirical verification. The results were discussed, leading to the conclusions. The results of the theoretical analysis were confirmed by the empirical experiment and the results were compared with commonly used values of process noise in underwater target tracking processes.
Fine-Grained Attention and Feature-Sharing Generative Adversarial Networks for Single Image Super-Resolution

Traditional super-resolution (SR) methods by minimize the mean square error usually produce images with over-smoothed and blurry edges, due to the lack of high-frequency details. In this paper, we propose two novel techniques within the generative adversarial network framework to encourage generation of photo-realistic images for image super-resolution. Firstly, instead of producing a single score to discriminate real and fake images, we propose a variant, called Fine-grained Attention Generative Adversarial Network (FASRGAN), to discriminate each pixel of real and fake images. FASRGAN adopts a UNet-like network as the discriminator with two outputs: an image score and an image score map. The score map has the same spatial size as the HR/SR images, serving as the fine-grained attention to represent the degree of reconstruction difficulty for each pixel. Secondly, instead of using different networks for the generator and the discriminator, we introduce a feature-sharing variant (denoted as Fs-SRGAN) for both the generator and the discriminator. The sharing mechanism can maintain model express power while making the model more compact, and thus can improve the ability of producing high-quality images. Quantitative and visual comparisons with state-of-the-art methods on benchmark datasets demonstrate the superiority of our methods. We further apply our super-resolution images for object recognition, which further demonstrates the effectiveness of our proposed method. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Rainyfish/FASRGAN-and-Fs-SRGAN</uri> .
Role and place of Informatics in the training of future teachers of mathematics

Abstract The announcement of the 2020/2021 academic year as the Year of Mathematical Education in Ukraine and the adoption of the Concept for the Development of Natural and Mathematical Education (STEM-education) until 2027 were a response to the systemic crisis of mathematical training in general secondary education and training of mathematics teachers in Ukraine. The World focus on the integration of natural and mathematics, informatical and engineering education requires rethinking of the role and place of Informatics as a science, as a study discipline and as a basis of computer engineering in training future mathematics teachers. Nowadays, the teacher of mathematics should be capable for effective professional activity in a rapidly changing technology, educational paradigms and catastrophic educational disruptions, such as the current COVID-19 pandemic.
XR collaboration beyond virtual reality: work in the real world

Abstract Collaborating in a physically remote location saves time and money. Many remote collaboration systems have been studied and commercialized. Their capabilities have been confined to virtual objects and information. More recent studies have focused on collaborating in a physical environment and with physical objects. However, they have limitations including shaky and unstable views (scenes), view dependence, low scalability, and poor content expression. In this paper, we propose a web-based extended reality (XR) collaboration system that alleviates the aforementioned issues and enables effective, reproducible cooperation. Our proposed system comprises three parts: interaction device webization, which expands the web browser’s device interfaces; unified XR representation, which describes content interoperable in both virtual reality (VR) and augmented reality (AR); and unified coordinate creation, which enables presenting physical objects’ pose in world coordinates. With this system, a user in VR can intuitively instruct the manipulation of a physical object by manipulating a virtual object representative of the physical object. Conversely, a user in AR can catch up with the instruction by observing the augmented virtual object on the physical object. Moreover, as the pose of the physical object at the AR user’s worksite is reflected in the virtual object, the VR user can recognize the working progress and give feedback to the AR user. To improve remote collaboration, we surveyed XR collaboration studies and proposed a new method for classifying XR collaborative applications based on the virtual–real engagement and ubiquitous computing continuum. We implemented a prototype and conducted a survey among submarine crews, most of whom were positively inclined to use our system, to convey that the system would be helpful in improving their job performance. Furthermore, we suggested possible improvements to it to enhance each participant’s understanding of the other user’s context within the XR collaboration.
ABioNER: A BERT-Based Model for Arabic Biomedical Named-Entity Recognition

The web is being loaded daily with a huge volume of data, mainly unstructured textual data, which increases the need for information extraction and NLP systems significantly. Named-entity recognition task is a key step towards efficiently understanding text data and saving time and effort. Being a widely used language globally, English is taking over most of the research conducted in this field, especially in the biomedical domain. Unlike other languages, Arabic suffers from lack of resources. This work presents a BERT-based model to identify biomedical named entities in the Arabic text data (specifically disease and treatment named entities) that investigates the effectiveness of pretraining a monolingual BERT model with a small-scale biomedical dataset on enhancing the model understanding of Arabic biomedical text. The model performance was compared with two state-of-the-art models (namely, AraBERT and multilingual BERT cased), and it outperformed both models with 85% F1-score.
Patch Craft: Video Denoising by Deep Modeling and Patch Matching

The non-local self-similarity property of natural images has been exploited extensively for solving various image processing problems. When it comes to video sequences, harnessing this force is even more beneficial due to the temporal redundancy. In the context of image and video denoising, many classically-oriented algorithms employ self-similarity, splitting the data into overlapping patches, gathering groups of similar ones and processing these together somehow. With the emergence of convolutional neural networks (CNN), the patch-based framework has been abandoned. Most CNN denoisers operate on the whole image, leveraging non-local relations only implicitly by using a large receptive field. This work proposes a novel approach for leveraging self-similarity in the context of video denoising, while still relying on a regular convolutional architecture. We introduce a concept of patch-craft frames – artificial frames that are similar to the real ones, built by tiling matched patches. Our algorithm augments video sequences with patch-craft frames and feeds them to a CNN. We demonstrate the substantial boost in denoising performance obtained with the proposed approach.
Spontaneous Parametric Down‐Conversion Sources for Multiphoton Experiments

Abstract Entangled photon sources are desirable and key elements for photonic quantum information processing. The spontaneous parametric down‐conversion (SPDC) process is a convenient way to produce entangled photon pairs. It has been widely used and has already made great contributions to quantum information science. SPDC sources have been thoroughly studied, and a detailed review, previously lacking, is now appropriate. In this article, different types of SPDC sources suitable for multiphoton experiments are reviewed. A comparison of their respective performances is presented, focusing on the parameters of indistinguishability, collection efficiency, and brightness, from which it is possible to see how the spatial and spectral properties of the SPDC process can be tailored, step by step, to achieve near‐perfect photon pairs. Recent progress on multiplexed heralded single‐photon sources, which effectively mitigate the nondeterministic nature of the SPDC process, is also described.
Heterogeneous Similarity Graph Neural Network on Electronic Health Records

Mining Electronic Health Records (EHRs) becomes a promising topic because of the rich information they contain. By learning from EHRs, machine learning models can be built to help human expert to make medical decisions and thus improve healthcare quality. Recently, many models based on sequential or graph model are proposed to achieve this goal. EHRs contain multiple entities and relations, and can be viewed as a heterogeneous graph. However, previous studies ignore the heterogeneity in EHRs. On the other hand, current heterogeneous graph neural networks cannot be simply used on EHR graph because of the existence of hub nodes in it. To address this issue, we propose Heterogeneous Similarity Graph Neural Network (HSGNN) to analyze EHRs with a novel heterogeneous GNN. Our framework consists of two parts: one is a preprocessing method and the other is an end-to-end GNN. The preprocessing method normalizes edges and splits the EHR graph into multiple homogeneous graphs while each homogeneous graph contains partial information of the original EHR graph. The GNN takes all homogeneous graphs as input and fuses all of them into one graph to make prediction. Experimental results show that HSGNN outperforms other baselines in the diagnosis prediction task.