Reviewerly/exHarmony huggingface dataset:
Empirical Laws of Natural Language Processing for Neural Language Generated Text

In the domain of Natural Language Generation and Processing, a lot of work is being done for text generation. As the machines become able to understand the text and language, it leads to a significant reduction in human involvement. Many sequence models show great work in generating human like text, but the amount of research work done to check the extent up to which their results match the man-made texts are limited in number. In this paper, the text is generated using Long Short Term Memory networks (LSTMs) and Generative Pretrained Transformer-2 (GPT-2). The text by neural language models based on LSTMs and GPT-2 follows Zipf’s law and Heap’s law, two statistical representations followed by every natural language generated text. One of the main findings is about the influence of parameter Temperature on the text produced. The LSTM generated text improves as the value of Temperature increases. The comparison between GPT-2 and LSTM generated text also shows that text generated using GPT-2 is more similar to natural text than that generated by LSTMs.
A Multi-path Neural Network for Hyperspectral Image Super-Resolution

The resolution of hyperspectral remote sensing images is largely limited by the cost and commercialization requirements of remote sensing satellites. Existing super-resolution methods for improving the spatial resolution of images cannot well integrate the correlation between spectral segments and the problem of excessive network parameters caused by high-dimensional characteristics. This paper studies a multipath-based residual feature learning method, which simplifies each part of the network into several simple and effective network modules to learn the spatial spectral features between different spectral segments. Through the designed multi-scale feature generation method based on wavelet transform and spatial attention mechanism, the non-linear mapping ability for features is effectively improved. The verification of three general hyperspectral data sets proves the superiority of this method compared with the existing hyperspectral SR methods.
Beyond the Map: Engaging Audiences with GIS at the Leventhal Map &amp; Education Center

The Leventhal Map & Education Center (LMEC) at the Boston Public Library (BPL) has expanded its activities in recent years to include educational and technical support for audiences working with geospatial data, tools and concepts. At the K-12 level, LMEC’s Empowering Maptivists initiative engages Boston public high school students in a deep exploration of the uses of geospatial data and mapping in order to help foreground relevant community issues and propose possible solutions. In its role as a partner with the BPL, the LMEC also offers a suite of free, helpful resources for any library visitor, including one-on-one project support and digital guides geared toward decoding and translating the sometimes-complicated intricacies of geospatial data and tools. The LMEC’s recently grant-funded Data Hub project accelerates this momentum by further expanding educational activities, and allowing the LMEC to build out the necessary technical infrastructure to support them, including a searchable and browsable digital library to hold geospatial data, as well as other learning materials for deciphering data.
Learning from Noisy Similar and Dissimilar Data

With the widespread use of machine learning for classification, it becomes increasingly important to be able to use weaker kinds of supervision for tasks in which it is hard to obtain standard labeled data. One such kind of supervision is provided pairwise in the form of Similar (S) pairs (if two examples belong to the same class) and Dissimilar (D) pairs (if two examples belong to different classes). This kind of supervision is realistic in privacy-sensitive domains. Although the basic version of this problem has been studied recently, it is still unclear how to learn from such supervision under label noise, which is very common when the supervision is, for instance, crowd-sourced. In this paper, we close this gap and demonstrate how to learn a classifier from noisy S and D labeled pairs. We perform a detailed investigation of this problem under two realistic noise models and propose two algorithms to learn from noisy SD data. We also show important connections between learning from such pairwise supervision data and learning from ordinary class-labeled data. Finally, we perform experiments on synthetic and real-world datasets and show our noise-informed algorithms outperform existing baselines in learning from noisy pairwise data.
Image Super-Resolution via Generative Adversarial Networks Using Metric Projections onto Consistent Sets for Low-Resolution Inputs

This paper addresses single image super-resolution (SR) based on convolutional neural networks (CNNs). It is known that recovery of high-frequency components in output SR images of CNNs learned by the least square errors or least absolute errors is insufficient. To generate realistic high-frequency components, SR methods using generative adversarial networks (GANs), composed of one generator and one discriminator, are developed. However, when the generator tries to induce the discriminator's misjudgment, not only realistic high-frequency components but also some artifacts are generated, and objective indices such as PSNR decrease. To reduce the artifacts in the GAN-based SR methods, we consider the set of all SR images whose square errors between downscaling results and the input image are within a certain range, and propose to apply the metric projection onto this consistent set in the output layers of the generators. The proposed technique guarantees the consistency between output SR images and input images, and the generators with the proposed projection can generate high-frequency components with few artifacts while keeping low-frequency ones as appropriate for the known noise level. Numerical experiments show that the proposed technique reduces artifacts included in the original SR images of a GAN-based SR method while generating realistic high-frequency components with better PSNR values in both noise-free and noisy situations. Since the proposed technique can be integrated into various generators if the downscaling process is known, we can give the consistency to existing methods with the input images without degrading other SR performance.
Ship identity authentication security model based on Blockchain

