Reviewerly/exHarmony huggingface dataset:
A Design Framework of Exploration, Segmentation, Navigation, and Instruction (ESNI) for the Lifecycle of Intelligent Mobile Agents as a Method for Mapping an Unknown Built Environment

Recent work on intelligent agents is a popular topic among the artificial intelligence community and robotic system design. The complexity of designing a framework as a guide for intelligent agents in an unknown built environment suggests a pressing need for the development of autonomous agents. However, most of the existing intelligent mobile agent design focus on the achievement of agent’s specific practicality and ignore the systematic integration. Furthermore, there are only few studies focus on how the agent can utilize the information collected in unknown build environment to produce a learning pipeline for fundamental task prototype. The hierarchical framework is a combination of different individual modules that support a type of functionality by applying algorithms and each module is sequentially connected as a prerequisite for the next module. The proposed framework proved the effectiveness of ESNI system integration in the experiment section by evaluating the results in the testing environment. By a series of comparative simulations, the agent can quickly build the knowledge representation of the unknown environment, plan the actions accordingly, and perform some basic tasks sequentially. In addition, we discussed some common failures and limitations of the proposed framework.
Envisioning networked provenance data storytelling with American cuneiform collections

Abstract Cuneiform tablets remain founding cornerstones of two hundred plus collections in American academic institutions, having been acquired a century or more ago under dynamic ethical norms and global networks. To foster data sharing, this contribution incorporates empirical data from interactive ArcGIS and reusable OpenContext maps to encourage tandem dialogues about using the inscribed works and learning their collecting histories. Such provenance research aids, on their own, initiate the narration of objects’ journeys over time while cultivating the digital inclusion of expert local knowledge relevant to an object biography. The paper annotates several approaches institutions are or might consider using to expand upon current provenance information in ways that encourage visitors’ critical thinking and learning about global journeys, travel archives, and such dispositions as virtual reunification, reconstructions, or restitution made possible by the provenance research.
Cost Accounting Algorithm of Environmental Pollution Control Based on Discrete Probability

Aiming at the problems of high error rate and long time of traditional environmental pollution control cost accounting algorithm, a cost accounting algorithm of environmental pollution control based on discrete probability was designed. Firstly, the cost of environmental pollution is classified and the monetization function of environmental pollution cost is constructed. Then, the cost accounting index system of environmental pollution control is established, and the cost function of environmental pollution control is constructed. Finally, a discrete probability model is used to optimize the cost function, and the optimized cost function is used to design the environmental pollution control cost accounting algorithm. The experimental results show that the proposed algorithm can quickly converge to the optimum within 70 iterations, the accounting error rate is between -0.2% and 1.3%, and the accounting time is always less than 0.4 s. It has good convergence and can accurately calculate the cost of ecological environmental pollution control.
Demonetization and Movement Towards Digital Cashless Indian Economy

One of the greatest painful, un-stabilizing self-imposed macroeconomic blows on the Indian economy occurred in the absence of a short-term crisis when the government decided to announce a major change in the macroeconomic environment by demonetizing the high value currency notes – of Rs 500 and Rs 1000. These two denominations ceased to be legal tender from midnight of 8th of November 2016. The reasons offered for demonetization are two-fold: one, to control counterfeit notes that could be contributing to terrorism, in other words a national security concern; and second, to undermine or eliminate the “black economy.” It has also been a step forward towards a digital cashless economy. In this study an attempt is being made to present a discussion on both the short- and long-run effects of demonetization. It attempts to throw light on the impact of some macroeconomic variables—GDP, sectoral composition, industrial production, inflation, employment—using secondary time-series analysis. The empirical analysis clearly reveals that contraction of currency in circulation was one of the most important factors responsible for decrease in GDP in India after the period of demonetization. Apart from the issue of transition, confronted by the banking system, the government initiative was needed to neutralize the short-term, medium-term, and long-term effects particularly on regularization of cash flows, withdrawals, income, employment, inflation, consumption, and production. Moreover, cyber and other digital security measures were also essential to curb any kinds of frauds and encourage people towards a more digital cashless economy.
Classifying random variables based on support vector machine and a neural network scheme

Support vector machine (SVM) is a supervised machine-learning method which can be used for both classification and regression models. In this paper, we introduce a new model of SVM which any of training samples containing inputs and outputs are considered the random variables with known probability functions. The SVM is first converted into equivalent quadratic programming (QP) formulations in linear and nonlinear cases. An artificial neural network for SVM learning is then proposed. The presented neural network framework guarantees to obtain the optimal solution of the SVM problem. The existence and convergence of the trajectories of the network are studied. The Lyapunov stability for the considered neural network is also shown. The efficiency of the proposed method is shown by four illustrative examples.
Mapping of the loose sediments of glacial and periglacial formations in areas with boreal vegetation using remote sensing

