Reviewerly/exHarmony huggingface dataset:
Global environmental monitoring from space

we are facing global environmental issues such as global warming, ozone depletion, deforestation, air pollution, ocean pollution, desertification, etc. Homeostasis among geosphere, atmosphere, cryosphere, hydrosphere, biosphere has to be maintained. In order for Global Sustainability, global environmental monitoring from space is needed. The monitoring system does work for not only global environmental monitoring, but also disaster mitigation such as Sulawesi Big Earthquake and Tsunami which was occurred on 28 September 2018. Other than environmental monitoring from space, modeling and simulation, big data analysis, Artificial intelligence are also needed.
Digital literacy unpacked

Digital Literacy Unpacked is an apt title for a book on a concept that is hard to define and is perhaps only now mature enough for a discussion on what it is, who is responsible for teaching it and...
Research on the Evaluation of Urban Skeleton Road Network and External Freeway Network Fusion

The reasonable connection between the external freeway network and the urban road network can alleviate the traffic pressure in the city. The fusion evaluation between the external freeway network and the urban road network are studied. Firstly, considering from three aspects of the facilities and the service performance and the connection point operation quality, the evaluation indicator system of two networks is established. Then, based on the cloud model and fuzzy comprehensive evaluation method, the fusion evaluation method based on cloud model - fuzzy comprehensive evaluation method of two networks is established, and the fusion evaluation process is determined. Finally, the effectiveness and scientific of the fusion evaluation method are verified by a case analysis of the skeleton road network and the external freeway of Changsha city.
Using online update of distributional semantics models for decision-making support for concepts extraction in the domain ontology learning task

Most of the information processed by computer systems is presented in the form of text corpuses. The number of such texts (as well as the corpus as a whole) only increases with time, and therefore the word processing tasks remain relevant to this day. Ontology allows to describe semantics using domain concepts and relations between them [1, 2]. In the ontology learning task, the ontology is dependent on quality of corpus which may not be readily available. There are different approaches to creating ontologies (including the use of different tools and frameworks). This paper discusses the use of word2vec (group of related models that are used to produce word embeddings) using online vocabulary update and extension of the original data corpus with additional training for the domain concepts extraction to automate the domain ontology creation.
Providing order to the handwritten TLS task: A complexity index

Text Line Segmentation (TLS) methods are intended to locate and separate text lines in document images for different stages of image analysis such as word spotting, keyword search, text alignment, text recognition and other stages of indexation involved in the retrieval of information from handwrit ten documents. The design of the proposed methods for the TLS and the tuning of their parameters assume a level of complexity according to the language and the writing style of a document collection. Therefore, the performance of these methods is not maintained against documents of greater or lesser complexity. In this paper, we present TLS-ICI, a TLS Intrinsic Complexity Index that allows measuring the complexity of a document for the TLS task, without the necessity of a human gold standard. Through experimentation, we demonstrate how our proposed TLS-ICI provides an order to both the TLS methods and the image-based handwritten documents. In this way, with our proposed complexity index it is possible to select the most appropriated method for each document of a collection, reducing the time spent in exhaustive tests and increasing the performance. In addition, we demonstrate through a new hybrid TLS method that the TLS-ICI outperforms previous individual TLS methods. The dataset consists of several standard TLS collections of contemporary and ancient texts from different languages and alphabets such as English, Spanish, Arabic, and Chinese, Greek, Khmer, Persian, Bengali, Oriya, Kannada and Nahuatl.
An Improved LBP Feature Based Moving Object Detection

Moving object detection (MOD) is strongly restricted by the change of dynamic background. In this paper, a novel MOD algorithm based on improved Local Binary Patterns (LBP) is proposed. Firstly, the LBP is improved to overcome the shortcomings of LBP operator. Then the improved LBP is used in MOD to extract moving object in natural scenes. The experimental results show that the proposed MOD method can extract moving object more accurately.
Irrigation Maintenance Priority Analysis (Case Study: Irrigation Areas in Salatiga City)

