Reviewerly/exHarmony huggingface dataset:
Machine Learning with Generative Adverserial Network

Generative Adversarial Networks have been presented to train generative models as they have pose a certain level of exertion and difficulty in the past years. Comparatively, GAN has received less attention. This model shows that it can generate MNIST (Modified National Institute of Standards and Technology database) digits conditioned on class labels. Translating an image from a certain source image or the original image to a clearer and higher resolution target domain is the approach introduced in this paper. This work presents a non-conventional way to resolve the problem presented in digitizing handwritten digits. This approach is supposed to be easier to implement and understand while having a high efficiency and accuracy as compared to the existing models. It is shown that MNIST digits conditioned on class labels can be generated by this model. Both the generator and discriminator are to be conditioned. The idea is to nurture both the generator and discriminator together and gradually. Beginning from a low-resolution image, more layers are added so that the model can increase the definite finer details and progress towards a clearer image as the training progresses.
Gesture Activated Robotic Glove and Robotic Arm with Automatic Alert System and Automation

The field of Robotics is increasingly advancing in order to make the people's life more efficient. Various machines are designed and manufactured in this particular field to help the physically challenged people. The proposed research work focuses on people, who are physically challenged with various physical illness. In particular, paralysis is the most dramatically widespread disorder. Approximately 1.7 percent of the population has reported that they are living with some form of paralysis. In this perspective, a smart system is developed in this paper to serve as a best companion to a person, who suffer from paralysis on one side of their body or below waist. The primary goal is to design and implement a low cost glove, which will help the needy to operate various electrical appliances and also send emergency voice messages to a helper. The proposed research work comprises a Robotic arm, which is a mechanical framework shaped by various joints and gripper characteristics of the fingers and this can be done by utilizing servo motor, when client do hand movement for any application. Hand gesture recognition is performed by using IR sensors and embedded c programming.
Authorship Verification with Personalized Language Models

Malicious posts from a social media account by an unauthorized user could have severe effects for the account holder, such as the loss of a job or damage to their reputation. In this work, we consider an authorship verification task to detect unauthorized malicious social media posts. We propose a novel approach for authorship verification based on personalized, i.e., user-tailored, language models. We evaluate our proposed approach against a previous approach based on word embeddings and a one-class SVM. A large amount of text might not necessarily be available for an individual social media user. We therefore demonstrate that our proposed approach out-performs previous approaches, while requiring orders of magnitude less user-specific training text.
MIR

To cope with semantic overload of IP addresses and centralized management of DNS, we have proposed a co-governing Multi-Identifier Network with supporting multiple network identifiers in early work. In this paper, we propose Multi-Identifier Router, a core hardware of MIN, which integrates AI filtering technology and supports different identifiers in routing and inter-translation. We deployed the prototype of MIR on the largest operators' network in Mainland China, Hong Kong and Macao, which supports service very well. At last we give the simulation and experiment of MIR's functions.
Dictionary Learning for Visual Tracking with Dimensionality Reduction

Recently, visual tracking has seen much progress in either accuracy or speed. However, due to drastic illumination variation, partial occlusion, scale variation and out-of-plane rotation, visual tracking remains a challenging task. Dealing with complicated appearance variations is an open issue in visual tracking. Existing trackers represent target candidates by a combination of target templates or previous tracking results under some constraints. When a drastic appearance variation occurs or some appearance variations occur simultaneously, such target representations are not robust. In this paper, we present a discriminative dictionary learning based target representation. A target candidate is represented via a linear combination of atoms in a learnt dictionary. The online dictionary learning can learn the appearance variations in tracking processing. So, the learnt dictionary can cover all of kinds of appearance variations. Based on this kind of target representation, a novel tracking algorithm is proposed. Extensive experiments on challenging sequences in popular tracking benchmark demonstrate competing tracking performances against some state-of-the-art trackers.
Summarizer: Fuzzy Rule-Based Classification Systems for Vertical and Horizontal Big Data

