Reviewerly/exHarmony huggingface dataset:
Panoptic segmentation of UAV images with deformable convolution network and mask scoring

Panoptic segmentation is an important method for UAV platforms to implement road condition monitoring and urban planning. In recent years, the panoptic segmentation technology provides more comprehensive information than the current semantic segmentation technology. In this paper, the framework of the panoptic segmentation algorithm is designed for the UAV application scenario. Due to the large target scene and small target of UAV, resulting in the lack of foreground targets in the segmentation results and the poor quality of the segmentation mask. To solve these problems, this paper introduces deformable convolution in the feature extraction network to improve the ability of network feature extraction. In addition, the MaskIoU module is introduced in the instance segmentation branch to improve the overall quality of the foreground target mask. In this paper, a series of data are collected by UAV and organized into UAV_OUC panoptic segmentation dataset. We tested on the UAV_OUC panoptic segmentation dataset. The experimental results on UAV_OUC panoptic benchmark validate the effectiveness of our proposed method.
An Attention Free Transformer

We introduce Attention Free Transformer (AFT), an efficient variant of Transformers \citep{transformer} that eliminates the need for dot product attention. AFT offers great simplicity and efficiency compared with standard Transformers, where the multi-head attention operation is replaced with the composition of element-wise multiplications/divisions and global/local pooling. During training time, AFT has linear time and space complexity w.r.t. both the sequence length and feature dimension; in the autoregressive decoding mode, AFT has constant memory and time complexity per step. We show that, surprisingly, we are able to train AFT effectively on challenging benchmarks, and also to match or surpass the standard Transformer counterparts and other efficient variants. In particular, AFT achieves the state-of-the-art result on CIFAR10 autoregressive modeling with much reduced complexity, and also outperforms several efficient Transformer variants on Enwik8.
Corporate Management in the Age of AI

Recent media reports and press releases have created the impression that Artificial Intelligence (AI) is on the verge of assuming an important role in corporate management. While, upon closer inspection, it turns out that these stories should not always be taken at face value, they clearly highlight AI’s growing importance in management and hint at the enormous changes that corporate leadership may experience in the future. This Article attempts to anticipate that future by exploring a thought experiment on corporate management and AI. It argues that it is not an insurmountable step from AI generating and suggesting expert decisions (which is already common today) to AI making these decisions autonomously. The Article then proceeds based on the assumption that next-generation AI will be able to take over the management of business organizations and explores the corporate law and governance consequences of this development. In doing so, the Article focuses on the fundamental areas of corporate leadership and management structures, managerial liability, and the corporate purpose. It also considers the phenomenon of algorithmic entities and leaderless entities.
Learning Recurrent High-order Statistics for Skeleton-based Hand Gesture Recognition

High-order statistics have been proven useful in the framework of Convolutional Neural Networks (CNN) for a variety of computer vision tasks. In this paper, we propose to exploit high-order statistics in the framework of Recurrent Neural Networks (RNN) for skeleton-based hand gesture recognition. Our method is based on the Statistical Recurrent Units (SRU), an un-gated architecture that has been introduced as an alternative model for Long-Short Term Memory (LSTM) and Gate Recurrent Unit (GRU). The SRU captures sequential information by generating recurrent statistics that depend on a context of previously seen data and by computing moving averages at different scales. The integration of high-order statistics in the SRU significantly improves the performance of the original one, resulting in a model that is competitive to state-of-the-art methods on the Dynamic Hand Gesture (DHG) dataset, and outperforms them on the First-Person Hand Action (FPHA) dataset.
MODEL FOR CAREER GUIDANCE AND PROFESSIONAL SELF-DETERMINATION OF CHILDREN OF DIFFERENT AGE GROUPS BASED ON THE GAMIFICATION PRINCIPLES FOR THE FORMATION OF A DIGITAL FOOTPRINT IN THE CONTINUOUS EDUCATION SYSTEM

He article presents a model forcareer guidance and professional self-determination of children of different age groups based on the principles of gamification, including target, meaningful, technological and effective components. The target component includes two types of goals: an overall goal and specific career guidance goals for each age.The The content component is a digital platform designed to record the digital footprint of each participant, which is formed in the process and as a result of developmental career guidance work. The technological component includes a set of game mechanics applied on the digital platform, which allow recording the number of classes attended by each user for each type of profession. The effective component is associated with the understanding of the effectiveness of career guidance and professional self-determination of children in the continuous education process.
A Scalable Verification Solution for Blockchains

Bitcoin and Ethereum, whose miners arguably collectively comprise the most powerful computational resource in the history of mankind, offer no more power for processing and verifying transactions than a typical smart phone. The system described herein bypasses this bottleneck and brings scalable computation to Ethereum. Our new system consists of a financial incentive layer atop a dispute resolution layer where the latter takes form of a versatile verification game. In addition to secure outsourced computation, immediate applications include decentralized mining pools whose operator is an Ethereum smart contract, a cryptocurrency with scalable transaction throughput, and a trustless means for transferring currency between disjoint cryptocurrency systems.
Aging-Related Bugs Prediction Via Convolutional Neural Network

Software aging refers to the phenomenon of system performance degradation or system crash in long-term running systems, which is mainly caused by Aging-Related Bugs (ARBs). To predict Aging-Related Bugs, previous studies usually focused on manually designing features, which extracted from the programs, and utilized different machine learning algorithms to detect those buggy codes. However, these traditional features often failed to distinguish programs' semantic differences.To explore deeply programs' semantics and make full use of these information, in this paper, we proposed a method, which based on deep learning method to automatically learn programs' semantic features of source codes. Specifically, we utilized Convolutional Neural Network (CNN) to automatically generate more distinguished features which based on the Abstract Syntax Trees (ASTs) of programs. Meanwhile, we combined these features with conventional aging-related metrics for more accurate ARB prediction. Finally, we evaluated our model on Linux and MySQL datasets, the experiment results showed that our approach was better than the baselines. The improvement can be achieved up to 6.9% on Linux, and 24.1% on MySQL in terms of balance, compared to traditional Naive Bayes method. And compared to Naive Bayes with logarithmic transformation, the improvement is 1% and 4.7% respectively.
Risk Analysis in the Application of Financore Information Systems Using FMEA Method

Abstract In the banking world maintaining customer data is very important so as not to experience various risks, including leakage of customer data. Leakage of customer data is one of the big risks that may occur in the banking world, whereas customer confidentiality should be guaranteed. Possible risks such as misuse of identity or theft of transaction data by unauthorized parties. This will certainly harm both the customer and the bank. So that customer data is not leaked, lost or misused, it is necessary to have a regulation or verification system of customer data that can be reviewed for security and use. The solution that can be done to prevent this event is to use the FMEA methods that can be used to reduce errors and system failures. Information system security has a positive and very significant effect on decision making, therefore it is necessary to have an information system risk analysis that supports it comprehensively so that it can help in decision making.
Contagious Defaults in a Credit Portfolio: A Bayesian Network Approach

The robustness of credit portfolio models is of great interest for financial institutions and regulators, since misspecified models translate into insufficient capital buffers and a crisis-prone financial system. In this paper, the authors propose a method to enhance credit portfolio models based on the model of Merton by incorporating contagion effects. While, in most models, the risks related to financial interconnectedness are neglected, the authors use Bayesian network methods to uncover the direct and indirect relationships between credits while maintaining the convenient representation of factor models. A range of techniques to learn the structure and parameters of financial networks from real credit default swaps data are studied and evaluated. Their approach is demonstrated in detail in a stylized portfolio, and the impact on standard risk metrics is estimated.
Research on Multi-feature and Machine Learning Hierarchical Pedestrian Detection Method Based on Deep Learning

Abstract With the rapid development of science and technology, computers have become an important way for people to obtain information. As an important means of transmitting visual information, image plays a very important role. Pedestrian detection is a key research field of machine vision and a key technology in intelligent video surveillance and intelligent transportation system. Pedestrian detection is the basis and premise of pedestrian tracking, behavior analysis, gait analysis and pedestrian identification, and a good pedestrian detection algorithm can provide strong support and guarantee for the latter. Pedestrian detection is not an independent problem, it is closely related to video pedestrian tracking, human behavior recognition and pedestrian identification. The method of machine learning is to let the program have the ability of learning. Through continuous training and iteration, the parameters of the model are optimized. Deep learning can give full play to the speed and rationality of the machine and improve the accuracy of pedestrian detection.
Formal composition of hybrid systems

We develop a compositional framework for formal synthesis of hybrid systems using the language of category theory. More specifically, we provide mutually compatible tools for hierarchical, sequential, and independent parallel composition. In our framework, hierarchies of hybrid systems correspond to template-anchor pairs, which we model as spans of subdividing and embedding semiconjugacies. Hierarchical composition of template-anchor pairs corresponds to the composition of spans via pullback. To model sequential composition, we introduce hybrid systems, each of which flows from an initial subsystem to a final subsystem in a Conley-theoretic sense. Sequential composition of directed systems is given by a pushout of graph embeddings, rewriting the continuous dynamics of the overlapping subsystem to prioritize the second directed system. Independent parallel composition corresponds to a categorical product with respect to semiconjugacy. To formalize the compatibility of these three types of composition, we construct a vertically cartesian double category of hybrid systems where the vertical morphisms are semiconjugacies, and the horizontal morphisms are directed hybrid systems.
Propaedeutics of Professional Digital Skills for First-Year Students at an Economic University

Global digitalization requires transformations in the educational process. General and special pedagogical procedures for ensuring digital literacy are being developed and revised. The global academic pedagogical community identifies, structures and studies the types of digital skills and the demand for them in different economic sectors. The Financial University under the Government of the Russian Federation aims to train specialists and help them gain full professional proficiency in modern digital technologies. The most efficient way to achieve this is to create a comprehensive multifaceted education system that runs through the entire educational process from the first year until graduation. The authors propose a propaedeutic methodology for developing professional digital skills as part of the subject Computer Practicum for first-year bachelors of economics. The methodology is built on the principles of continuity and coherence and is aimed at further development of digital skills within the subject Data Analysis. The authors identify the following components of the methodology: the development of basic technological skills for a digital economy specialist; hands-on approach to the educational process; development of communication skills; interactive forms of communication. The authors provide their experience of adding practical content to the methodology in the process of organizing classes. MS Excel and RStudio packages are considered the primary IT-tools.
Sequential UI behaviour prediction system based on long short-term memory networks

In this paper, we propose a method for user interface (UI) behaviour prediction in commercial applications. The proposed method predicts appropriate UI behaviours for an application by learning repeated UI behaviour sequences from users. To this end, we adopted the long short-term memory algorithm based on the evaluation of a keystroke-level model. Our prediction model takes up to seven consecutive actions as inputs to predict the final UI actions that a user is likely to perform. We verified the effectiveness of the proposed method for both PC applications and mobile game environments. Our experimental results demonstrate that the proposed system can predict user UI behaviours in an application on the client side and provide useful behavioural information for optimising UI layouts.
The application of Lanczos interpolation in video scaling system based on FPGA

This paper proposes a video scaling hardware system based on Lanczos algorithm to achieve real-time high-quality scaling of video resolution at any ratio.The traditional image scaling algorithm has the problem that the interpolation result and the calculation speed cannot be ideal at the same time. The speed of bilinear interpolation and nearest neighbor interpolation algorithm is very high, but mosaics and blurred edges will occur. The interpolation effect of bicubic interpolation is better but the calculation speed is very slow. Experimental results show the interpolation effect of Lanczos algorithm is better than the traditional interpolation algorithm, and the time cost is less than bicubic interpolation. Real-time scaling of the video signal can be achieved in the FPGA system designed in this paper.
A Modification of Convolutional Neural Network Layer to Increase Images Classification Accuracy

Image classification is one of the fundamental steps in digital image processing. Research in this area has received considerable attention, with photos shared on social media which are sometimes similar but have different identities. There are various classification methods proposed in the literature to improve accuracy. One important strategy is Convolutional Neural Networks (CNN). Although CNN is superior in pattern recognition, it has limitations inaccuracy. It requires additional training time, especially when dealing with variants in data generated from a large number of images but of similar properties. Therefore, this study aims to overcome this problem by proposing a modification of the CNN layer to increase the accuracy of the multi-class image classification. This research used four different flower species with similar patterns added from a public database. Each category consists of 400 colour images with different angles, backgrounds, and lighting conditions that provide different variations to the training process. Through experiments using 1,600 of the four flower species, this study shows that the 18-34 layer modification produces the most optimal accuracy in the training process ranging from 99.3% with misclassification of MSE =0.0025, RMSE = 0.1606, and MAE = 0. 0133. Meanwhile, the computation time required to compile the data set is 3 minutes, 18 seconds. This result is 50% faster when compared to computation time using existing architecture such as Alexnet model with a similar number of layers.
Randomized Automatic Differentiation

