Reviewerly/exHarmony huggingface dataset:
Efficient Deep Learning Framework with Group Convolution for Segmentation of Histopathology Image

Automatic detection and segmentation of histopathology images to diagnose and treatment of cancer disease are the demand of today’s time. A deep learning framework is very effective to do this task as much related research has revealed their outcomes. While going deeper into the network to extract more relevant features, the total number of trainable parameters increases, which makes the model more complex, and there is always a chance of overfitting. In this paper, we address the problem of increasing complexity in terms of the total number of trainable parameters by incorporating the standard convolution method with depth-wise separable convolution called group convolution in encoder–decoder architecture. The dataset we have used is 730 hematoxylin and eosin (H&E) stained kidney dataset and 50 H&E stained triple-negative breast cancer (TNBC) dataset. Our proposed approach yields very good results because standard convolution with depth-wise separable convolution effectively reduced the complexity while maintaining model accuracy.
A Deformable Convolutional Neural Network with Oriented Response for Fine-Grained Visual Classification

Fine-grained visual classification (FGVC) aims to classify images belonging to the same basic category in a more detailed sub-category. It is a challenging research topic in the field of computer vision and pattern recognition in recent years. The existing FGVC method conduct the task by considering the part detection of the object in the image and its variants, which rarely pays attention to the difference in expression of many changes such as object size, posture, and perspective. As a result, these methods generally face two major difficulties: 1) How to effectively pay attention to the latent semantic region, and reduce the interference caused by many changes in pose and perspective; 2) How to extract rich feature information for non-rigid and weak structure objects. In order to solve these two problems, this paper proposes a deformable convolutional neural network with oriented response for FGVC. The proposed method can be divided into three main steps: firstly, the local region of latent semantic information is localized based on a lightweight CAM network; then, the deformable convolutional ResNet-50 network and the rotation-invariant coding oriented response network are designed, which input the original image and local region into the feature network to learn the discriminant features of rotation invariance; finally, the learned features are embed into a joint loss to optimize the entire network end-to-end. Experiments are carried out on three challenging FGVC datasets, including CUB-200-2011, FGVC_Aircraft and Aircraft_2 datasets. The results show that the accuracy of the proposed method on all datasets is better than the comparison method, which can effectively improve the accuracy of weakly supervised FGVC.
An ensemble learning method for variable selection and its application on Railroad Fatal Accidents.

Abstract Scalars based on linear and generalized linear models are commonly used in fatality and disease predictions. Currently, standard approaches for variable selection in linear and generalized linear models are not explored well with high dimensional data. This manuscript proposed a new variable selection method for logistics models with inspiration from heterogeneous ensemble methods. The goal of this study is to extend classical variables selection methods such as stepwise, lasso, or RF in the case of high dimensional accident data with missing values. Real world accidents-collated data sets are collected from the official website of US Railroad Administration and US Police Department from 2000 to 2015. Preprocessing includes log transformation and z score transformation. Simulated sampling was applied due to the imbalanced outcome. The base variables was selected from the Empirical Model (EM) [1], Lasso model[2], Xgboost[3] and the traditional stepwise backward. An intermediate selection was proposed by ensemble training of base variables. Final models built by variables selected improved Area Under the Curve (AUC) from 0.79 to 0.84 and from 0.79 to 0.84 in test data compared to arbitrary APS model. Comparison with the existing model shows that the logistics model based on selected variables has high accuracy.
Learned Image Compression with Super-Resolution Residual Modules and DISTS Optimization

Neural network-based image compressors have the ability to optimize various perceptual image quality metrics. We propose improved methods that is based on selective-detail decoding, which uses two decoders (a main decoder and selective-detail decoder) optimized for different image-quality metrics and applies the output result of a suitable decoder for each part of an image. The following three improvements are obtained with the proposed method. (1) Inspired by the super-resolution task, we add a super-resolution residual module to the main decoder, which is trained to up-sample an image to a resolution beyond the source image, aiming to output a visually clearer image. (2) To improve the perceptual image quality of the main decoder, we use an image quality metric based on Deep Image Structure and Texture Similarity (DISTS), the similarity of which is close to that of human senses with respect to texture. (3) To improve the mask accuracy for decoder selection, cross entropy loss is used for comparing predicted masks and ground truth masks. We also use the weighted mean squared error to improve the visual quality of the text part of an image.
USING OF LSTM NETWORKS IN SENTIMENT ANALYSIS OF DOCUMENTS IN KAZAKH LANGUAGE

