Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of automatic map based on saliency detection

As one of the key technologies in film special effects production, virtual reality, augmented reality and digital image editing, image matting has been widely applied. In computer vision, art design and other aspects have played a very important role. In view of the limitations of traditional matting technology on background and the need for a large number of human interactions, this paper adopts an automatic matting algorithm based on saliency detection to solve this problem. Experimental results show that the improved RC algorithm can automatically mat out the images, and has a good effect and high efficiency in detail processing.
Significant improvement of cloud representation in the global climate model MRI-ESM2

Abstract. The development of the climate model MRI-ESM2 (Meteorological Research Institute Earth System Model version 2), which is planned for use in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) simulations, involved significant improvements to the representation of clouds from the previous version MRI-CGCM3 (Meteorological Research Institute Coupled Global Climate Model version 3), which was used in the CMIP5 simulations. In particular, the serious lack of reflection of solar radiation over the Southern Ocean in MRI-CGCM3 was drastically improved in MRI-ESM2. The score of the spatial pattern of radiative fluxes at the top of the atmosphere for MRI-ESM2 is better than for any CMIP5 model. In this paper, we set out comprehensively the various modifications related to clouds that contribute to the improved cloud representation and the main impacts on the climate of each modification. The modifications cover various schemes and processes including the cloud scheme, turbulence scheme, cloud microphysics processes, interaction between cloud and convection schemes, resolution issues, cloud radiation processes, interaction with the aerosol model, and numerics. In addition, the new stratocumulus parameterization, which contributes considerably to increased low-cloud cover and reduced radiation bias over the Southern Ocean, and the improved cloud ice fall scheme, which alleviates the time-step dependency of cloud ice content, are described in detail.
Применение сверточных нейронных сетей для решения задачи противодействия атаке спуфинга в системах лицевой биометрии

Subject of Research. We propose the method for detecting an incident at face authentication when imposter tries to disguise himself as a real client. He tries to falsify the client's face by photo or video. Method. The face anti-spoofing method involves two successive steps. Obtaining facial features takes place at the first stage. Classification is performed at the second stage for making a decision if the real person or an imposter is in front of the camera. Facial features are extracted with the use of deep convolutional neural network. Classification is realized by support vector machine. One frame or a group of С. С. Волкова, Ю. Н. Матвеев Научно-технический вестник информационных технологий, механики и оптики, 2017, том 17, No 4 703 frames can become the input data for the method. Main Results. The proposed method for anti-spoofing gives the possibility to work with both real faces obtained with low quality and with fake faces displayed on high-resolution displays. It is confirmed by experiments on two available test datasets. Experiments show that the average value of the first and second kind errors on the test data does not exceed 9%, and the accuracy reaches values over 91%. Classification results are comparable with the best results shown when applying the other known techniques for detecting spoofing attacks on the same test bases. Practical Relevance. The proposed method can be applied to improve the quality of face authentication systems, as well as for the development of multimodal biometric systems.
No-reference image quality assessment based on AdaBoost BP neural network in wavelet domain

Considering the relatively poor robustness of quality scores for different types of distortion and the lack of mechanism for determining distortion types, a no-reference image quality assessment (NR-IQA) method based on the AdaBoost BP Neural Network in Wavelet domain (WABNN) is proposed. A 36-dimensional image feature vector is constructed by extracting natural scene statistics (NSS) features and local information entropy features of the distorted image wavelet sub-band coefficients in three scales. The ABNN classifier is obtained by learning the relationship between image features and distortion types. The ABNN scorer is obtained by learning the relationship between image features and image quality scores. A series of contrast experiments are carried out in the LIVE database and TID2013 database. Experimental results show the high accuracy of the distinguishing distortion type, the high consistency with subjective scores and the high robustness of the method for distorted images. Experiment results also show the independence for the database and the relatively high operation efficiency of this method.
Flash-Based Storage Deduplication Techniques: A Survey

Exponential growth of the amount of data stored worldwide together with high level of data redundancy motivates the active development of data deduplication techniques. The overall increasing popularity of solid-state drives (SSDs) as primary storage devices forces the adaptation of deduplication techniques to technical peculiarities of this type of storage (such as write amplification and wearout), implying active research in SSD-equipped storage data deduplication subdomain. In this survey paper the authors summarize the recent results on deduplication in SSD-enhanced storage, providing a novel taxonomy of the techniques. They classify the techniques on the basis of storage device complexity, starting from a sub-device level up to the storage network. Linux deduplication implementations are discussed, and the results of experimental comparison of several widely used tools are presented. Finally, the authors briefly outline open problems in the field and possible points of future research.
Mutual Information Based Feature Selection for Fingerprint Identification