The successes of deep learning, variational inference, and many other fields have been aided by specialized implementations of reverse-mode automatic differentiation (AD) to compute gradients of mega-dimensional objectives. The AD techniques underlying these tools were designed to compute exact gradients to numerical precision, but modern machine learning models are almost always trained with stochastic gradient descent. Why spend computation and memory on exact (minibatch) gradients only to use them for stochastic optimization? We develop a general framework and approach for randomized automatic differentiation (RAD), which can allow unbiased gradient estimates to be computed with reduced memory in return for variance. We examine limitations of the general approach, and argue that we must leverage problem specific structure to realize benefits. We develop RAD techniques for a variety of simple neural network architectures, and show that for a fixed memory budget, RAD converges in fewer iterations than using a small batch size for feedforward networks, and in a similar number for recurrent networks. We also show that RAD can be applied to scientific computing, and use it to develop a low-memory stochastic gradient method for optimizing the control parameters of a linear reaction-diffusion PDE representing a fission reactor.
Nuclei Segmentation in Histopathology Images Using Rotation Equivariant and Multi-level Feature Aggregation Neural Network

The histopathological analysis is the gold standard for assessing the presence and many complex diseases, like tumors. As one of the essential part of tumors, the shape, staining, and tissue distribution of the nuclei plays an important role in tumor diagnosis. However, due to nuclei congestion and possible occlusion, nuclei segmentation remains challenging. In this paper, we propose an automatic and effective nuclei segmentation method in histopathology images based on rotation equivariant and multi-level feature aggregation neural network (REMFANet). First, considering the inherent rotation equivariant of digital pathological images, we introduce group equivariant convolutions to improve the performance of the automatic segmentation of pathological images. Second, to eliminate the semantic gap between shallow features and deep features in encoder-decoder structural models, we propose a multi-level feature aggregation strategy based on U-Net 3+. Specifically, (1) we design a new decoder module to restore pixel-level predictions more accurately; (2) we propose an improved long-skip connection mode to provide richer semantic information in the decoder; (3) we also construct a semantic enhancement block to enhance the robustness of lowlevel semantic information. Finally, we evaluate our REMFA-Net on the MoNuSeg dataset and compare the results with seven state-of-the-art methods. Experimental results demonstrate the superiority of the proposed method over other models for the nuclei segmentation in histopathology images.
Qubit-efficient exponential suppression of errors

Achieving a practical advantage with near-term quantum computers hinges on having effective methods to suppress errors. Recent breakthroughs have introduced methods capable of exponentially suppressing errors by preparing multiple noisy copies of a state and virtually distilling a more purified version. Here we present an alternative method, the Resource-Efficient Quantum Error Suppression Technique (REQUEST), that adapts this breakthrough to much fewer qubits by making use of active qubit resets, a feature now available on commercial platforms. Our approach exploits a space/time trade-off to achieve a similar error reduction using only $2N+1$ qubits as opposed to $MN+1$ qubits, for $M$ copies of an $N$ qubit state. Additionally, we propose a method using near-Clifford circuits to find the optimal number of these copies in the presence of realistic noise, which limits this error suppression. We perform a numerical comparison between the original method and our qubit-efficient version with a realistic trapped-ion noise model. We find that REQUEST can reproduce the exponential suppression of errors of the virtual distillation approach, while out-performing virtual distillation when fewer than $3N+1$ qubits are available. Finally, we examine the scaling of the number of shots $N_S$ required for REQUEST to achieve useful corrections. We find that $N_S$ remains reasonable well into the quantum advantage regime where $N$ is hundreds of qubits.
Research Progress on Chaos, Memory and Neural Network Circuits Based on Memristor

Memristor is the fourth basic electronic component in addition to resistor, capacitor and inductor. It is a nonlinear device with memory characteristics, which can be used to design chaotic circuits, memory devices and neural networks. The design of memristor-based chaos circuits, memory and neural systems, and some research of neural dynamics in this field are reviewed, and their research prospects are also given.
A Survey for target tracking on Meanshift algorithms

Target tracking has always been a key problem in the field of computer vision. The research on target tracking algorithm is mainly divided into the research on generative model tracking algorithm and the research on discriminative model tracking algorithm. This paper first introduces the Meanshift based target algorithm, Then the development and improvement of Meanshift target tracking algorithm in anti-jamming (occlusion, illumination), target fast moving, scale and direction estimation are reviewed in detail.
Reinforcement Learning for Improving Object Detection

The performance of a trained object detection neural network depends a lot on the image quality. Generally, images are pre-processed before feeding them into the neural network and domain knowledge about the image dataset is used to choose the pre-processing techniques. In this paper, we introduce an algorithm called ObjectRL to choose the amount of a particular pre-processing to be applied to improve the object detection performances of pre-trained networks. The main motivation for ObjectRL is that an image which looks good to a human eye may not necessarily be the optimal one for a pre-trained object detector to detect objects.