Due to the growing trust in information in social media resources, interest in the field of sentiment analysis is growing. Because sentiment analysis is one of the main technologies for monitoring the opinions of millions of users of social networks. The article discusses the use of LSTM networks in the analysis of the tonality of texts in the Kazakh language. For training the neural network, 1000 user reviews of mobile phones were used. The experiments were carried out in two ways: in the first case, preprocessing of the analyzed reviews was carried out, in the second case, the preprocessing was not carried out. The average value of the metric for assessing the quality of the pre-processed model reached 80%. This indicator is 11% higher than for a model trained on data without preprocessing. The results of the study allowed us to conclude that the preprocessing of the texts improves the quality of the model.
Outdoor training for students in the context of distance education

The article reveals the main problems of art and pedagogical education in teaching students to paint in the open air practice in the conditions of distance learning. The modern educational environment has put forward new conditions for the methodology of teaching the fine arts using computer technology. Primary data processing on the development of a theoretical and methodological substantiation of the traditional system of teaching painting, existing IT technologies and programs that allow distance learning in different modes, confirm the possibility of using a new direction in artistic pedagogy, which reveals the potential for the implementation of new pedagogical ideas at a high effective level. Modeling a new educational process includes the development of new curricula, not only in the field of traditional education, but also the participation of IT technologies in the adaptation of electronic learning systems to working conditions in the open air, in various modes of transferring audio and video information. Traditional curricula should also be modernized to fit the structure of an e-learning resource. The most successful solution to this educational problem will allow bringing the system of artistic training of students to a new educational level, since will allow remotely conducting classes by leading highly qualified specialists from various regions and countries.
Decoding of Lifted Affine-Invariant Codes

Lifted Reed-Solomon codes, a subclass of lifted affine-invariant codes, have been shown to be of high rate while preserving locality properties similar to generalized Reed-Muller codes, which they contain as subcodes. This work introduces a simple bounded distance decoder for (subcodes of) lifted affine-invariant codes that is guaranteed to decode up to half of an asymptotically tight bound on their minimum distance. Further, long q-ary lifted affine-invariant codes are shown to correct almost all error patterns of relative weight $\frac{{q - 1}}{q} - \varepsilon $ for ε > 0.
Simulation and sensitivity of isotopic alteration and vein development in carbonate rock formations

Isotopic alteration of carbonate rocks during hydrothermal flow is common across a range of geological settings, can be used to identify areas of greater mineralization potential, and is a guide to mineral exploration. To understand potential controls on the variability of this isotopic alteration, we created a series of models to investigate parameter sensitivity. The resulting variograms for δ18O distribution closely matched the variograms for the initial porosity and demonstrate the importance of porosity in controlling isotopic variation. This result shows the utility of variograms for investigating the correlation between hydrologic parameters and isotopic distribution and vectoring toward ore deposits. The mass transfer variables between flowing and stagnant zones within the model were the strongest secondary control on isotopic alteration. The alteration of the carbonate rocks releases 18O, creating a pulse of enriched fluid that flows through the system. The isotopic variation of the solid phase decreases with distance from the inlet because of mixing and the increased size of the 18O-enriched pulse that travels through the system within the fluid phase. The isotopic pulse seen in the model matches isotopic records preserved in zoned calcites from the Upper Mississippi Valley. We also inferred from the model that when sampling isotopic systems, sample intervals taken from large intervals of core can reasonably be used to determine the average alteration but that characterizing the variation is sensitive to sample spacing and frequency.
An Efficient Numerical Method for Reaction-Diffusion Problems Based on Particle Swarm Optimization Algorithm

In this paper, based on the standard particle swarm optimization algorithm, an efficient numerical method for solving a singularly perturbed reaction-diffusion problem on a Shishkin mesh. An upwind finite difference scheme on a Shishkin mesh is developed to approximate the integro-differential equation transformed from the singularly perturbed reaction-diffusion equation. In order to obtain the best the Shishkin mesh parameters and first-derivative of the exact solution at point $$x=0$$ , we construct a nonlinear unconstrained optimization problem, which is solved by using the PSO algorithm. Compared with the other algorithms, the PSO algorithm can obtain more accurate numerical results, which demonstrate the feasibility and effectiveness of the presented method.
Dynamic Regularization on Activation Sparsity for Neural Network Efficiency Improvement

