Reviewerly/exHarmony huggingface dataset:
Accelerated Red-Black Strategy for Image Composition Using Laplacian Operator

Digital image composition deals with the problem of embedding portions of the source image into the target image to generate a single desirable image seamlessly.The aim is to produce a new image that combines both source and target images so that the seam between the two images is less noticeable.Image composition based on numerical differentiation using the Laplacian operator to obtain the solution of the Poisson equation is presented.The proposed method employs a red-black strategy to speed up the computation by using an accelerated parameter to the existing relaxation method.This modified variant of the relaxation method known as the Modified Accelerated Over-Relaxation (MAOR) method is derived from the existing Modified Successive Over-Relaxation (MSOR) method.Several examples were tested to examine the effectiveness of the proposed method.The results showed that both modified accelerated variants performed faster than their corresponding standard modified successive variants.
Flow group convolution: group convolution with channels information interaction

Group convolution can significantly reduce the computational cost by dividing the feature map channels into groups and then convolution operation is applied within each group. However, evenly dividing channels will cause the isolation between divided groups, i.e., no interaction between groups. To address the channel isolation problem, we proposed flow group convolution (FGConv) that utilizes different but overlapped input channels to compute output channels and enhance the interaction of different groups. FGConv can be easily applied to the existing networks and reduce their computational cost. We replace the original convolution with FGConv in ResNets and validate them on CIFAR-10 and CIFAR-100 benchmarks. Experiments results demonstrate that FGConv performs better than existing group convolution techniques.
Reimagining Russian Higher Education

In Tyumen, a Siberian city located some 1,000 miles east of Moscow, a radical experiment in Russian higher education is taking place. The School of Advanced Studies (SAS) is an institute that is attempting to bring multidisciplinary, liberal arts-influenced education to Russia. Founded in 2017, SAS operates as an autonomous institution within the state-funded University of Tyumen (UTMN). This article will analyze SAS’s current educational model through data and interviews with faculty, administration, and students. It is divided into five parts. The first section offers background information. We explain how SAS was founded, its source of funding, and why liberal education is an outlier in Russia. The middle three sections take a deeper look at the institution from the perspectives of the administration, students, and faculty. We document their observations about the SAS experiment, highlighting their differing views on what they believe the institute’s mission should be. These sections also analyze which elements of the SAS model are working so far and which ones need further development. The final section sums up our findings: how is SAS, a progressive, liberal experiment, able to exist in a traditional, generally inflexible Russian education system? So far, what are the institution’s successes and failures? And finally, is SAS a fluke experiment, or is there potential to create similar institutions throughout Russia?
Multi-Agent Trust Region Learning

Trust-region methods are widely used in single-agent reinforcement learning. One advantage is that they guarantee a lower bound of monotonic payoff improvement for policy optimization at each iteration. Nonetheless, when applied in multi-agent settings, such guarantee is lost because an agent's payoff is also determined by other agents' adaptive behaviors. In fact, measuring agents' payoff improvements in multi-agent reinforcement learning (MARL) scenarios is still challenging. Although game-theoretical solution concepts such as Nash equilibrium can be applied, the algorithm (e.g., Nash-Q learning) suffers from poor scalability beyond two-player discrete games. To mitigate the above measurability and tractability issues, in this paper, we propose Multi-Agent Trust Region Learning (MATRL) method. MATRL augments the single-agent trust-region optimization process with the multi-agent solution concept of stable fixed point that is computed at the policy-space meta-game level. When multiple agents learn simultaneously, stable fixed points at the meta-game level can effectively measure agents' payoff improvements, and, importantly, a meta-game representation enjoys better scalability for multi-player games. We derive the lower bound of agents' payoff improvements for MATRL methods, and also prove the convergence of our method on the meta-game fixed points. We evaluate the MATRL method on both discrete and continuous multi-player general-sum games; results suggest that MATRL significantly outperforms strong MARL baselines on grid worlds, multi-agent MuJoCo, and Atari games.
Ontop-Spatial: Ontop of Geospatial Databases