The accuracy of ship information directly determines the efficiency of ship management and the safety of navigation, which are important in shipping management for ensuring the truthfulness, reliability and credibility of ship information. Therefore, to meet the needs of shipowners, shipping authorities and third-party users, this paper studies the ship information authentication system based on the blockchain smart contracts and security mechanisms and proposes a framework for storing and sharing ship information based on blockchain in multichannel mode. In the framework, token-based authentication, attribute-based fine-grained permission control, and a combined security model based on the transmission encryption of double asymmetric certificates are developed to authentication the identity of each agent and control the access rights of ship information in the blockchain data storage mode. We use the automated verification tool Automated Validation of Internet Security Protocols and Applications to verify the security of the modeladditionally, the performance of the system is tested and analyzed, and the optimal parameter range is obtained. The proposed system can solve the long-standing trust and security problems of ship communication while guaranteeing performance.
Architectural Aspects of a Data-Intensive System: A Covid-19 Related Case Study

The Covid-19 pandemic caused serious turbulences in most aspects of humans activities. Due to the need to address the epidemic developments at extreme scales, ranging from the entire population of the country down to the level of individual citizens, a construction of adequate mathematical models faces substantial difficulties caused by lacking knowledge of the mechanisms driving transmission of the infections and the very nature of the resulting disease. Therefore, in modeling Covid-19 and its effects, a shift from the knowledge-intensive systems paradigm to the data-intensive one is needed. The current paper is devoted to the architecture of ProME, a data-intensive system for forecasting the Covid-19 and decision making support needed to mitigate the pandemics effects. The system has been constructed to address the mentioned challenges and to allow further relatively easy adaptations to the dynamically changing situation. The system is mainly based on open-source solutions so can be reproduced whenever similar challenges occur.
Encoding and Refuge Shelter by Embracing Steganography with Hybrid Methods in Image Reduction Processing

The assessment chooses to propose a mutt technique to move the data from the source to the objective securely. Security is something imperative on the Web. Numerous programmers will assault the information during transmission. In some cases, the data does not hold for what it is worth at the spot of the objective. In a current framework, the information is installed with the picture. Thus, this technique has a disadvantage with a few restrictions, and commotion is distinguished with this strategy. Pixel abandonment is a significant clash in the current framework. It was shut to find the intruder which getting to the steganography system, and it serves as the ciphertext to the different sides of the beginning to end structure. It guaranteed and produced the security as vital for the sensitive information has been ensured to that. At the same time, the encoded information pack is itself proof of the proximity of critical data. Steganography works out in the right way past and makes the ciphertext vague to unapproved clients. In the proposed framework, the disadvantage defeats by the mixture strategy. The half and half methodology contains MD5, DES, and 3DES. Here, the pictures are changed over into ciphertext by applying the steganography imaging framework (SIS). These strategies installed the content inside the image and sent it without the deficiency of pixels.
Diary Entries by N.P. Kiselev as the Secretary of the Publishing House “Musaget”

For the history of the Moscow Symbolist publishing house “Musaget”, which in the early years of its existence was a circle of like-minded people who sought occult knowledge, in many respects the key years were 1913–1914. The fascination with anthroposophy of some of the employees of Musaget caused rejection on the part of the founder of the publishing house, E.K. Metner, who defended the principle of serving culture, and not one of the spiritual teachings. The diary of N.P. Kiselev, who took up the post of secretary in the publishing house during the separation, reflects not only the daily life of the editorial office, but also attempts to reunite different directions of “Musaget”.
Fairly Private Through Group Tagging and Relation Impact