When deploying deep neural networks in embedded systems, it is crucial to decrease the model size and computational complexity for improving the execution speed and efficiency. In addition to conventional compression techniques, e.g., weight pruning and quantization, removing unimportant activations can also dramatically reduce the amount of data communication and the computation cost. Unlike weight parameters, the pattern of activations is directly related to input data and thereby changes dynamically. To regulate the dynamic activation sparsity (DAS), in this work, we propose a generic low-cost approach based on winners-take-all (WTA) dropout technique. The network enhanced by the proposed WTA dropout, namely DASNet , features structured activation sparsity with an improved sparsity level. Compared to the static feature map pruning methods, DASNets provide better computation cost reduction. The WTA dropout technique can be easily applied in deep neural networks without incurring additional training variables. More importantly, DASNet can be seamlessly integrated with other compression techniques, such as weight pruning and quantization, without compromising accuracy. Our experiments on various networks and datasets present significant runtime speedups with negligible accuracy losses.
Autonomic Cloud Computing: A Survey Using IBM SPSS Tool

Cloud is a huge scale, heterogeneous, complex system. Due to uncertainty and dispersion of resources, cloud faces problems of allocation of resources, which is caused by things such as heterogeneity, dynamism, and failures. Unfortunately, existing resource management techniques, frameworks, and mechanisms are insufficient to handle these environments, applications, and resource behaviors. They require mechanized and coordinated strong methodologies for secure, dependable, and cost-effective management of the system. Hence, there is a need of self-manageable properties like self -properties of autonomic computing. In this chapter, we have tried to identify the challenges in autonomic cloud computing with the help of a questionnaire survey; a structured questionnaire in this direction was prepared with 44 items and 200 respondents analyzed the results using IBM SPSS Tool.
Multidimensional optical video signals superposition for measurement offset, rotation angle, and scale with additive and multiplicative noise

The estimation of the offset, angle rotation, and other deformation of multidimensional optical video signals is an important task at intelligence video systems. One of the main tasks for estimation of the multidimensional signal deformation is increasing the speed of data processing. The television signals characterized a significant volume of data. This property is a constraint for applying brute force methods. Those methods are the universal solution for estimating unknown parameters of the parametric model. This research describes the issue of measurement offset, rotation, and scale with additive and multiplicative noise for spatial-temporal superposition of television images. The reduction of time processing is provided by the iteration procedure of unknown parameters estimation. This procedure consists of approximation by separate estimate offsets, scale, and rotates.
Object Detection in Driving Video Based on Improved Mask R-CNN

This paper will explore the optimization of object detection for driving video. MobileNet is used instead of ResNeXt as backbone of Mask R-CNN, and the model is lightweight under the premise of ensuring detection accuracy to adapt to future road monitoring and intelligent driving. During the Mask R-CNN training and learning process, redundant neural network channels are trimmed to accelerate the model’s computing speed. Experimental results show that our method is effective and robust which increases the accuracy of object detection.
Decision Support System with Multi Criteria Decision Making Technique

Abstract An assistant is a person who is assigned to help with the distribution of paperwork in the office. It requires males and females on staff. The assistant is in charge of assisting the smooth functioning of these activities of the office. It is not easy to find a good assistant. For this election, I could use a set of tools that can assist me in determining whether the recommendations given should be good. The Multi-Criteria Decision-Making method can be used to provide recommendations for the selection. In this method, one of the methods is Multi-factor Evaluation Process (MFEP). Each item will be rated on a set of criteria, and the ratings will be compared for the highest rating possible. Afterward, the result will be ranked the highest. Our findings will appear after we have first considered the highest score due to the order in which the scores are generated. If vou apply the mobile network application method to the service, vou can make a smooth and fast network reception.
Optimization of Local Contrast Factor with Adaptive Brightness Improvement: Impact on Mammogram Image Analysis