The southeastern Fennoscandian Shield is a specific region dominated by glacial and periglacial originated Quaternary deposits. In dense boreal vegetation zones, direct remote decoding of Quaternary sediments is impossible. Our aim is to assess the efficiency of machine learning algorithms for identification of types of Quaternary deposits based on spectral data of vegetation cover and digital elevation model data. A comparative analysis of two classification methods, discriminant analysis (DA) and classification and regression trees (CARTs), was performed. Two models, DA and CART, were constructed based on a set of spectral variables. They included principal components and spectral indices, such as the normalized difference vegetation index, the normalized difference moisture index, and the clay minerals ratio. When the absolute height and the topographic position index (DA+ and CART+ models) are added to a set of independent variables, the classification of Quaternary sediments becomes more accurate. The nonparametric CART method was shown to be more accurate in differentiating loose deposits. The correctness of the CART+ model for the reference sample of observation points was as high as 90.5%. According to the trained data, mapping of Quaternary deposits was carried out. Kappa analysis showed that the agreement with the map of Quaternary deposits for the CART+ and DA+ models was 0.5 and 0.44, respectively. For the DA and CART models, the agreement was much lower. It is noted that the sets of spectral data for the vegetation cover more clearly show the grain-size composition of loose sediments. To make the classification of Quaternary deposits more correct, morphometric indicators were added. Furthermore, the CART+ method was used to estimate the maximum height of glaciolacustrine sediments (117.5 m), which corresponds to the maximum absolute mark of the periglacial reservoir.
A flexible framework for visualizing and exploring patient misdiagnosis over time

Diagnosis is a complex and ambiguous process and yet, it is the critical hinge point for all subsequent clinical reasoning and decision-making. Tracking the quality of the patient diagnostic process has the potential to provide valuable insights in improving the diagnostic accuracy and to reduce downstream errors but needs to be informative, timely, and efficient at scale. However, due to the rate at which healthcare data are captured on a daily basis, manually reviewing the diagnostic history of each patient would be a severely taxing process without efficient data reduction and representation. Application of data visualization and visual analytics to healthcare data is one promising approach for addressing these challenges. This paper presents a novel flexible visualization and analysis framework for exploring the patient diagnostic process over time (i.e., patient diagnosis paths). Our framework allows users to select a specific set of patients, events and/or conditions, filter data based on different attributes, and view further details on the selected patient cohort while providing an interactive view of the resulting patient diagnosis paths. A practical demonstration of our system is presented with a case study exploring infection-based patient diagnosis paths.
Tools and methods for Edge-AI-systems

Abstract The enormous potential of artificial intelligence, especially artificial neural networks, when used for edge computing applications in cars, traffic lights or smart watches, has not yet been fully exploited today. The reasons for this are the computing, energy and memory requirements of modern neural networks, which typically cannot be met by embedded devices. This article provides a detailed summary of today’s challenges and gives a deeper insight into existing solutions that enable neural network performance with modern HW/SW co-design techniques.
Comparison of Image Blending Using Cycle GAN and Traditional Approach

GANs are a very interesting emerging domain. Implementation of the Neural style transfer technique can be done using particular types of these GANs. In this technique, a particular art style is superimposed over a certain image, resulting in the input image looking like a painting in the same style. A similar result can be achieved through manually mixing the input image and a painting using rudimentary image manipulation techniques. The goal of this paper is to analyse the difference between these outputs using a variety of techniques such as Discrete Cosine Transform and Fourier Transform along with others, to observe the amount of difference in the techniques. We conclude with a discussion on how the manually mixed image can be tweaked to most closely resemble the GAN output.
Awaaz: A Sign Language and Voice Conversion Tool for Deaf-Dumb People

The silent group is confronted with issues all around the world. There are numerous connection issues. Ordinary people can speak with each other through typical oral communication. People who are unable to speak or listen can use sign language as a substitute, but they notice many things when speaking with typical people. As a result, communication is always a possibility. The fact that a speech-impaired individual interacts with others through ineffective gestures is a clear impediment to this communication barrier. The paper's purpose is to bridge the gap between deaf-dumb persons and the rest of society. To convert gesture language to voice, the overall paper is built on machine learning and computer vision. The proposed work is carried out using a large self-made labeled dataset of about 15,000 images. We proposed a deep convolutional neural network for sign and gesture recognition. The model is being deployed using Tkinter GUI for creating Desktop based user application.
Automated Intelligent Hematology Classification System Using Image Processing and Neural Networks