In this paper, we propose an OBDA approach for accessing geospatial data stored in relational databases using the R2RML mappings and OGC standard GeoSPARQL. On the theoretical side, we provide a formalization of GeoSPARQL in terms of SPARQL entailment regime. For a practical query answering algorithm, we introduce an extension to the existing SPARQL-to-SQL translation method to support GeoSPARQL features. Our approach has been implemented in the system Ontop-spatial, an extension of the OBDA system Ontop for creating virtual geospatial RDF graphs on top of geospatial relational databases. We present an experimental evaluation of our system using and extending a state-of-the-art benchmark. To measure the performance of our system, we compare it to two state-of-the-art geospatial RDF stores – a free and open-source one, and a commercial one – and confirm its efficiency.
Design and application of component-based automatic test system software

Abstract Aiming at the problems of low reuse and long development cycle caused by the increasing complexity and scale of automatic test system software, this paper describes the design of a component-based automatic test system software. The essence of this design is to design the general functional modules into relatively independent components by analyzing the software requirements of typical test system, and to carry out the research and development of modular software to improve the reuse rate of software modules. The practice of a large number of projects has proved that the software design of componentized automatic test system accelerates the speed of software development and improves the quality of system products.
Network Attentive Payload Allocation in Heterogeneous Cloud like Computing Environment

In fast-growing computing era, many consumers and small-scale organizations are starving for large computing power to run their applications. Rapidly growing technology and limited time usage, restrict users to invest in possessing their own computing power. Whenever there is a need for executing large applications users are looking for an approach where resources are pooled dynamically and execute the application. Majority of existing resource allocation frameworks are concentrating on resource allocation based on CPU cycles, storage, and memory. It is observed that these resource allocation methods can perform even better when network bandwidth is considered depending on the size of the task and efficient sharing of bandwidth among peer users. In this article, we have proposed a novel approach called Network Attentive Payload Allocation [NAPA] for resource scheduling based on the size of the task and available network bandwidth, considering all the users gets essential network bandwidth. Task are allocated to virtual machines using the payload distribution model. By using this model, we have evaluated the performance of the NAPA in cloud sim by editing the broker datacenter package. Experimental results were compared, and it is observed that after applying NAPA the performance of the model is increased fairly.
Recognition of english and russian-language texts based on frequency characteristics

Abstract Distinction of texts in one language from texts in others is necessary to solve the problems of automated text analysis. The paper presents criteria and critical values for recognizing English-language and Russian-language texts. The obtained criteria are estimated by experiments. The paper describes the methods to estimate the size of character codes and to identify a space character in a text. The algorithm for recognizing texts in the English and Russian languages with arbitrary encoding is studied and its accuracy is estimated experimentally.
Grey Wolf Optimizer with Conditional Random Fields for Healthcare Related Medical Named Entity Recognition Model

Named Entity Recognition (NER) finds useful in medical sector to extract the knowledge from unstructured text and offers it in the form of Knowledge Graph (KG). This study proposes a new grey wolf optimizer (GWO) with conditional random fields (CRF), called GWO-CRF model for NER in medical field. The proposed GWO-CRF model involves preprocessing to convert the actual input to a compatible format. Then, CRF model is employed for classification process in which the weight vector of the CRF is tuned by the GWO algorithm. The performance of the GWO-CRF model is tested using benchmark dataset. The obtained results showcased that the GWO-CRF model has resulted to a maximum average precision of 89.46%, recall of 89.08%, and F-score of 89.24%.
Zero-Day Attack Defenses and Test Framework for Connected Mobility ECUs

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Recent developments in the commercialization of mobility services have brought unprecedented connectivity to the automotive sector. While the adoption of connected features provides significant benefits to vehicle owners, adversaries may leverage zero-day attacks to target the expanded attack surface and make unauthorized access to sensitive data. Protecting new generations of automotive controllers against malicious intrusions requires solutions that do not depend on conventional countermeasures, which often fall short when pitted against sophisticated exploitation attempts. In this paper, we describe some of the latent risks in current automotive systems along with a well-engineered multi-layer defense strategy. Further, we introduce a novel and comprehensive attack and performance test framework which considers state-of-the-art memory corruption attacks, countermeasures and evaluation methods. Finally, we demonstrate the ability to deter and prevent in-field zero-day attacks on connected vehicle ECUs.&lt;/div&gt;&lt;/div&gt;
Power law decay of stored pattern stability in sparse Hopfield neural networks