The performance of Fuzzy Rule-Based Classification Systems (FRBCSs) is highly affected by the increasing number of instances and attributes present in Big Data. Previously proposed approaches try to adapt FRBCSs to Big Data by distributing data processing with the MapReduce paradigm, by which the data is processed in two stages: Map and Reduce. In the Map stage, the data is divided into multiple blocks and distributed among processing nodes that process each block of data independently. In the Reduce stage, the results coming from every node in the Map stage are aggregated and a final result is returned. This methodology tackles vertical high dimensionality (high number of instances), but it does not approach datasets with simultaneous vertical and horizontal high dimensionality (high number of attributes), as it is the case of text datasets. In this work, we deal with the aforementioned drawbacks by proposing Summarizer, an approach for building reduced feature spaces for horizontally high dimensional data. To this end, we carry out an empirical study that compares a well-known classifier proposed for vertical high dimensionality datasets with and without the horizontal dimensionality reduction process proposed by Summarizer. Our findings show that existing classifiers that tackles vertical Big Data problems can be improved by adding the Summarizer approach to the learning process, which suggests that an unified learning algorithm for datasets with a high number of instances as well as a high number of attributes might be possible.
An Automatic Centroid Image Selection Method Based on Fuzzy Logic Reasoning in Image Deduplication

Centroid selection plays a key role in image deduplication. It means selecting an optimal solution as a centroid image in a duplicate image set. Meanwhile, it will delete other image copies and establish pointers to point to the centroid image in the original position. At present, there is not a mature centroid selection scheme. Centroid selection mainly relies on users to manually complete according to experience. In a massive data environment, it will consume a lot of human resources, and it is easy to make mistakes by subjective judgment. Therefore, in order to solve this problem, this article proposes an automatic centroid image selection method based on fuzzy logic reasoning. In a duplicate image set, the image attribute information is used to automatically infer comprehensive quantized values to represent images, and the centroid image is selected by comparing the quantized values. The experimental results showed that the scheme not only could meet the visual perception characteristics, but also meet the purpose of image deduplication.
Computational Intelligence and Automated Methods for Control Fuzzy System Design

This paper aims to present a complex method of computational intelligence for a control fuzzy system design in a situation when there is no much of prior knowledge. Initial values are obtained by rule-based computations using available body of knowledge. Two fuzzy logic automated methods, batch and recursive least squares, are used to continue the computation and modelling of the system, to build and enhance the knowledge base. The extension principle methods are used in complex environments with both, discretized and continuous functions, with crisp and fuzzy data and transformations. The application of this model is illustrated by solving the autonomous cruise control problem, specifically, the throttle system. This computational method has an advantage comparing to artificial neural networks because the later do modeling of the system based only on learning data without knowing the nature of modelling applications.
Research on the Dilemma of Earthquake Public Opinion Governance and Countermeasures of Scientific Guidance

The correct handling and guidance of the public opinion of the earthquake network in the new situation is related to the social image of the earthquake department and is an important indicator for testing the modernization of social governance capabilities for earthquake prevention and mitigation. On the basis of summarizing and analyzing the experience and lessons of multiple earthquake public opinion cases, this paper systematically summarizes the social governance dilemma faced by the earthquake public opinion, introduces public opinion index for quantitative analysis, puts forward new countermeasures and suggestions to do a good job in seismic network public opinion scientific guidance under the new situation.This is of great significance to further improve the effectiveness of earthquake public opinion guidance and response capabilities.
Review of Outlier Detection Algorithms Based on Grain Storage Temperature Data