In this paper, a method has been proposed which uses an Image Processing and Deep learning-based approach to classify microscopic blood smear images based on 7 classes of blood diseases namely, Leukemia, Anemia, Lymphoma (CLL, FL, MCL), Myeloma and Malaria, from the healthy blood images. Image preprocessing techniques based on Feature Extraction and Ni-black Thresholding were used on image dataset to obtain features for identification and classification of Leukemia and Anemia. Thereafter, a neural network based on VGG16 was implemented to train the model for classification of all the diseases which included pretrained weights from ImageNet. For validation of the model, the scores of Precision, Recall, and F-score were taken into account to calculate the accuracy of the model. Through this methodology, the model was able to achieve an accuracy of 98.6% with minimum loss of 0.47. The proposed system will help hematologists to identify blood diseases more accurately and faster with this automatic analysis system.
Text to Image Generation Using Gan

Text to image synthesis, one of the most fascinating applications of GANs, is one of the hottest topics in all of machine learning and artificial intelligence. This paper comprises techniques for training a GAN to synthesise human faces and images of flowers from text descriptions. In this paper, we are proposing to train the GAN progressively as proposed in the ProGAN architecture and along with that trying to improve its results by proposing a custom update rule for alpha which controls the fading rate during the progressive growth of the architecture. With experimental testing using the Oxford102 and LFW datasets, our proposed architecture and training process ensures fast learning and smooth transitions between each trained generation.
Truthful auction mechanisms for VNF chain provisioning and allocation across geo-distributed datacenters

With the fast development of network function virtualization (NFV), NFV markets are emerging that allows network service providers (NSPs) to trade different virtual network function (VNF) chains among users. Therefore, efficient mechanisms for VNF chain provisioning and allocation are key to guarantee efficient operations of the NFV markets. One fundamental issue is how to maximize the social welfare of the market such that selfish users have incentives to acquire VNF chains from the markets. To overcome this issue, in this paper, we propose a truthful randomized combinatorial auction mechanism (TRCAM), which integrates the primal–dual approximation algorithm design technique and efficient pricing strategy. This mechanism considers dynamic VNF chain provisioning of the NSP and flexible VNF chain deployment of users. To be specific, we first formulate the social welfare maximization problem as an integer linear program , which is proven to be NP-hard. Then, we propose a primal–dual approximate algorithm which is near-optimal with polynomial-time complexity and employ it as a building block to design the TRCAM. Furthermore, we develop a novel binary-search-based algorithm to boost the average-case performance of the TRCAM. Both strict theoretical analysis and extensive experimental studies demonstrate the effectiveness of our proposed TRCAM, which achieves approximate truthfulness, near-optimal social welfare, individual rationality , and computational efficiency.
Automatic segmentation of intracytoplasmic sperm injection images

In this paper, a multiclass image semantic segmentation problem was solved. For analysis, images of the intracytoplasmic sperm injection process were used. For training the neural network, 656 frames were manually labelled. As a result, each pixel of the images was assigned to one of four classes: microinjector, suction micropipette, oolemma, background. An analysis of modern approaches was carried out and the best architecture, encoders, and hyperparameters of the neural network were selected experimentally: the convolutional neural network FPN (feature pyramid network) with the resnext101 encoder having a depth of 101 layers with 32 parallel separable convolutions. The developed neural network model has allowed obtaining the segmentation efficiency of IOU=0.96 at the algorithm speed of 15 frames per second.
An optimality-theoretic account of the acquisition of English tenses: The case of Moroccan EFL learners

The study intends to describe the acquisition of the English tenses among Moroccan EFL learners. The analysis investigates each tense, employing the constraint demotion algorithm (CDA). The belief here is that errors emerging at the initial learning stages are indicators of L1 transfer. Based on the L1 properties, CDA rearranges the constraints to establish a constraint ranking, identical to that of English, based on the hierarchy detected through universal grammar (UG). Two hundred and twenty students, belonging to three different academic levels, participated in the study. The questionnaire they answered tested tenses through three different tasks. The output stemming from the analysis was that students’ mother tongue plays an important role in the acquisition of the tenses of English. The article concludes that the learners reach an intermediate phase in the interlanguage before eventually learning the correct tense form.
Design of Online Reading Education Simulation System Based on Intelligent Human-Computer Interaction Technology