Privacy and Fairness both are very important nowadays. For most of the cases in the online service providing system, users have to share their personal information with the organizations. In return, the clients not only demand a high privacy guarantee to their sensitive data but also expected to be treated fairly irrespective of their age, gender, religion, race, skin color, or other sensitive protected attributes. Our work introduces a novel architecture that is balanced among the privacy-utility-fairness trade-off. The proposed mechanism applies Group Tagging Method and Fairly Iterative Shuffling (FIS) that amplifies the privacy through random shuffling and prevents linkage attack. The algorithm introduces a fair classification problem by Relation Impact based on Equalized Minimal FPR-FNR among the protected tagged group. For the count report generation, the aggregator uses TF-IDF to add noise for providing longitudinal Differential Privacy guarantee. Lastly, the mechanism boosts the utility through risk minimization function and obtain the optimal privacy-utility budget of the system. In our work, we have done a case study on gender equality in the admission system and helps to obtain a satisfying result which implies that the proposed architecture achieves the group fairness and optimal privacy-utility trade-off for both the numerical and decision making Queries.
Generating Multi-type Temporal Sequences to Mitigate Class-Imbalanced Problem

From the ad network standpoint, a user’s activity is a multi-type sequence of temporal events consisting of event types and time intervals. Understanding user patterns in ad networks has received increasing attention from the machine learning community. Particularly, the problems of fraud detection, Conversion Rate (CVR), and Click-Through Rate (CTR) prediction are of interest. However, the class imbalance between major and minor classes in these tasks can bias a machine learning model leading to poor performance. This study proposes using two multi-type (continuous and discrete) training approaches for GANs to deal with the limitations of traditional GANs in passing the gradient updates for discrete tokens. First, we used the Reinforcement Learning (RL)-based training approach and then, an approximation of the multinomial distribution parameterized in terms of the softmax function (Gumble-Softmax). Our extensive experiments based on synthetic data have shown the trained generator can generate sequences with desired properties measured by multiple criteria.
Shuffling-SinGAN: Improvement on Generative Model from a Single Image

Abstract Recently, SinGAN takes the use of GANs into a new realm – unconditional generation learned from a single natural image. Following the SinGAN architecture, we propose Shuffling- SinGAN, an efficient unconditional generative model that trained on a single natural image for general image manipulation. Our new network includes a pyramid of fully convolutional GANs, in which each layer is responsible for learning the patch distribution at a different scale of the image. We can generate new samples with variability through our network, which have the function of maintaining the texture and global structure of the original image. New random image generated by the model after multiple training is different from the original image in detail. Inspired by sinIR, we decided to add random pixel shuffling to the network. After experimentation, we found that the changed model generated more random new samples. Shuffling-SinGAN allows generating new samples of arbitrary size and aspect ratio, that have significant variability, yet maintain both the global structure and the fine textures of the training image. User tests confirm that the generated samples are commonly confused to be real images. With quantitative evaluation, we show that Shuffling-SinGAN has competitive performance on random image generation.
FastPSO: Towards Efficient Swarm Intelligence Algorithm on GPUs

Particle Swarm Optimization (PSO) has been widely used in various optimization tasks (e.g., neural architecture search and autonomous vehicle navigation), because it can solve non-convex optimization problems with simplicity and efficacy. However, the PSO algorithm is often time-consuming to use, especially for high-dimensional problems, which hinders its applicability in time-critical applications. In this paper, we propose novel techniques to accelerate the PSO algorithm with GPUs. To mitigate the efficiency bottleneck, we formally model the PSO optimization as a process of element-wise operations on matrices. Based on the modeling, we develop an efficient GPU algorithm to perform the element-wise operations in massively parallel using the tensor cores and shared memory. Moreover, we propose a series of novel techniques to improve our proposed algorithm, including (i) GPU resource-aware thread creation to prevent creating too many threads when the number of particles/dimensions is large; (ii) designing parallel techniques to initialize swarm particles with fast random number generation; (iii) exploiting GPU memory caching to manage swarm information instead of allocating new memory and (iv) developing a schema to support customized swarm evaluation functions. We conduct extensive experiments on four optimization applications to study the efficiency of our algorithm called “FastPSO”. Experimental results show that FastPSO consistently outperforms the existing CPU-based PSO libraries by two orders of magnitude, and transcends the existing GPU-based implementation by 5 to 7 times, while achieving better or competitive optimization results.
Back Propagation Optimization of Convolutional Neural Network Based on the left and the right hands Identification