In the field of fingerprint identification, local histograms coding is one of the most popular techniques used for fingerprint representation, due to its simplicity. This technique is based on the concatenation of the local histograms resulting in a high dimension histogram, which causes two problems. First, long computing time and big memory capacities are required with databases growing. Second, the recognition rate may be degraded due to the curse of dimensionality phenomenon. In order to resolve these problems, we propose to reduce the dimensionality of histograms by choosing only the pertinent bins from them using a feature selection approach based on the mutual information computation. For fingerprint features extraction we use four descriptors: Local Binary Patterns (LBP), Histogram of Gradients (HoG), Local Phase Quantization (LPQ) and Binarized Statistical Image Features (BSIF). As mutual information based selection methods, we use four strategies: Maximization of Mutual Information (MIFS), minimum Redundancy and Maximal Relevance (mRMR), Conditional Info max Feature Extraction (CIFE) and Joint Mutual Information (JMI). We compare results in terms of recognition rates and number of selected features for the investigated descriptors and selection strategies. Our results are conducted on the four FVC 2002 datasets which present different image qualities. We show that the combination of mRMR or CIFE feature selection methods with HoG features gives the best results. We also show that the selection of useful fingerprint features can surely improve the recognition rate and reduce the complexity of the system in terms of computation cost. The feature selection algorithms may reach 98% of time reduction by considering only 20% of the total number of features while also improving the recognition rate of about 2% by avoiding the curse of dimensionality phenomena.
Sieve maximum likelihood estimation in generalized linear models with an unknown link function

Standard generalized linear models (GLMs) consist of three components: random component referring to a distribution of the response variable that belongs to the exponential family; systematic component referring to the linear predictor; and known link function specifying the relationship between the linear predictor and the mean of the distribution function. A flexible extension of the standard GLMs allows an unknown link function. Classical parametric likelihood approach is not applicable due to a large parameter space. To address this issue, sieve maximum likelihood estimation has been developed in literature in which the estimator of the unknown link function is assumed to lie in a sieve space. Various methods of sieves including the B‐spline and P‐spline based methods are introduced. The numerical implementation and theoretical properties of these methods are also discussed. WIREs Comput Stat 2018, 10:e1425. doi: 10.1002/wics.1425
The Effect of School Consolidation on Student Achievement

Many countries have seen a substantial increase in the average school size over the past decades, and a corresponding reduction in the number of schools. It has been widely argued that both students and local communities have suffered from these consolidations. Despite their vast extent and controversy, the literature is scarce with evidence of how consolidation affects performance of exposed students. This study explores how consolidation of 76 rural lower secondary schools in Norway affects students' educational performance, using Rich register data. I find no indication that school consolidation reduces educational achievement. nonetheless, the analysis suggests a negative association between school closure and grade point average, but this effect appears to be driven by lenient grading in small schools.
Machine Learning Methods for Seismic Hazards Forecast

In this paper, we suggest two machine learning methods for seismic hazard forecast. The first method is used for spatial forecasting of maximum possible earthquake magnitudes ( M m a x ), whereas the second is used for spatio-temporal forecasting of strong earthquakes. The first method, the method of approximation of interval expert estimates, is based on a regression approach in which values of M m a x at the points of the training sample are estimated by experts. The method allows one to formalize the knowledge of experts, to find the dependence of M m a x on the properties of the geological environment, and to construct a map of the spatial forecast. The second method, the method of minimum area of alarm, uses retrospective data to identify the alarm area in which the epicenters of strong (target) earthquakes are expected at a certain time interval. This method is the basis of an automatic web-based platform that systematically forecasts target earthquakes. The results of testing the approach to earthquake prediction in the Mediterranean and Californian regions are presented. For the tests, well known parameters of earthquake catalogs were used. The method showed a satisfactory forecast quality.
Multi Channels Data Fusion Algorithm on Quantum Genetic Algorithm for Sealed Relays

