Reviewerly/exHarmony huggingface dataset:
Multi-Group Multi-Attention

Learning spatiotemporal features is very effective but challenging for video understanding especially action recognition. In this paper, we propose Multi-Group Multi-Attention, dubbed MGMA, paying more attention to "where and when" the action happens, for learning discriminative spatiotemporal representation in videos. The contribution of MGMA is three-fold: First, by devising a new spatiotemporal separable attention mechanism, it can learn temporal attention and spatial attention separately for fine-grained spatiotemporal representation. Second, through designing a novel multi-group structure, it can capture multi-attention rendered spatiotemporal features better. Finally, our MGMA module is lightweight and flexible yet effective, so that can be easily embedded into any 3D Convolutional Neural Network (3D-CNN) architecture. We embed multiple MGMA modules into 3D-CNN to train an end-to-end, RGB-only model and evaluate on four popular benchmarks: UCF101 and HMDB51, Something-Something V1 and V2. Ablation study and experimental comparison demonstrate the strength of our MGMA, which achieves superior performance compared to state-of-the-arts. Our code is available at https://github.com/zhenglab/mgma.
Increasing the Motivation of Teachers for the Continuous Development of Professional and Personal Competencies Based on Reflexive Self-Analysis Technology

The article presents a five-step algorithm of reflexive self-analysis technology, which has been tested and is in demand in the system of additional professional education of teachers, which implements full-time and distance training courses. The technology is aimed at psychological and pedagogical support of the process of continuous personal and professional development of teachers for quality performance of their professional tasks in the field of education, upbringing and development of children in accordance with the requirements of the professional standard “Teacher (teaching activities in pre-school, primary General, basic General, secondary General education) (educator, teacher)” and the task of national Russian project “Education”. The sequence of actions in the algorithm is spelled out from the first stage — the understanding by teachers of the internal reasons for resistance to the continuous development of their own professional and personal competencies, to the last — the development and implementation of an individual route for professional and personal self-development. At the same time, the teacher himself tracks his step-by-step movement along the route — on the basis of reflection on the achieved and designing the latest results.
Semantic Inpainting with Multi-dimensional Adversarial Network and Wasserstein Distance

Inpainting represents a procedure which can restore the lost parts of an image based upon the residual information. We present an inpainting network that consists of an Encoder-Decoder pipeline and a multi-dimensional adversarial network. The Encoder-Decoder pipeline extracts features from the input image with missing area and learns these features. Through unsupervised learning, the pipeline can predict and fill the missing region with the most reasonable content. Meanwhile the multi-dimensional adversarial network identifies the difference between the ground truth and the generated images both in detail and in general. Compared with the traditional training procedure, our model combines with Wasserstein Distance that enhances the stability of network training. The network is training specifically on street view images and not only performs a satisfying outcome, but also shows competitiveness when comparing with existing methods.
Multi-model transfer and optimization for cloze task

Substantial progress has been made recently in training context-aware language models. CLOTH is a human created cloze dataset, which can better evaluate machine reading comprehension. Although the author of CLOTH has done many experiments on BERT and context2wec, it is still worth studying the performance of other models. We applied the CLOTH dataset to other models and evaluated their performance based on different model mechanisms. The results showed that ALBERT performed well on the cloze task. The accuracy of ALBERT is 92.24%, which is 6.34% higher than the human performance. In addition, we introduce adversarial training into the model. Experiments show that adversarial training has significant effects in improving the robustness and accuracy of the model. On the BERT-large model, the accuracy rate is up to 0.15% after using adversarial training.
William Smith's map brought alive by digital remastering

The geological mapping carried out by William Smith, which resulted in the publication of his famous map in 1815, was remarkable in many respects, not least because it relied on him being able to make consistent and accurate observations on the rock types he encountered during his fieldwork. This ability, gained from his many years studying rocks, allowed him to observe features with his own eyes (or at the very least, with the aid of a simple magnifying device) that others could not. We take a new look at William Smith's original stratigraphical sequences, and with samples collected from his classic field areas (many of which are around the city of Bath, Somerset, UK), demonstrate how spatial mineralogy mapping can be incorporated into the modern age of digital mapping.
Object localization based on natural language descriptions for fine-grained image