Outliers may be objectively existing data generated by mechanisms different from most data, and have related laws and certain research value. Outliers have been widely used in recent years, including financial fraud, cyber fraud, and abnormal weather warnings. Traditional outlier detection algorithms such as statistical-based outlier detection, cluster-based outlier detection, deviation-based outlier detection, distance-based outlier detection, density-based outlier detection, etc., but there are many shortcomings in the traditional outlier detection algorithms. Outlier detection algorithms based on information entropy, outlier detection algorithms based on spectral clustering, etc. have gradually appeared. Because in practice, traditional outlier detection algorithms cannot perform good detection applications on high-dimensional data, spatial data, and time series data, so they are performed for high-dimensional outlier detection, space outlier detection, and time series outlier detection. Dedicated research. How to improve the effectiveness, scalability and space-time efficiency of the algorithm will be the future research direction.
The IBN BATTOUTA Air Traffic Control Corpus with Real Life ADS-B and METAR Data

The Ibn Battouta ATC (Air Traffic Control) communication corpus is English corpus characterized with a strong Moroccan accent speech, under the creation and concentrated on Tangier's airport1. It synchronizes the voice, the ADS-B (flight data broadcast by aircraft) and the METAR (Weather Report on Airport), as trusted structured data frequently pronounced during the communication. The aim of this work is to update the acoustic model and train the ASR (Automatic Speech Recognition) engine like CMU Sphinx or IBM Kaldi for the purpose of improving ATC capabilities, facilitating decision making and ensuring security. Currently we have recorded more than five hours long audio files with silence elimination of real life communication between pilots and controllers during takeoffs, approaches and landings in Tangier airport, Gibraltar airport and in the way from the Moroccan airspace to the Spanish one and vice versa. In addition to 10logging files from ADS-B flight data and METAR reports. All audio files have been fully transcribed manually, with time marking to indicate the start of transmission and its duration, by airlines pilots and controllers working on Tangier airport. To the best knowledge, it's the first corpus that aligns speech data and structured data perfectly to all for a richer ASR modeling. In this paper, we will describe the Tangier's airport geographic specification, and the techniques used to ensure quality of recording, data collecting, and their transcription, using the META data annotation and the advantages of the ATC phraseology as a reducing and controlling language, with synchronized flight data, and weather reports as trusted structured data.
Robust Detection of Vowel Onset and End Points

A novel approach for detecting vowels, vowel onset-points and vowel end-points is presented in this paper. This study is motivated by the fact that some vowels have significant amount of spectral information even in the high frequency region. Furthermore, high-pitched speakers such as adult females and children have relatively more high frequency components than adult males. In order to effectively capture that information, we have exploited linear frequency cepstral coefficients (LFCC) along with Mel-frequency cepstral coefficients (MFCC). The MFCC features are known to down-sample the high frequency components. The LFCC features, on the other hand, provide equal resolution to all frequencies. Therefore, the use of LFCC features helps in effectively resolving high frequency components as well. In order to detect the vowels, two separate vowel non-vowel classification systems, employing deep learning architectures, are developed using MFCC and LFCC features, respectively. Next, for any given test utterance, lattices are generated using the trained acoustic models. The beginning time, duration and confidence scores are then extracted for each occurrence of vowel/non-vowel from the lattices. The weak evidences are discarded by applying a threshold on the confidence scores in order to reduce spurious detection. Finally, the evidences obtained using MFCC and LFCC features are weighted with their respective confidence scores and combined. The proposed approach is observed to outperform the existing ones. Using the detected vowel regions, we have also developed a simple scheme to determine whether the given speech utterance is from an adult or a child speaker. The developed scheme is highly effective in discriminating between adult and child speakers.
Secure Voice Input on Augmented Reality Headsets

Voice-based input is usually used as the primary input method for augmented reality (AR) headsets due to immersive AR experience and good recognition performance. However, recent researches show that attackers can inject inaudible voice commands to the devices that lack voice verification. Even if we secure voice input with voice verification techniques, attackers can record the victim’s voice and replay it. To defend against voice-spoofing attacks, AR headsets should be able to determine whether the voice is from the person who is using the AR headsets. Existing voice-spoofing defense systems are designed for smartphone platforms and usually fail to work due to the special locations of microphones and loudspeakers on AR headsets. To address this challenge, in this paper, we propose a voice-spoofing defense system for AR headsets by leveraging both the internal body propagation and the air propagation of human voices. Experimental results show that our system can successfully accept normal users with average accuracy of 97 percent and defend against two basic types of attacks with average accuracy of at least 98 percent. More importantly, even if the attackers can fool our line-fitting model by manipulating special voice signals, our MCD-SVDD model can still reject them with accuracy of 100 percent.
Cloud Service Brokerage: Exploring Characteristics and Benefits of B2B Cloud Marketplaces