Particle Impact Noise Detection (PIND) is a screening test that must be done before the sealed relay is released. PIND signals are mostly occasional weak, which are difficult to measure and identify. In this paper, the three channel sensor is used to detect the remainder signals, and the weak signal is enhanced by weighted data fusion. For the first time, quantum genetic algorithm (QGA) is used to self-adaptively configure the weight, taking the place of arti-ficial settings. Experiments show that after data fusion, and the variance is only about 20% of the single input signal, which verifies the effectiveness of the algorithm. The computational complexity of QGA is offered.
Investigation of Various Techniques of Spectrum Sensing for Power Detection in Cognitive Radio

The most competitiveness among the remote clients in India, transfer speed turns into the noticeable factor. Every one of the clients search for financially savvy and frightfully proficient frameworks to utilize the data transfer capacity viably for the essential and the auxiliary client. In this association, the current research on various MAC layers plainly uncovers that MC-CDMA procedure is giving a promising information throughput and better ghastly productivity. In the current present day advancement the medium range correspondence frameworks like dispatch administrations, roadway administrations, inn segment administrations is winding up exceptionally prevalent and every one of these applications needs an organized range detecting, range openings, range sharing, range administration, and range planning. Every one of these variables is kept up by intellectual radio approach. In this paper we examine about different procedures of range detecting in intellectual radios and its energy phantom thickness is assessed.
A Nature Inspired Algorithm for location and Performance analysis of UPFC

The paper presents a PSO based approach for optimal location of UPFC in transmission system. Two objectives reduction of power loss and improving the bus voltages are considered in this paper. In this regard the power flow injection model of UPFC is derived and injected into load flow studies. In this work the effect of placement of UPFC at optimal location on the system parameter is studied. The effectiveness of the method is tested on IEEE 30 bus system. The results obtained shoe that the proposed method is capable of finding the optimal location and improving the system stability.
Beacon based structures in polygonal domains

We consider beacon-based point-to-point routing and coverage problems motivated by sensor-network applications. A beacon b is a point that can be activated to eﬀect a ‘gravitational pull’ toward itself everywhere in a polygonal domain P . We demonstrate polynomial-time algorithms to compute the attraction region of a beacon, the inverse attraction region of a point and a connected polygonal region, and the beacon kernel of a polygon. We establish a polynomial-time algorithm for routing from a point s to a point t using a discrete set of candidate beacons, as well as a 2-approximation and a PTAS for routing between unrestricted beacons placed in P .
Sexbots: a case for artificial ethical agents

ABSTRACT Recent years have seen significant developments in Artificial Intelligence and we are now beginning to see some applications applied to sexual technologies. This has led to certain individuals and groups calling for a ban on the development of artificial sexual companions or “sexbots”. This is largely due to fears that commercialising sex in this way could could re-enforce existing gender inequalities, and sexual objectification. We argue that one possible risk mitigation strategy could be to develop ethical safeguards into sexual robots. In this paper we explore the problem of implementing artificial ethical agents through a review of the relevant literature. We begin by exploring the motivation to the problem, and the science fiction that has inspired it. Following this, the practical, and philosophical implementation challenges are discussed before providing an overview of the current state of the art. The paper concludes by introducing the grand challenges of the field.
Steady-state assessment

An important part of power quality is steady-state (and quasi-steady-state) waveform distortion. The resulting information is sometimes presented in the time-domain (e.g. notching) and more often in the frequency-domain (e.g. harmonics and interharmonics). Randomly varying non-linear loads, such as arc furnaces, as well as substantial and varying harmonic (and interharmonic) content, cause voltage fluctuations that often produce flicker. The random nature of the load impedance variation with time prevents an accurate prediction of the phenomena. However, the EMTP method can still help in the selection of compensating techniques, with arc models based on the experience of existing installations. Another application of the EMTP method for steady-state assessment is its use in developing accurate harmonically coupled models for other modelling frame-works, such as the harmonic domain. This is desirable as frequency-domain techniques are more amendable for simulating very large power systems.
Catalysts for Nitrogen Oxides Removal from Flue Gases

Main catalytic systems for nitrogen oxides removal from flue gases of thermal power plants by the method of selective catalytic reduction (SCR) with ammonia are reviewed. Mechanisms of the process and catalysts preparation methods are examined; analysis of commercial catalytic systems is performed. Novel advanced catalysts with improved functional properties and higher resistance to water vapor and SO2 are examined.
Data Analysis and Visualization of Continental Cancer Situation by Twitter Scraping