As a tool to express common semantics of objects, language can be used to describe the attributes and locations of objects within the scope of human vision. Searching for the location of an object in the field of vision through natural language is an important capability of the human. Proposing a mechanism to learn this ability of human is a major challenge for computer vision. Most existing object localization methods usually use strong supervised information of the training set to train the model. However, these models lack interpretability and require expensive labels which are difficult to obtain. Facing these challenges, we propose a new method for locating object by natural language descriptions for fine-grained image. Firstly, we propose a model that can learn the semantically relevant parts between fine-grained images and languages, and achieve ideal localization accuracy without using strong supervisory signal. In addition, we have improved the contrast loss function to make natural language descriptions better match target regions of fine-grained images.The multi-scale fusion techniques are utilized to improve the ability of capturing details on fine-grained images. Comprehensive experiments demonstrate that the proposed method achieves ideal localization results on the CUB200-2011 dataset. And the proposed model has strong zero-shot learning ability on untrained data.
Formation Control Algorithm of Agents Based on Earth Mover’s Distance

Massive sport, such as unmanned aerial vehicle performance, often needs fast and efficient calculation of formation morphing and individual path planning. This paper introduces a novel fast formation control method of a crowd. First, we get the agents’ location in a 2D polygon with centroidal Voronoi tessellation and L-BFGS techniques. Then, we transform crowd formation shapes with a global shortest motion path pair assignment using earth mover’s distance algorithm. Finally, the repulsing force between agents and obstacles is calculated based on the recursive velocity observer method control agents’ motion. Extensive experimental results show the effectiveness and usefulness of our algorithm in 2D group formation transformation.
Quantum random number generation on a photonic chip using single photons from hexagonal boron nitride

Quantum random number generation (QRNG) harnesses the intrinsic randomness of quantum mechanical phenomena. Here, we couple bright room-temperature single-photon emission from a hexagonal boron nitride atomic defect into a laser-written photonic chip and demonstrate QRNG. © 2020 The Author(s)
A DSA-like digital signature protocol

In this paper we propose a new digital signature protocol inspired by the DSA algorithm. The security and the complexity are analyzed. Our method constitutes an alternative if the classical scheme DSA is broken.
Assessing Overall Software Defect-Based Risk Using Analytic Hierarchy Process

Risk analysis and mitigation methods are gaining momentum among researchers and software professionals for preventing software project failures. It involves methods to deal with project uncertainties so that the project can become successful overcoming all threats. In this context, determining the potential risk factors at each phase of software development lifecycle and relating those risks to the defects they produce is important. Analytic Hierarchy Process (AHP) is used to determine the priority of defects that leads to risk. AHP helps in assessing risk for each phase of an agile project. As agile models primarily help projects to accommodate change requests, and with each change request, a new risk items is generated, and AHP-based risk analysis becomes extremely relevant to agile software development. This paper investigates use of AHP for effective decision making to identify and remove defects. Weighted criteria are used for evaluating the decision. To implement meaningful weighted criteria, a case study is conducted among 20 software professionals with various experience levels. Total 35 potential risk source factor-based questions were evaluated according to Saaty’s scale.
JAFPro: Joint Appearance Fusion and Propagation for Human Video Motion Transfer from Multiple Reference Images

We present a novel framework for human video motion transfer. Deviating from recent studies that use only single source image, we propose to allow users to supply multiple source images by simply imitating some poses in the desired target video. To aggregate the appearance from multiple input images, we propose a JAFPro framework that incorporates two modules: an appearance fusion module that adaptively fuses the information in the supplied images and an appearance propagation module that propagates textures through flow-based warping to further improve the result. An attractive feature of JAFPro is that the quality of its results progressively improves as more imitating images are supplied. Furthermore, we build a new dataset containing a large variety of dancing videos in the wild. Extensive experiments conducted on this dataset demonstrate JAFPro outperforms state-of-the-art methods both qualitatively and quantitatively. We will release our code and dataset upon publication of this work.
Experimental entanglement quantification for unknown quantum states in a semi-device-independent manner