With the increasing popularity of cloud computing, a new technology and business model called cloud service brokerage (CSB) is emerging. CSB is, in essence, a middleman in the cloud-computing supply chain to connect prospective cloud buyers with suitable service providers. This chapter focuses on a type of CSB, B2B cloud marketplaces. Recently, this type of marketplace has evolved into two broad categories—business application marketplaces and API marketplaces. This chapter reviews the characteristics of B2B cloud marketplaces, and their benefits, which include ease-of-use and ease-of-integration, enhanced security, increased manageability, faster implementation, and cost reduction. The chapter concludes with two mini-case studies, on Salesforce AppExchange and RapidAPI, to illustrate how firms could use B2B cloud marketplaces to generate, capture and measure business value.
Biometric Measurement in Software Engineering

Biometric sensor technology provides new opportunities to measure physiological changes in the human body that can be linked to various psychological processes. In software engineering, these biometric measurements can be used to gain insights on fundamental cognitive and emotional processes of software developers while they are working. In addition, biometric measures may be used to provide better and more instantaneous tool support for developers, for instance, by preventing defects from being introduced in the code or supporting focused work. In this chapter, we motivate the use of biometric measurements, introduce common types of biometric sensors and measures, discuss how to choose the right set of them and considerations for analyzing the collected data. We also discuss work in the area of software engineering and recommend further reading.
Industrial Control Robot Based on Augmented Reality and IoT Protocol

The use of robotic systems into production processes has resulted in the reduction of manufacturing costs and an improvement in product quality, However, as technologies advance, complex and high-cost control systems are required, hence the need to create low-cost and efficient control systems, with low complexity when operating them. For these reasons, the research presents a control system for the Scorbot ER 4U manipulator arm using a low-cost embedded board and augmented reality platform that brings to the users a real experience with virtual add-ons, thus facilitating the training and management of robotic equipment. A virtual control proposed in Unity 3D allowing the application to be much more interactive and very easy for the user. For the validation of the system, operating tests were carried out, resulting in movements with great precision and in a very simple way without having greater complexity when indicating positions to which the manipulator must reach.
Application Research of Boyer-Moore Algorithm in Cryptography

The arbitrariness and unpredictability of the sequence generated by the key stream generator determine the strength of the stream cipher, the design of the key stream generator becomes the core problem, the linear feedback shift register is generally used as the driving part of the key stream generator. The number of stages of the shortest linear shift register that produces the sequence is an important indicator of the strength of the stream cipher system, called the linear complexity of the sequence. Based on this, this paper applies the Boyer-Moore algorithm to the cryptography problem, and proves the feasibility of using the Boyer-Moore algorithm to obtain the shortest linear shift register and the linear complexity of the sequence, and proves the uniqueness of the shortest linear shift register obtained by the Boyer-Moore algorithm.
Shape robust Siamese network tracking based on weakly supervised learning

This paper combines the boundary box regression with the training data occlusion processing method, the occlusion problem is more accurate and the tracking accuracy is improved. The occlusion problem is now the major challenge in target tracking. This paper puts forward a weakly monitoring framework to address this problem. The main idea is to randomly hide the most discriminating patches in the input images, forcing the network to focus on other relevant parts. Our method only needs to modify the inputs, no need to hide any patches during the test.
3-D Tissue Image Reconstruction from Digitized Serial Histologic Sections to Visualize Small Tumor Nests in Lung Adenocarcinomas

Pyciński, Bartlomiej Yagi, Yukako Walts, