Reviewerly/exHarmony huggingface dataset:
A Robust Method for Speech Replay Attack Detection

Spoofing attacks, especially replay attacks, pose great security challenges to automatic speaker verification (ASV) systems.Current works on replay attacks detection primarily focused on either developing new features or improving classifier performance, ignoring the effects of feature variability, e.g., the channel variability.In this paper, we first establish a mathematical model for replay speech and introduce a method for eliminating the negative interference of the channel.Then a novel feature is proposed to detect the replay attacks.To further boost the detection performance, four post-processing methods using normalization techniques are investigated.We evaluate our proposed method on the ASVspoof 2017 dataset.The experimental results show that our approach outperforms the competing methods in terms of detection accuracy.More interestingly, we find that the proposed normalization strategy could also improve the performance of the existing algorithms.
Calculation of Client Similarities in Large-Scale on Social Network Using Recommendation Framework

Analysis of OSN to find out how people are being connected with each other into a network and existing methods can’t evaluate efficiently internal connectivity of a friendship graph and fail to provide proper recommendation. In this paper, we propose an efficient user similarity measurement between two users using profile and network similarity. With a view to estimating the user similarity we compute an average weight which denotes the possibility of two persons being alike. After calculating profile similarity, we also compute network similarity using friendship edge and mutual friends edge and later to determine user similarity, we apply conditional probability by considering profile similarity and network similarity as evidence. To handle missing valued of any profile attributes we provide a technique that assume the valued based on friends profile attribute. Finally, we conduct a feature analysis on our proposed scheme in a large network to prove its batter outcome.
Do We Need Word Order Information for Cross-lingual Sequence Labeling.

Word order variances generally exist in different languages. In this paper, we hypothesize that cross-lingual models that fit into the word order of the source language might fail to handle target languages. To verify this hypothesis, we investigate whether making models insensitive to the word order of the source language can improve the adaptation performance in target languages. To do so, we reduce the source language word order information fitted to sequence encoders and observe the performance changes. In addition, based on this hypothesis, we propose a new method for fine-tuning multilingual BERT in downstream cross-lingual sequence labeling tasks. Experimental results on dialogue natural language understanding, part-of-speech tagging, and named entity recognition tasks show that reducing word order information fitted to the model can achieve better zero-shot cross-lingual performance. Furthermore, our proposed methods can also be applied to strong cross-lingual baselines, and improve their performances.
Samara Polytech Innovation: Digital Campus 2.0

The paper considers an innovative solution of University infrastructure digitalization based on the implementation of an original stack of information technologies. The proposed solution includes students, teachers, and university employees' identification, adaptive acquiring in multiple sales points, digital library and smartphones mobile application. Student identification is connected with banking card that improves an opportunity of the interaction in virtual educational space. Some services are provided for parents to monitor and control the students' financial operations and academic progress. Special intelligent module is introduced for coordination and decision-making support. Research conclusions and recommendations are illustrated by the implementation results in Samara State Technical University.
Remote Sensing Image Mixed Noise Denoising with Noise Parameter Estimation

Images acquired by optical remote sensing often suffer from noise, due to low-light conditions and long distances from the object. Noise introduces undesirable effects such as artifacts, unrealistic edges, unseen lines and corners, blurred objects and disturbed background scenes, and decreasing the performance of visual and computational analysis. The great majority of denoising methods only focus on single noise type, and mostly assume additive Gaussian white noise. However, in practice, according to the different sources of noise during the capturing process, the real image noise may be composed of noises from several different distributions, i.e., Poisson distribution from shot noise and dark current, Gaussian distribution from readout noise, etc. Thus, it is hard to model complex noise with a single noise model, which therefore limits the practical performance of image denoiser for real captured images. To address this problem, we propose a deep residual network with parameter estimation to model and remove mixed noises of remote sensing data, with no noise type and noise level information. In this chapter, a deep convolutional neural network based framework is proposed to implement both noise estimation and noise removing. The framework is composed of noise estimator module and denoiser module. The noise estimator module is used to estimate the noise distribution characteristics of the input image, which are used to synthesize the noisy dataset for the training of the denoiser module. After training, the denoiser module can then denoise the input image specifically for that noise characteristic of the input image. Besides, we use some advanced designment to improve the performance of the network like residual learning, skip connections, and perceptual loss. Our model works for both given noise types and others without taking into account in training process, and for the latter case we test the model on the noise mixed with different types, i.e., shot noise, Gaussian noise, salt and pepper noise, speckle noise, dark current, and quantization noise, covering most of the common noise types in practical situation. The estimated noise distribution according to the proposed estimator is compared with the ground truth to validate the performance of the estimator. We also compare the proposed method with the existing denoising methods, including both the traditional algorithm (i.e., BM3D (Dabov et al., IEEE Trans Image Process 16:2080–2095, 2007)) and the deep neural network (i.e., DnCNN (Zhang et al., IEEE Trans Image Process 26:3142–3155, 2017)). Experimental results showed that our method outperforms these state-of-the-art methods in both PSNR (peak signal to noise ratio) and SSIM (structural similarity index).
Aplikasi Data Mining untuk Clustering Daerah Penyebaran Penyakit Diare di DKI Jakarta Menggunakan Algoritma K-MEANS