Using the concept of non-degenerate Bell inequality, we show that quantum entanglement, the critical resource for various quantum information processing tasks, can be quantified for any unknown quantum state in a semi-device-independent manner, where the quantification is based on the experimentally obtained probability distributions and prior knowledge of the quantum dimension only. Specifically, as an application of our approach to multi-level systems, we experimentally quantify the entanglement of formation and the entanglement of distillation for qutrit-qutrit quantum systems. In addition, to demonstrate our approach for multi-partite systems, we further quantify the geometric measure of entanglement of three-qubit quantum systems. Our results supply a general way to reliably quantify entanglement in multi-level and multi-partite systems, thus paving the way to characterize many-body quantum systems by quantifying the involved entanglement.
Web Service Instant Recommendation for Sustainable Service Mashup

Service Mashups can help users to integrate data of multiple sources based on Web services composition. Considering a kind of sustainable service Mashup whose data requirement cannot be predetermined, so users need to choose and compose services in a tentative manner. Meanwhile, users can choose and compose services continually to obtain more data based on existing composition results. In such Mashups, a Web service is chosen according to the data provided by the service. Because it is difficult for users to choose from large amounts of services manually, it is a challenge to recommend services instantly for users during the construction of a sustainable service Mashup. This paper proposes an approach to recommend Web services instantly for a sustainable service Mashup. According to the services used in the service Mashup under construction, candidate services are chosen based on the Mashups that are similar to the constructing Mashup, as well as the parameter correlations of services from the perspective of actual operations of Web service composition. Experimental results indicate that the proposed approach has better precision, recall, and coverage values compared to existing state-of-the-art approaches, and therefore, it is more suitable for instant service recommendation of sustainable service Mashups.
Earth Science and Biodiversity Journals can Improve Support for Data Sharing

This study reviews research data policies and author instructions of 31 journals from the Earth sciences and from Biodiversity that are published by German learned societies or research institutions. 12 journals don’t address data sharing at all. The statements on data sharing of the journal’s data policies/author guidelines were matched to 14 defined features of journal research data policies. A brief discussion on quality of data policies is presented to raise awareness of German learned societies/research institutions and to guide them towards improved data policies of their journals.
Scale-aware Progressive Optimization Network

Crowd counting has attracted increasing attention due to its wide application prospect. One of the most essential challenge in this domain is large scale variation, which impacts the accuracy of density estimation. To this end, we propose a scale-aware progressive optimization network (SPO-Net) for crowd counting, which trains a scale adaptive network to achieve high-quality density map estimation and overcome the variable scale dilemma in highly congested scenes. Concretely, the first phase of SPO-Net, band-pass stage, mainly concentrates on preprocessesing the input image and fusing both high-level semantic information and low-level spatial information from separated multi-layer features. And the second phase of SPO-Net, rolling guidance stage, aims to learn a scale-adapted network from multi-scale features as well as rolling training manner. For better learning local correlation of multi-size regions and reducing redundant calculations, we introduce a progressive optimization strategy. Extensive experiments on three challenging crowd counting datasets not only demonstrate the efficacy of each part in SPO-Net, but also suggest the superiority of our proposed method compared with the state-of-the-art approaches.
Collaborative filtering recommendation algorithm based on bisecting K-means clustering

The traditional collaborative filtering recommendation algorithm has the problem of data sparsity and expansibility. Aiming at this problem, and improved bisecting k-means collaborative filtering algorithm proposed.The algorithm first removes unrated items in the rating data matrix based on the Weighted Slope One algorithm preprocessing to reduce its sparsity. Then the preprocessed rating data is clustered based on the bisecting K-means algorithm, which reduces the nearest neighbor search space of the target user by assembling similar objects, thereby improving the algorithm’s expansibility. Finally, use the recommendation algorithm to generate the final result.Experimental results show that the improved bisecting k-means algorithm improves the recommendation effect.
Fuzzy Hidden Markov Model in Images Segmentation