The absence of a standard reference and limited funds for determining the classification of irrigation maintenance and rehabilitation activities in the Salatiga become an important research nowadays. Budget funds for 14 irrigation areas come different sources. Those funds are limited only to support the maintenance activities, while the rehabilitation activities rely on funds from the provincial and central governments that are not easily to be approved. Therefore, it is necessary to analyse the classification and the priority of irrigation activities. In this research, the grouping analysis employs two assessment criterias, namely; physical condition of irrigation networks and irrigation performance indices. Assessment of irrigation performance indices according to PU Minister's Regulation Number 32/PRT/M/2007 is for technical irrigation status, therefore it is necessary to identify adjustments to existing conditions. The priority analysis of irrigation maintenance activities in this research uses Analytical Network Process (ANP) method. The criterias that are investigated are irrigation performance indices, area width, irrigation status, estimated cost and distance of warehouse to intake. In this research, 4 groups of identifying the classification of irrigation status, i.e. i) technical irrigation, ii) semi-technical irrigation with weir main building, iii) semi-technical irrigation with spring pond main building and iv) simple irrigation, are used. The results of the grouping analysis showed that 11 irrigation areas are included in irrigation maintenance activities. The results of the priority analysis of irrigation maintenance activities from the first to the last priority are; Kedungkopyah, Cengek, Kedawung, Andong, Banyuputih, Siluwing, Sijamban, Bonorejo, Siandran, Sijambe, and Plampeyan. By determining the priority of irrigation maintenance, the budget allocation will be more targetted.
A Chinese Text Classier Based on Strong Class Feature Selection and Bayesian Algorithm

For improve the efficiency and accuracy of Chinese text categorization, this paper presents a new Chinese text classier, in which word segmentation system is based on forward scan corpus, and word frequency statistics method is different in the training stage and the test stage, after that, a novel feature selection is proposed according to word frequency, mutual information and classificatory information, Finally a fast Bayes theory classier is designed. Experiments prove this classier is simple and effective.
Unrestricted Random Sampling of data batch to improve the efficiency of neural networks

One of the main sources of the overfitting problem is the co-adaptation of the machine learning algorithm with the training data set [1].
Arbitrated Quantum Signature Protocol with Asymmetric Key Based on Qubit Block Encryption

Here, The new arbitrated quantum signature (AQS) protocol assisted by asymmetric key based on qubit block encryption algorithm is given. Interestingly, our protocol with the asymmetric key can be easily extended to multi-party verification. Unlike all previous quantum works on arbitrated quantum signature, the encryption algorithm of the pre-sented protocol is qubit block encryption. Since, the proposed protocol has a better performance in resisting existential forgery attack. In addi-tion, with the help of the arbitrator, it can be proved that our protocol is secure.
Selection of Core Words from Textual Patent Data with DEA based on Citation

The web includes enormous data such as patents. The purpose of this research finds the rule of textual patent data and creates new model. Hence, we suggest new weighted method using DEA to handle unstructured data like patent. Our proposed method is advantageous because this considers the value of the patent compared with TF-IDF and other weighted methods. Using suggested method, we probe new text-mining in the field of patent.
An investigation on the relationship between distortions in the motion magnified videos, and the choice of filter bank

Phase-based motion magnification is one the most recent advances in computer vision and computational photography that has introduced new approaches for structural dynamics identification. Unwanted artifacts and distortions are often present in the motion-magnified videos that can be traced back to the reconstruction procedure after manipulating the phase variations. Within this article, we have studied this phenomenon more closely to find out the relations between the distortions and the choice of filter bank, and the magnification factor. It has been shown that Fourier transformation can reconstruct motion-magnified frames without inducing any distortions, and on the other hand using other filter banks such as Gabor wavelets will generate motion-magnified frames that have distortions in them. The source of these distortions are also discussed briefly.
Minimal condition for shortest vectors in lattices of low dimension

For a lattice, finding a nonzero shortest vector is computationally difficult in general. The problem becomes quite complicated even when the dimension of the lattice is five. There are two related notions of reduced bases, say, Minkowski-reduced basis and greedy-reduced basis. When the dimension becomes d = 5, there are greedy-reduced bases without achieving the first minimum while any Minkowski-reduced basis contains the shortest four linearly independent vectors. This suggests that the notion of Minkowski-reduced basis is somewhat strong and the notion of greedy-reduced basis is too weak for a basis to achieve the first minimum of the lattice. In this work, we investigate a more appropriate condition for a basis to achieve the first minimum for d = 5. We present a minimal sufficient condition, APG+, for a five dimensional lattice basis to achieve the first minimum in the sense that any proper subset of the required inequalities is not sufficient to achieve the first minimum.
18. Rules, tools, and trends of the EGFL Laboratory