Diarrhea is a disease that makes sufferers defecate more than 4 times, diarrhea usually strikes all people, including toddlers, teens and adults. The increasing population density in DKI Jakarta Province in 2017 around 10,177,924 inhabitants spread over 6 cities consisting of 44 districts and 267 villages. To be able to see the area of diarrhea distribution, it is necessary to make a grouping based on the attributes used consisting of JSP, JSSP, SHARING and OD in order to obtain the center of the diarrhea spread point. K-Means algorithm is very suitable in clustering the spread of diarrhea Kelurahan. To determine the centroid starting point based on diarrhea sufferers as a parameter in determining C1, C2 and C3 which get C1 is Duren Tiga Village, C2 is Kebon Baru Village and C3 is Bendungan Hilir Village. The results of grouping based on the attributes used get C1 totaling 11 Kelurahans, C2 totaling 34 Kelurahans and C3 totaling 43 Kelurahans from 88 Kelurahan data used in which C1 is categorized as a diarrhea prone point, C2 is categorized as a possible spread of diarrhea and C3 is categorized as a Kelurahan that is safe from spread of diarrhea.
Reference interval establishment of full blood count extended research parameters in the multi-ethnic population of Malaysia.

Haematological cellular structures may be elucidated using automated full blood count (FBC) analysers such as Unicel DxH 800 via cell population data (CPD) analysis. The CPD values are generated by calculating volume, conductivity, and five types of scatter angles of individual cells which would form clusters or populations. This study considered 126 CPD parameter values of 1077 healthy Malaysian adults to develop reference intervals for each CPD parameter. The utility of the CPD reference interval established may range from understanding the normal haematological cellular structures to analysis of distinct cellular features related to the development of haematological disorders and malignancies.
Semantic Enrichment of Web Services using Linked Open Data

With the rise of the internet, a lot of web services (WSs) are offered. However, most of these WSs lack explicit and sufficient semantic information. As a result, relevant WSs are not delivered to consumers during service discovery and WS composition and selection are so challenging. A possible solution to this is to provide the existing syntactic WSs with explicit information of their meaning through an annotation. Semantic annotations play an important role in semantics-aware service discovery, recommendation and composition. In this paper, we first conduct a literature review and then we present an approach for semantically annotating WSs based on linked open data (LOD) knowledge. The proposal uses similarity measure algorithms for matching the appropriate LOD concepts to the corresponding WS' parameters. A performance evaluation on real-world WSs was performed to validate the proposed approach.
Monte Carlo Tree Search for 3D/2D Registration of Vessel Graphs

3D/2D registration techniques can compensate for the deficiencies of X-ray angiography-based navigation in vascular interventional surgery, such as the lack of depth information and excessive use of contrast agents. In this study, we propose a novel Monte Carlo tree search-based 3D/2D vessel graph registration method. The registration problem is transferred to a tree search problem according to the topology of vessel centerlines. Then, the Monte Carlo tree search method is applied to find the optimal vessel matching associated with highest registration score. Experiments on uninitialized vessel data demonstrate that the proposed method can achieve the highest accuracy among four state-of-the-art methods. An average accuracy of 1.91 mm on clinical coronary artery data is obtained. For the independence of initial pose and robustness to noise, the proposed method can align 3D and 2D vessels without prior initialization in vascular interventional surgery.
Model for Mobile Application Development on Traveling Guide: A General Proposal

The increase in tourist visits has an impact on providing information on tourist destinations and all related to tourism trips. The information is still inadequate, especially about the purpose of tourist attractions and accommodation or infrastructure for tourist travel. The lack of information will impact the decline in tourist visits to the province of South Sumatra. The efforts that have been made by the government of South Sumatra Province does not maximize. In this research, we made a proposed model based on a mobile application in the travel guide in the Government of South Sumatra. This model will help the Government in making policies in digital tourism. We use mobile smartphone media that are currently used by more than 90% of the people in searching for information related to tourism. Our proposed approach utilizes the stages in mobile development, in general, using the Mobile-D development method, the overall architectural model, and configuration. The result is the application of the model to the use. The implementation was carried out to see how important the model could be developed and implemented in South Sumatra.
The Case for Custom TMY’s: Examples Using the NSRDB

A typical meteorological year (TMY) data set essentially represents an hourly compilation of median months constructed using multi-year datasets. Although TMY data sets are generated from irradiance data in the horizontal plane, they are used in photovoltaic (PV) modeling for systems inclined to various angles. This paper demonstrates that a POA TMY (plane of array TMY) generated by selecting median months from a multi-year POA irradiance timeseries dataset produces significantly different results than a POA TMY generated by transposing a TMY dataset constructed from horizontal data. In some months the differences can be more than 3%. These results point to the need for generating TMY's using POA irradiance timeseries representing the orientation at which PV panels will be deployed.
Distribution Distillation Loss: Generic Approach for Improving Face Recognition from Hard Samples

Large facial variations are the main challenge in face recognition. To this end, previous variation-specific methods make full use of task-related prior to design special network losses, which are typically not general among different tasks and scenarios. In contrast, the existing generic methods focus on improving the feature discriminability to minimize the intra-class distance while maximizing the interclass distance, which perform well on easy samples but fail on hard samples. To improve the performance on those hard samples for general tasks, we propose a novel Distribution Distillation Loss to narrow the performance gap between easy and hard samples, which is a simple, effective and generic for various types of facial variations. Specifically, we first adopt state-of-the-art classifiers such as ArcFace to construct two similarity distributions: teacher distribution from easy samples and student distribution from hard samples. Then, we propose a novel distribution-driven loss to constrain the student distribution to approximate the teacher distribution, which thus leads to smaller overlap between the positive and negative pairs in the student distribution. We have conducted extensive experiments on both generic large-scale face benchmarks and benchmarks with diverse variations on race, resolution and pose. The quantitative results demonstrate the superiority of our method over strong baselines, e.g., Arcface and Cosface.
The Dark Data Quandary.

The digital universe remains a black box. Despite attaining high-technology capabilities like artificial intelligence and cognitive computing, "Big Data" analytics have failed to keep pace with surging data production. At the same time, the falling costs of cloud storage and distributed systems have made mass data storage cheaper and more accessible. These effects have produced a chasm between data that is stored and data that can be readily analyzed and understood. Enticed by the promise of extracting future value from rising data stockpiles, organizations now retain massive quantities of data that they cannot presently know or effectively manage. This rising sea of "dark data" now represents the vast majority of the digital universe. Dark data presents a quandary for organizations and the judicial system. For organizations, the inability to know the contents of retained dark data produces invisible risk under a spreading patchwork of digital privacy and data governance laws, most notably in the medical and consumer protection areas. For courts increasingly confronted with Big Data-derived evidence, dark data may shield critical information from judicial view while embedding subjective influences within seemingly objective methods. To avoid obscuring organizational risk and producing erroneous outcomes in the courtroom, decision-makers must achieve a new awareness of dark data’s presence and its ability to undermine Big Data’s vaunted advantages.
Contribution to a Smart Home Design for Medical Surveillance

Patients or elderly individuals’ surveillance is no more restricted to hospitals thanks to the advanced development of new technologies, where we have the capability to supervise their conditions remotely throughout medical surveillance systems that are based on the Internet of Things (IoT), multiagent systems (MAS) and artificial intelligence (AI). These computing paradigms can be brought together to establish an intelligent system that would focus on patient comfort and ultimately reduce costs, speed up controlling operations and finally enhance the diagnosis accuracy and precision. Hence, a smart home conception following specific architectures and using the appropriate technologies of medical surveillance would form an essential aspect for the smart healthcare paradigm. Accordingly, an optimal design and architecture for an intelligent home and an effective intelligent healthcare would consider a resilient balance of IoT, MAS and an adequate machine-learning model for human behavior analysis. In this paper, a new smart home design for medical surveillance is proposed in order to contribute to the expansion of the number of smart healthcare users and providers on a winner-winner relationship that promotes individuals comfort and prosperity and drives-up the Information Technology contributions and business.
FastGAE: Fast, Scalable and Effective Graph Autoencoders with Stochastic Subgraph Decoding.

Graph autoencoders (AE) and variational autoencoders (VAE) are powerful node embedding methods, but suffer from scalability issues. In this paper, we introduce FastGAE, a general framework to scale graph AE and VAE to large graphs with millions of nodes and edges. Our strategy, based on node sampling and subgraph decoding, significantly speeds up the training of graph AE and VAE while preserving or even improving performances. We demonstrate the effectiveness of FastGAE on numerous real-world graphs, outperforming the few existing approaches to scale graph AE and VAE by a wide margin.
Phase factors of periodically driven two-level systems

Using a perturbative solution for a periodically driven two-level quantum system, we show how to obtain phase factors for both a two-level quantum system and two two-level quantum systems non-interacting and interacting. The method is easily implemented by numerical routines and presents the advantage of being stable for long-time periods. We furthermore explore the possibility of implementing a quantum phase gate using the perturbative solution.
ϵ <scp>KTELO</scp>

The adoption of differential privacy is growing, but the complexity of designing private, efficient, and accurate algorithms is still high. We propose a novel programming framework and system, ϵ KTELO for implementing both existing and new privacy algorithms. For the task of answering linear counting queries, we show that nearly all existing algorithms can be composed from operators, each conforming to one of a small number of operator classes. While past programming frameworks have helped to ensure the privacy of programs, the novelty of our framework is its significant support for authoring accurate and efficient (as well as private) programs. After describing the design and architecture of the ϵ KTELO system, we show that ϵ KTELO is expressive, allows for safer implementations through code reuse, and allows both privacy novices and experts to easily design algorithms. We provide a number of novel implementation techniques to support the generality and scalability of ϵ KTELO operators. These include methods to automatically compute lossless reductions of the data representation, implicit matrices that avoid materialized state but still support computations, and iterative inference implementations that generalize techniques from the privacy literature. We demonstrate the utility of ϵ KTELO by designing several new state-of-the-art algorithms, most of which result from simple re-combinations of operators defined in the framework. We study the accuracy and scalability of ϵ KTELO plans in a thorough empirical evaluation.
User's Height Estimation based on Acceleration from Smartphone Sensors

The recent advance of machine learning enabled a variety of estimation data from sensors. In this study, we investigate estimation of the heights of the users of smartphones from the sensed data for the next step of the studies of estimation based on the machine learning. We propose a method for estimating the two-classed height, which is tall or short, by the linear regression. Our evaluation shows that the proposed method estimated the height class with 92% accuracy in case of persons whose heights are far from the median height.
RescueMark: Visual Analytics of Social Media Data for Guiding Emergency Response in Disaster Situations: Award for Skillful Integration of Language Model

This paper presents RescueMark, a web-based visual analytics tool for analyzing disaster situations and guiding emergency response. In disaster situations operators must take quick and effective decisions to solve critical problems. RescueMark provides spatial, topic and temporal event exploration supporting decision making for resource allocation and determine damaged areas of the city. We describe the data analysis and visualization process of the social media data applied to extract the relevant information.
PMIQD 2019: A Pathological Microscopic Image Quality Database with Nonexpert and Expert Scores

In medical diagnostic analysis, pathological microscopic image is often regarded as a gold standard, and hence the study of pathological microscopic image is of great necessity. High quality microscopic pathological images enable doctors to arrive at correct diagnosis. The pathological microscopic image is an important cornerstone for modernization and computerization of medical procedures. The quality of pathological microscopic images may be degraded due to a variety of reasons. It is difficult to acquire key information, so research for quality assessment of pathological microscopic image is quite necessary. In this paper, we perform a study on subjective quality assessment of pathological microscopic images and investigate whether the existing objective quality measures can be applied to the pathological microscopic images. Concretely, we establish a new pathological microscopic image quality database (PMIQD) which includes 425 pathological microscopic images with different quality degrees. The mean opinion scores rated by nonexperts and experts are calculated afterwards. Besides, we investigate the prediction performance of the existing popular image quality assessment (IQA) algorithms on PMIQD, including 8 no-reference (NR) methods. Experimental results demonstrate that the present objective models do not work well. IQA for pathological microscopic image needs to be developed for predicting the quality rated by nonexperts and experts.