Mammography has been known worldwide as the most common imaging modalities utilized for early detection of breast cancer. The mammographic images produced are in greyscale, however they often produced low contrast images, contain artefacts and noise, as well as non-uniform illumination. These limitations can be overcame in the pre-processing stage with the image enhancement process. Therefore, in this research we developed an optimized enhancement framework where the local contrast factor is manipulated to preserve details of the image. This method aims to improve the overall image visibility without altering histogram of the original image, which will affect the segmentation and classification processes. We performed dark background removal in the image histogram at early stage to increase the efficiency of new mean histogram calculation. Then, the histogram is separated into two partitions to allow histogram clipping process to be conducted individually for underexposed and overexposed areas. Consequently, the local contrast factor optimization is conducted to preserve the image details. The results from our proposed method are compared with other methods by the measurement of peak signal-to-noise ratio, structural similarity index, average contrast, and average entropy difference. The results portrayed that our proposed method yield better quality over the others with highest peak signal-to-noise ratio of 32.676. In addition, in terms of qualitative analysis, our proposed method depicted betterlesion segmentation with smoother shape of the lesion.
A Hierarchical Scene Text Detector Concerning Hard Examples

Nowadays, the scene text detection approaches based on deep learning have already achieved promising performances. However, due to the insufficient use of the intermediate feature layer information of the neural network, and the learning of the indistinguishable scale and hard examples, it is difficult to improve the accuracy and the recall. Therefore, based on the Fully Convolutional Networks (FCNs), we propose a hierarchical scene text detector concerning hard examples. Specifically, our proposed detector HST-DHE directly predicts text lines of arbitrary orientations in full images without pre-defined anchor boxes. And the feature pyramid is constructed to predict the features at different levels to make full use of the feature information in the middle layers. At the same time, the loss function is redesigned to make the network focus on hard examples and further improve the precision of text detection. Experiments on standard datasets including ICDAR 2013, ICDAR 2015 and MSRA-TD500 demonstrate that the proposed algorithm significantly outperforms state-of-the-art detectors. According to the results, our proposed method has stronger robustness in multi-scale and multi-direction natural scene images.
Computer Information Processing Technology in Financial Accounting Information System

Under the traditional accounting process, the information provided by the accountant still cannot meet the needs of corporate management. This research mainly discusses the application of computer information processing technology in financial accounting information system. Based on the business data and analysis data, the pre-defined data warehouse model can use data conversion service tools such as SQL Server to extract, organize and load data from the business database to the data warehouse. The system will generate accounting vouchers according to the established rules. Accounting personnel only need to maintain the corresponding classification and summary mechanism, which realizes the accounting information systematization of all tasks from data collection to data output, greatly liberating the basic workload of accounting personnel. Authorization approval is carried out by means of digital signatures, realizing a networked and digital financial process. After the implementation of network finance, business processing speed has been accelerated by 27.5%, and the accuracy of production and procurement plans has been increased from 30% to more than 90%. This research will help advance the process of enterprises to realize computerized accounting.
The feasibility and inevitability of stealth attacks

Abstract We develop and study new adversarial perturbations that enable an attacker to gain control over decisions in generic Artificial Intelligence (AI) systems including deep learning neural networks. In contrast to adversarial data modification, the attack mechanism we consider here involves alterations to the AI system itself. Such a stealth attack could be conducted by a mischievous, corrupt or disgruntled member of a software development team. It could also be made by those wishing to exploit a ‘democratization of AI’ agenda, where network architectures and trained parameter sets are shared publicly. We develop a range of new implementable attack strategies with accompanying analysis, showing that with high probability a stealth attack can be made transparent, in the sense that system performance is unchanged on a fixed validation set which is unknown to the attacker, while evoking any desired output on a trigger input of interest. The attacker only needs to have estimates of the size of the validation set and the spread of the AI’s relevant latent space. In the case of deep learning neural networks, we show that a one-neuron attack is possible—a modification to the weights and bias associated with a single neuron—revealing a vulnerability arising from over-parameterization. We illustrate these concepts using state-of-the-art architectures on two standard image data sets. Guided by the theory and computational results, we also propose strategies to guard against stealth attacks.
On the Computational Efficiency of Catalyst Accelerated Coordinate Descent

This article is devoted to one particular case of using universal accelerated proximal envelopes to obtain computationally efficient accelerated versions of methods used to solve various optimization problem setups. We propose a proximally accelerated coordinate descent method that achieves the efficient algorithmic complexity of iteration and allows taking advantage of the data sparseness. It was considered an example of applying the proposed approach to optimizing a SoftMax-like function, for which the described method allowing weaken the dependence of the computational complexity on the dimension n in \(\mathcal {O}(\sqrt{n})\) times and, in practice, demonstrates a faster convergence in comparison with standard methods. As an example of applying the proposed approach, it was shown a variant of obtaining on its basis some efficient methods for optimizing Markov Decision Processes (MDP) in a minimax formulation with a Nesterov smoothed target function.