Abstract Hopfield neural networks on scale-free networks display the power law relation between the stability of patterns and the number of patterns. The stability is measured by the overlap between the output state and the stored pattern which is presented to a neural network. In simulations the overlap declines to a constant by a power law decay. Here we provide the explanation for the power law behavior through the signal-to-noise ratio analysis. We show that on sparse networks storing a plenty of patterns the stability of stored patterns can be approached by a power law function with the exponent −0.5. There is a difference between analytic and simulation results that the analytic results of overlap decay to 0. The difference exists because the signal and noise term of nodes diverge from the mean-field approach in the sparse finite size networks.
Effects of Source Texts’ Types on Korean EFL College Learners’ Summary Writing Performance

This study aims to investigate the extent to which source texts’ types affect Korean EFL college learners’ summary writings in terms of lexical, sentential, and discourse-level features. About 51 summary writings of three genres (cause/effect and comparison/contrast expository texts and argumentative texts) were collected and analyzed using a computational assessment tool, Coh-Metrix. The results show that the participants’ summary writings in the three different text types were statistically different according to their lexical-level (the mean word length, word frequency, imageability, concreteness, the third person pronouns), sentential-level (the mean sentence length, causal connectives, temporal connectives, noun density, FRE, FKGL), and discourse-level (type-token ratio, LSA cosines for all and adjacent sentences) features. This study provides some pedagogical implications for teaching various types of English summary writings.
2020 UK Lockdown Cyber Narratives: The Secure, the Insecure and the Worrying

On the 23rd March 2020, the UK entered a period of lockdown in the face of a deadly pandemic. While some were unable to work from home, many organisations were forced to move their activities online. Here, we discuss the technologies they used, from a privacy and security perspective. We also mention the communication failures that have exacerbated uncertainty and anxiety during the crisis.An organisation could be driven to move their activities online by a range of disasters, of which a global pandemic is only one. We seek, in this paper, to highlight the need for organisations to have contingency plans in place for this kind of eventuality.The insecure usages and poor communications we highlight are a symptom of a lack of advance pre-pandemic planning. We hope that this paper will help organisations to plan more effectively for the future.
Converse, Focus and Guess - Towards Multi-Document Driven Dialogue

We propose a novel task, Multi-Document Driven Dialogue (MD3), in which an agent can guess the target document that the user is interested in by leading a dialogue. To benchmark progress, we introduce a new dataset of GuessMovie, which contains 16,881 documents, each describing a movie, and associated 13,434 dialogues. Further, we propose the MD3 model. Keeping guessing the target document in mind, it converses with the user conditioned on both document engagement and user feedback. In order to incorporate large-scale external documents into the dialogue, it pretrains a document representation which is sensitive to attributes it talks about an object. Then it tracks dialogue state by detecting evolvement of document belief and attribute belief, and finally optimizes dialogue policy in principle of entropy decreasing and reward increasing, which is expected to successfully guess the user's target in a minimum number of turns. Experiments show that our method significantly outperforms several strong baseline methods and is very close to human's performance.
A Bayesian Federated Learning Framework with Multivariate Gaussian Product.

Federated learning (FL) allows multiple clients to collaboratively learn a globally shared model through cycles of model aggregation and local model training without the need to share data. In this paper, we comprehensively study a new problem named aggregation error (AE), arising from the model aggregation stage on a server, which is mainly induced by the heterogeneity of the client data. Due to the large discrepancies between local models, the accompanying large AE generally results in a slow convergence and an expected reduction of accuracy for FL. In order to reduce AE, we propose a novel federated learning framework from a Bayesian perspective, in which a multivariate Gaussian product mechanism is employed to aggregate the local models. It is worth noting that the product of Gaussians is still a Gaussian. This property allows us to directly aggregate local expectations and covariances in a definitely convex form, thereby greatly reducing the AE. Accordingly, on the clients, we develop a new Federated Online Laplace Approximation (FOLA) method, which can estimate the parameters of the local posterior by repeatedly accumulating priors. Specifically, in every round, the global posterior distributed from the server can be treated as the priors, and thus the local posterior can also be effectively approximated by a Gaussian using FOLA. Experimental results on benchmarks reach state-of-the-arts performance and clearly demonstrate the advantages of the proposed method.
View-consistent 4D Light Field Depth Estimation.