The last two decades, between the end of the last century and the start of this century, have marked an extraordinary increase in interest on themes related to the relationship between ‘food’ and ‘law’, and between ‘foodstuffs’ and ‘health’. This led to inevitable questions on the identification of a ‘food law’, which cannot be resolved in a brief formula, descriptive but not identificative, and that rather leads to identifying a disciplinary system, not reduced to minute precepts, but characterised by elements of systematic coherence and of specific identity. The attention has been focused on the ‘object’ and the ‘principles’ typical of the subject, and at the same time on the elements capable to identify the ‘tools’ proper of this area, tools which make food law a ‘matter of law’, as well as a ‘matter of rights’. The paper examines the relationship between national and European regulations and identifies in the food production chain, the ‘identity’ and ‘systematic’ standard of a discipline that is together necessarily European and by its very nature includes all the stages, from agricultural production, to processing and final distribution. The result is a ‘food law’ that is the expression of a special way of being a system, in which intertwine national, regional and community plans, the liability of private individuals and interventions by subjects equipped with persuasive and scientific authority, as well as with powers in the classic sense, which can be reduced to units only when read through the subsidiarity standard and within the complexity of the regulating subjects, in a dual interpretation, vertical and horizontal, which requires knowledge tools suitable for this complex multitude of sources.
Random feedback shift registers and the limit distribution for largest cycle lengths

Abstract For a random binary noncoalescing feedback shift register of width $n$ , with all $2^{2^{n-1}}$ possible feedback functions $f$ equally likely, the process of long cycle lengths, scaled by dividing by $N=2^n$ , converges in distribution to the same Poisson–Dirichlet limit as holds for random permutations in $\mathcal{S}_N$ , with all $N!$ possible permutations equally likely. Such behaviour was conjectured by Golomb, Welch and Goldstein in 1959.
Domestic and Foreign Earnings Quality Literature Review

The quality of corporate earnings has always been a research hotspot in the academic and practical circles of economics and finance.The study of earnings quality can help investors adopt a more optimized investment strategy.From 1968, Ball and Brown began to study the earnings quality.Through years of hard work, the majority of scholars have made fruitful results in the study of earnings quality.This paper analyzes the connotation and quantification of earnings quality, the influencing factors of earnings quality, the economic consequences of earnings quality, and the international comparison of earnings quality.A review of the quality literature provides a basis for subsequent research.
Optimization of Mapping Based on Points Mark

Mapping based on points mark is a widely used method in the process of digital surveying and mapping. The traditional mapping method based on points mark is difficult to meet the needs of rapid mapping with the use of advanced surveying and mapping instruments such as RTK and non prism total station in surveying and mapping. This paper presents an optimized method of mapping based on points mark through the in-depth analysis of the characteristics of the method, and gives the block diagram and idea of the implementation of the method based on the AutoCAD platform, in order to reduce the unnecessary information input and raise the efficiency of the elevation number mapping.
A New Method for Differential Cryptanalysis on Addition on Residue Class Ring Z/2<sup> <i>n</i> </sup>Z

In the paper, we present a new method to study the differential properties of addition modulo 2n using the formulas of coefficients of sum of 2-adic integers.
Reliability assessment on human activity recognition

Recognition of activity of daily living (ADL) with ubiquitous sensors has been studied so far, aiming to provide services like automatic life logging, elderly monitoring and energy saving in domestic environments. Although existing studies achieve good accuracy of ADL recognition on average, mis-classification of some activities often occur. In this paper, we try to minimize mis-classification in ADL recognition through reliability assessment of the recognition results obtained by machine learning. Specifically, we propose a novel ADL recognition model which extends the random forest classifier trained by ADL data-set by adding the real time uncertainty propagation of the measured variables to each decision tree providing thus the confidence probability of each output class. This adds to the classifier output a confidence value that holds an important role for many purposes such as decision making, features design to improve the classification rate for some classes, etc. The proposed model classifies the input data samples into activity classes with high confidence probability (e.g., more than 50% confidence) and an unclassifiable class, where higher confidence probability leads to the higher recognition accuracy but higher ratio of unclassifiable samples. Through experiments, we confirmed that the proposed model achieve 75% accuracy with less than 30% unclassifiable samples and 95% accuracy with 50% unclassifiable samples.
PC-Corpus: A Persian-Chinese Parallel Corpora

With the intense use of neural networks in machine translation of NLP as the latest and most promising approach, neural machine translation (NMT), however, has a serious weakness in terms of corpus availability. This type of machine translation though has achieved a significantly remarkable results for the rich-resource language pairs but it has a challenging performance for the low-resource language pairs. Persian-Chinese is one of the low-resource language pairs that lacks a large scale freely available parallel dataset for training translation models. In this paper we illustrate the creation of bilingual Persian-Chinese corpus (PC-Corpus), which is the very first corpus for this language pair. Our work is significantly considerable for the future of machine translation on Persian-Chinese language pair.
Metrology of 3D NAND in electron micrographs by scale space snakes