In this work, we segment some fuzzy color and grey level images using the fuzzy model of Markov: Fuzzy Hidden Markov Chain, we use three algorithms EM, SEM and ICE for estimating the parameters to this model. We compare these estimators under some criteria of evaluation such as: segmentation quality, running time and convergence and complexity. The results of segmentation show that Fuzzy Hidden Markov Chain provides the same encouragement results under EM, SEM and ICE estimators. Moreover, EM algorithm has the less running time comparing to SEM and ICE, but SEM and ICE converge quickly than EM. They keep the same level of complexity, it is linear O(N) .
Two-photon Comb with Wavelength Conversion for Long-distance Quantum Communication

We demonstrate two-photon comb (TPC) technique and wavelength conversion. TPC consists of telecom entangled photon pairs with narrow linewidth and two-photon coincidence after conversion was observed. 20-km distribution was also achieved aiming at quantum internet.
Особенности внимания современных подростков

The paper describes the characteristics of the cognitive sphere and cognitive processes of the Internet generation. The results of a study of the attention features of adolescents with normal and developmental pathology are presented. Conclusions about the strong development of attention switchability and its volume and the weak stability of attention of adolescents with a normal rate of development due to their active use of digital technologies are discussed.
On public key cryptosystem based on the word problem in a group

The asymmetric encryption methods are based on difficult problems in mathematics. Let G be a group, A group word in G on a set Γ of generators is a string where .The word problem in a group G with respect to a subset Γ is the question of telling whether two words in Γ are equal. It is known that in general the word problem is undecidable, meaning that there is no algorithm to solve it.In this paper, we introduce a cryptosystem based on the word problem in a group G.
Modeling Weighted Graphs with Given Parameters of the Minimum Route Between the Vertices

A number of typical problems that arise during the automatic generation of weighted graphs using conventional methods are considered. Based on their analysis, formal rules have been proposed that can eliminate these shortcomings in the process of constructing such graphs.A set of basic initial parameters of the simulated graph is proposed, which allow setting its structure at the initial stage. According to them, the calculation of the derived numerical parameters of the projected graph is first given, as well as the description of three additionally used auxiliary characteristic arrays.The algorithm for modeling a graph that satisfies given requirements is described in detail. The construction of its matrix of weights is divided into three successive stages: initialization of the full matrix of weights of the edges at the initial ordered definition of the vertices, modeling of the initial block of the matrix, modeling of the full matrix for the full set of vertices.After constructing the complete matrix of weights, the removal of part of the edges of the graph that have the maximum values of the weights is modeled, and the numbers of the vertices of the graph are rearranged. As a result, the final matrix of weights and other characteristics of the graph are finally formed.The proposed algorithm allows guaranteed modeling of meaningful random graphs for which not only the minimum cost of the path, but also the path itself is known for one pair of vertices at the construction stage. The shortest path generation incorporated in the algorithm eliminates the subsequent solution of this problem, which is especially important when modeling graphs with a large number of vertices. By controlling the input parameters, the algorithm allows you to significantly vary the properties of the resulting graph structures.
MaLiang: An Emotion-driven Chinese Calligraphy Artwork Composition System

We present a novel Chinese calligraphy artwork composition system (MaLiang) which can generate aesthetic, stylistic and diverse calligraphy images based on the emotion status from the input text. Different from previous research, it's the first work to endow the calligraphy synthesis with the ability to express fickle emotions and composite a whole piece of discourse-level calligraphy artwork instead of single character images. The system consists of three modules: emotion detection, character image generation, and layout prediction. As a creative form of interactive art, MaLiang has been exhibited in several famous international art festivals.
Deep Dependency Network for Multi-label Text Classification

In multi-label text classification tasks, the effective extraction of text features and the use of correlations between labels are the main starting points to improve the performance of the tasks. This paper utilizes the powerful feature representation ability of deep learning, combined with the intuitionistic and easily extensible label correlations modeling method of conditional dependency network, to propose a Deep Dependency Network (DDN) framework based on label dependencies. The experimental results on a real-world dataset show that the framework proposed in this paper is effective and has good scalability.