With the advent of user-generated content, usability, and interoperability of web platforms, people are today more eager to express and share their opinions on the web regarding both daily activities and global issues. Cancer is often undetected, leading to serious issues which continue to affect a person's life and his surroundings. Recently Twitter has been very popular to be used to predict and monitor real-world outcomes as well as health-related concerns. Nowadays people are using social media in any situation. Even cancer patients, their friends, and family are increasingly sharing their experience in social media, which has increased the ability of patients to find others similar to their conditions to discuss treatment options, suggest lifestyle changes, and to offer support. Our work targets to link patients with a particular illness (cancer) together and to provide researchers with enriched patient data that might be very useful for future analysis of this disease. We wanted to create a meeting point for the healthcare sector and social media through our work. Our target was to collect Twitter data from different continents of the world and analyze them. We scraped tweets from over the last two years from all around the world. Then clean the data using a regular expression and then process it to prepare our own dataset. We used sentiment analysis and natural language processing to classify them into positive, negative and neutral tweets to determine which of the tweet means to have cancer and which don't. We then analyzed the prepared dataset and visualized and compared them with veritable cancer-related information to ascertain if people's tweets are allied with actual cancer situation.
A Novel Survey Paper on Big Data Analytics

An enormous repository of terabytes of {information} is generated daily from trendy information systems and digital technolo- gies such as net of Things and cloud computing. Analysis of those large information needs lots of efforts at multiple levels to extract information for call creating. Therefore, huge information analysis may be a current space of analysis and development. the fundamental objective of this paper is to explore the potential impact of massive information challenges, open analysis problems, and varied tools related to it. As a result, this article provides a platform to explore huge information at varied stages. to boot, it opens a replacement horizon for researchers to develop the answer, based mostly on the challenges and open analysis problems.
A hybrid Technique for Cleaning Missing and Misspelling Arabic Data in Data Warehouse

— Real-World datasets accumulated over a number of years tend to be incomplete, inconsistent and contain noisy data, this, in turn, will cause an inconsistency of data warehouses. Data owners are having hundred-millions to billions of records written in different languages, hence continuously increases the need for comprehensive, efficient techniques to maintain data consistency and increase its quality. It is known that the data cleaning is a very complex and difficult task, especially for the data written in Arabic as a complex language, where various types of unclean data can occur to the contents. For example, missing values, dummy values, redundant, inconsistent values, misspelling, and noisy data. The ultimate goal of this paper is to improve the data quality by cleaning the contents of Arabic datasets from various types of errors, to produce data for better analysis and highly accurate results. This, in turn, leads to discover correct patterns of knowledge and get an accurate Decision-Making. This approach established based on the merging of different algorithms. It ensures that reliable methods are used for data cleansing. This approach cleans the Arabic datasets based on the multi-level cleaning using Arabic Misspelling Detection, Correction Model (AMDCM), and Decision Tree Induction (DTI). This approach can solve the problems of Arabic language misspelling, cryptic values, dummy values, and unification of naming styles. A sample of data before and after cleaning errors presented.
Closing the Loop: The Capacities and Constraints of Information and Communication Technologies for Development (ICT4D)

As a mechanism for collecting and sharing information, information and communications technologies (ICT) hold immense potential for individuals and institutions in lowand middle-income countries. Currently the distribution and adoption of ICTs—particularly mobile devices—has far outpaced the provision of other household services like clean water, sanitation, hygiene, or energy services. At the same time, the development and deployment of Internet of Things (IoT) devices including cellularand satellite-connected sensors is facilitating more rapid feedback from remote regions where basic services are most limited. When used in conjunction with economic development or public health interventions, these devices and the feedback they provide can inform operation and maintenance activities for field staff and improve the monitoring and evaluation of outcomes for project stakeholders. This dissertation includes three chapters written as journal articles. While each chapter is framed around the work and research efforts being undertaken by the Sustainable Water, Energy, and Environmental Technologies Lab (SweetLab) at Portland State University, the common thread that weaves all three investigations together is the theme of ICT-enabled programmatic feedback. The first chapter introduces the three theoretical lenses that inform these investigations and the ways that ICTs and the data they provide can (1) serve as more appropriate proxies for measuring access to services, (2) reduce information asymmetries between various stakeholders including communities, governments, implementers, and funders, and (3) enable more robust methodologies for measuring outcomes and impacts of interventions within complex adaptive systems. The second chapter presents a critical review of the methodologies and technologies being used to track progress on sanitation and hygiene development goals. Chapter three describes how simple sensors and weight measurements can be combined with complex machine learning algorithms to facilitate more reliable and