N owadays, medical plastic gloves are sorted into the left and the right hands manually with low efficiency during productive process. In this paper, an automated way is proposed to improve this situation through establishing a convolutional neural network model for image recognition. The back propagation process of learning and training is analyzed in order to optimize the weight by adopting the combination of different activation layers and different loss functions. For the same learning times, there are two evaluation indexes. One is the result of recognition accuracy in the training set, the other is the convergence curve and oscillation amplitude of the loss function. Finally, the adaptability of the combinations is discussed, which plays an important role in improving the recognition accuracy of the left and the right hand.
Dep-$$L_0$$: Improving $$L_0$$-Based Network Sparsification via Dependency Modeling

Training deep neural networks with an \(L_0\) regularization is one of the prominent approaches for network pruning or sparsification. The method prunes the network during training by encouraging weights to become exactly zero. However, recent work of Gale et al. [] reveals that although this method yields high compression rates on smaller datasets, it performs inconsistently on large-scale learning tasks, such as ResNet50 on ImageNet. We analyze this phenomenon through the lens of variational inference and find that it is likely due to the independent modeling of binary gates, the mean-field approximation [], which is known in Bayesian statistics for its poor performance due to the crude approximation. To mitigate this deficiency, we propose a dependency modeling of binary gates, which can be modeled effectively as a multi-layer perceptron (MLP). We term our algorithm Dep-\(L_0\) as it prunes networks via a dependency-enabled \(L_0\) regularization. Extensive experiments on CIFAR10, CIFAR100 and ImageNet with VGG16, ResNet50, ResNet56 show that our Dep-\(L_0\) outperforms the original \(L_0\)-HC algorithm of Louizos et al. [] by a significant margin, especially on ImageNet. Compared with the state-of-the-arts network sparsification algorithms, our dependency modeling makes the \(L_0\)-based sparsification once again very competitive on large-scale learning tasks. Our source code is available at https://github.com/leo-yangli/dep-l0.
Epsilon Consistent Mixup: Structural regularization with an adaptive consistency‐interpolation trade‐off

In this paper, we propose Epsilon Consistent Mixup (∈mu). ∈mu is a data‐based structural regularization technique that combines Mixup's linear interpolation with consistency regularization in the Mixup direction, by compelling a simple adaptive tradeoff between the two. This learnable combination of consistency and interpolation induces a more flexible structure on the evolution of the response across the feature space and is shown to improve semi‐supervised classification accuracy on the SVHN and CIFAR10 benchmark datasets, yielding the largest gains in the most challenging low label‐availability scenarios. Empirical studies comparing ∈mu and Mixup are presented and provide insight into the mechanisms behind ∈mu's effectiveness. In particular, ∈mu is found to produce more accurate synthetic labels and more confident predictions than Mixup.
Developing the communicative competency among prospective engineers

In this article we review the current status of the investigated problem in both Russian and international science and practice. We specify the attributes of the scope covered by the basic concept of “communicative competency” as well as its components and types; we provide theoretical justification for pedagogical conditions promoting the effective development of communicative competency among prospective engineers during their chemistry studies. According to the stated objective, the hypothesis was based on the assumption that the development of communicative competency among prospective engineers during their chemistry education will be effective when the following pedagogical conditions are met: setting up scenarios of success for university students in the process of studying chemistry, engaging students in interactive dialogs during the performance of team tasks (with subsequent explanation of educational materials to each other, mutual assessment and its justification) and using elements of problem-based learning.
Investigation of frequency and step responses of pressure sensors with semi-closed structures based on numerical simulations and experiments

We are developing a pressure sensor to detect very small pressure fluctuations produced during tsunami formation. For example, such a pressure fluctuation of± 50 Pa was actually observed in the 2011 Great East Japan Earthquake. The sensor has a semi-closed structure with a small hole which provides a unique high-pass filter function. Cutoff frequency of the high-pass characteristic is an important index to detect pressure fluctuations due to tsunami formation without interfering with normal atmospheric pressure change. Cutoff frequency can be evaluated from frequency response of the sensor. On the other hand, the step response of the sensor is also significant in providing a unique index, called the ‘duration time of induced pressure difference,’ which is defined as the time required for the pressure difference between the inside and outside of the semi-closed structure to disappear. If a connection between the cutoff frequency and the duration time can be established, the cutoff frequency could be estimated from the duration time obtained by a one-step simple measurement of the step response. In this study, this connection was considered based on the simulated results of frequency and step responses dependent on small-hole area, semi-closed-structure volume and pressure amplitude. Then, the connection was experimentally verified using a fabricated prototype of the semi-closed structure.