We propose a method to compute depth maps for every sub-aperture image in a light field in a view consistent way. Previous light field depth estimation methods typically estimate a depth map only for the central sub-aperture view, and struggle with view consistent estimation. Our method precisely defines depth edges via EPIs, then we diffuse these edges spatially within the central view. These depth estimates are then propagated to all other views in an occlusion-aware way. Finally, disoccluded regions are completed by diffusion in EPI space. Our method runs efficiently with respect to both other classical and deep learning-based approaches, and achieves competitive quantitative metrics and qualitative performance on both synthetic and real-world light fields
Sparse self-attention aggregation networks for neural sequence slice interpolation

Abstract Background Microscopic imaging is a crucial technology for visualizing neural and tissue structures. Large-area defects inevitably occur during the imaging process of electron microscope (EM) serial slices, which lead to reduced registration and semantic segmentation, and affect the accuracy of 3D reconstruction. The continuity of biological tissue among serial EM images makes it possible to recover missing tissues utilizing inter-slice interpolation. However, large deformation, noise, and blur among EM images remain the task challenging. Existing flow-based and kernel-based methods have to perform frame interpolation on images with little noise and low blur. They also cannot effectively deal with large deformations on EM images. Results In this paper, we propose a sparse self-attention aggregation network to synthesize pixels following the continuity of biological tissue. First, we develop an attention-aware layer for consecutive EM images interpolation that implicitly adopts global perceptual deformation. Second, we present an adaptive style-balance loss taking the style differences of serial EM images such as blur and noise into consideration. Guided by the attention-aware module, adaptively synthesizing each pixel aggregated from the global domain further improves the performance of pixel synthesis. Quantitative and qualitative experiments show that the proposed method is superior to the state-of-the-art approaches. Conclusions The proposed method can be considered as an effective strategy to model the relationship between each pixel and other pixels from the global domain. This approach improves the algorithm’s robustness to noise and large deformation, and can accurately predict the effective information of the missing region, which will greatly promote the data analysis of neurobiological research.
Individual identification of electronic equipment based on electromagnetic fingerprint characteristics

With the rapid development of communication and computer, the individual identification technology of communication equipment has been brought to many application scenarios. The identification of the same type of electronic equipment is of considerable significance, whether it is the identification of friend or foe in military applications, identity determination, radio spectrum management in civil applications, equipment fault diagnosis, and so on. Because of the limited-expression ability of the traditional electromagnetic signal representation methods in the face of complex signals, a new method of individual identification of the same equipment of communication equipment based on deep learning is proposed. The contents of this paper include the following aspects: (1) Considering the shortcomings of deep learning in processing small sample data, this paper provides a universal and robust feature template for signal data. This paper constructs a relatively complete signal template library from multiple perspectives, such as time domain and transform domain features, combined with high-order statistical analysis. Based on the inspiration of the image texture feature, characteristics of amplitude histogram of signal and the signal amplitude co-occurrence matrix (SACM) are proposed in this paper. These signal features can be used as a signal fingerprint template for individual identification. (2) Considering the limitation of the recognition rate of a single classifier, using the integrated classifier has achieved better generalization ability. The final average accuracy of 5 NRF24LE1 modules is up to 98% and solved the problem of individual identification of the same equipment of communication equipment under the condition of the small sample, low signal-to-noise ratio.
Small Ball Tracking with Trajectory Prediction

We propose small ball tracking with trajectory prediction to track them when the athletes playing with balls at sports field, as shown in Figure 1. This is a challenging task and important for intelligent physical education, especially for the coaches to grasp the accuracy of actions by the players based on the pre-defined rules. The proposed method achieves a good performance on small ball tracking since the designed algorithm incorporates motion, temporal and directional information to predict the trajectory. Experimental results show that the proposed method effectively reduces the number of identity switches and decreases track fragmentation. With the integration of motion, directional information and frames storage, this framework efficiently track small balls when the athletes playing with them.
Exploration on the Cultivation of Compound Innovative Talents for Graduate Students Majoring in Computer in Colleges of Finance and Economics

There are many problems in the cultivation of innovative talents for postgraduates majoring in computer science in finance and economics colleges in terms of training target, cultivation Model, and curriculum system. In particular, the construction of "double first-class" and "emerging engineering" puts forward higher requirements for talent training. This paper focuses on these issues, sums up years of experience in postgraduate training, and explores the cultivation model of computer majors under the background of finance and economics from the aspects of Scientific Orientation of postgraduate innovative talentâ€™s training target, improving the postgraduate compound innovation training curriculum system with financial characteristics, strengthening the construction of interdisciplinary cooperation tutor groups and building an all-round financial feature Innovation platform, strengthening the connection between industry, university and research. These measures are to achieve the goal of cultivating highly distinctive compound innovative talents.