Metrology of 3D NAND device architecture is challenging due to structural complexity, low signal to noise and contrast to noise ratio in the electron micrographs. Efficient, automated tools that can measure critical dimensions of 3D NAND in electron micrographs can be a part of solution for process monitoring, uniformity control and structural modelling through OCD, CD-SEM, e-beam tech., etc. In this paper we present an automated technique based on a snake algorithm in a multi-stage, scale-space framework to delineate continuous interfaces between different materials/layers of 3D NAND cells. The snake algorithm takes an initial contour and forces the initial contours to move and deform towards the interface between different material layers using an iterative energy minimization process while preserving the continuity and smoothness of the contour. At the end of energy minimization, the interface between different materials such as central hole, core silicon oxide, poly silicon, tunneling silicon oxide, silicon nitride with storage function, blocking silicon oxide, outer metal layers, etc., are delineated and marked by labelled contours. Prior knowledge, if available, about the number of material-layers and approximate distance between them can be used to improve the efficiency of the process. The proposed method transforms the micrograph into a digital metrology image where critical dimensions such as thickness of the materials, shape of the material layers, etc., can be automatically measured. The additional information provided by continuous contours can be used with ‘Big Data’ analytics to uncover patterns, variations, and outliers that may go unnoticed in discrete measurement data.
Remote sensing data acquisition system based on FPGA sampling time variable

The microwave imager is based on remote sensing data acquisition system to conduct remote sensing observation of the earth. The sampling mode and sampling time affect the Microwave Imager. After analyzing the traditional remote sensing data acquisition system, when collecting multi-channel data, the sampling interval time and sampling start time can't be controlled, and multi-channel data acquisition can't be carried out in parallel. The new remote sensing data acquisition system uses fully parallel mode and FPGA to control sampling time and sampling interval. Simulation results and test results show that the remote sensing data acquisition system is multichannel and controllable.
Accelerating Deep Action Recognition Networks for Real-Time Applications

In this work, the authors propose several techniques for accelerating a modern action recognition pipeline. This article reviewed several recent and popular action recognition works and selected two of them as part of the tools used for improving the aforementioned acceleration. Specifically, temporal segment networks (TSN), a convolutional neural network (CNN) framework that makes use of a small number of video frames for obtaining robust predictions which have allowed to win the first place in the 2016 ActivityNet challenge, and MotionNet, a convolutional-transposed CNN that is capable of inferring optical flow RGB frames. Together with the last proposal, this article integrated a new software for decoding videos that takes advantage of NVIDIA GPUs. This article shows a proof of concept for this approach by training the RGB stream of the TSN network in videos loaded with NVIDIA Video Loader (NVVL) of a subset of daily actions from the University of Central Florida 101 dataset.
KLRfome - Kernel Logistic Regression on Focal Mean Embeddings

KLRfome is a package for predicting a geographic area's relative sensitivity for the presence of archaeological sites.This is achieved by fitting, predicting, and visualizing a Kernel Logistic Regression model on mean feature embeddings.This regression algorithm and package are created to improve upon the current methods in archaeological predictive modeling.These improvements include 1) modeling rich descriptions of archaeological land forms by mitigating undesirable spatial correlation between samples, 2) explicitly modeling the similarity between archaeological sites as characterized by rich features, 3) the ability to define research specific similarity measures, and 4) focal window prediction that can be modified based on theory or management goals.
Foggy image–sharpening method with multi-channel polarization information system

This article aims to reduce the influence of heavy fog on the outdoor imaging equipment and to maximally improve the foggy image resolution. It is known that reflected light of the object includes the abundant target polarization information. The foggy image can be restored by these information of the target and the airlight. Therefore, this article introduces a multi-channel polarization information system and defogging algorithm accordingly. The polarization information system provides a necessary solution for the accurate application of a specific algorithm, since it can ensure the accuracy of acquired image information. The key point of the proposed algorithm lies in accurately estimating the parameters in the polarization defogging model. Based on the normal threshold distribution comparison, it can avoid the highlight portion of the non-sky area in the image and accurately estimate the airlight intensity at infinite distance so as to effectively reduce the color distortion of the bright area. The median filtering algorithm is used to obtain the airlight degree of polarization by using three obtained polarization scenes. At last, this article analyzes the experimental results through defogging evaluation indexes and compared the result obtained by this algorithm with others.
Governance by Emerging Technologies—The Case for Sand and Blockchain Technology

Emerging technologies can also be used for governanceGovernance . This is the case for sand. Sand is a key ingredient for many industries, including concrete, glass, and electronics. In this chapter, sand governanceSand governance framework is suggested through the applying blockchain technologyBlockchain technology with the aim of regulating sand extraction and trade. In this case, blockchain is the technology that can be used for distributed concurrency monitoring. Agent-Based Modeling and Simulation (ABMS) is applied to demonstrate the application of the model.