As an important input for language learning, reading has always been highly valued by linguists. With the rapid development of human-computer interaction technology, its in-depth application in the field of education is also increasing. In this paper, the online reading education simulation system based on intelligent HCIT is discussed, and the simulation system is tested and analyzed by combining k-nearest neighbor algorithm, support vector machine algorithm and maximum entropy algorithm. Finally, the survey analysis shows that the online reading education simulation system based on intelligent HCIT improves the participation rate of reading education to a certain extent. Compared with the traditional reading education model, the efficiency is increased by 7%.
GPU-Friendly FRW Algorithms for Electrostatic Computation Problems

With the advancement of fabrication technology, the electrostatic coupling has increasing impact on the performance of very large-scale integrated (VLSI) circuits and micro-electromechanical systems (MEMS). For the structures in VLSI circuits which are mostly rectilinear geometries, the floating random walk (FRW) method using cubic transition domains has been successfully applied to calculate the electric capacitances among interconnect wires. It is also applicable to the calculation of electric field intensity, which becomes important for nowadays nanometer-technology circuits. In this chapter, an efficient approach is presented to parallelize the FRW algorithm with the graphic processing units (GPUs). GPU-friendly algorithmic flow and data structure are designed to reduce the divergence among random walks and the time of accessing the device memory. The techniques accelerating the extraction of multi-dielectric structures and for extracting multiple nets concurrently are also discussed. Numerical results are presented with several simple structures and larger ones from real VLSI circuits or MEMS. The results validate the accuracy of the presented techniques and demonstrate several tens times speedup due to the GPU-based parallel computing.
Monte Carlo Method for Solving PDE

In this chapter, we present the basics of Monte Carlo method for solving deterministic PDE. We first introduce more theoretic aspects of Monte Carlo method, along with random walk process and method. Then, we introduce the discrete random walk method and floating random walk method, respectively. We try our best to make the presentation not for specific applications, and instead to be of general interest.
UGAN-2G：An Unsupervised Target Image Remodeling Enhancement Network

In facing of the generative adversarial network (GAN) has slow training convergence speed, fuzzy generated image features, and network information separation leads to the lack of image diversity. Combined with U-Net network architecture, this paper proposed a new target image remodeling enhancement algorithm for a dual-gradient U-type generative adversarial network( Encoder10 decoder Generative Adversarial Network with Gradient Fusion and Gradient Penalty, UGAN-2G), which was an unsupervised self-learning generative model, to solve the problem of target image detail loss while enhancing the multitasking of target detection. This method chooses a WGAN-GP network with a gradient penalty, which solves the instability of the generative adversarial network. Moreover, a new generator, the U-type network with a self-mapping gradient fusion, was proposed to lock the target image, which the fusion of the DenseNet idea to break the symmetry of the network, the unsupervised remodeling of the details of the finished image, and improve the network usage efficiency. To enhance the remodeling effect, combine the image pixel loss with the adversarial loss as the generator loss, and the trained model is more robust. Comparison of the algorithm performance in the FFHQ-baby, LSUN, and pavement crack datasets. The FID (Frechet inception distance) values of UGAN-2G decreased by 29.5% and 25.8% over conventional WGAN-GP and DCGAN algorithms, and decreased by 16.3% and 10.1% over deep convolutional UNet-GAN and Re-GAN algorithms, indicating better image quality from UGAN-2G. The IS (Inception score) values of UGAN-2G increased by 23.3% and 21.6% over WGAN-GP and DCGAN algorithms, respectively, and increased by 20% and 14.1% over UNet-GAN and Re-GAN algorithms, indicating a good diversity of images generated by UGAN-2G. The experimental results show that UGAN-2G algorithm integrates deep convolution to enhance learning ability, with rich application scenarios, high target image quality and strong diversity, and accelerates network convergence with good robustness.
A Stronger Stitching Algorithm for Fisheye Images Based on Deblurring and Registration

Fisheye lens, which is suitable for panoramic imaging, has the prominent advantage of a large field of view and low cost. However, the fisheye image has a severe geometric distortion which may interfere with the stage of image registration and stitching. Aiming to resolve these drawbacks, we devise a stronger stitching algorithm for fisheye images by combining the traditional image processing method with deep learning. In the stage of fisheye image correction, we propose the Attention-based Nonlinear Activation Free Network (ANAFNet) to deblur fisheye images corrected by Zhang’s calibration method. Specifically, ANAFNet adopts the classical single-stage U-shaped architecture based on convolutional neural networks with soft-attention technique and it can restore a sharp image from a blurred image effectively. In the part of image registration, we propose the ORB-FREAK-GMS (OFG), a comprehensive image matching algorithm, to improve the accuracy of image registration. Experimental results demonstrate that panoramic images of superior quality stitching by fisheye images can be obtained through our method.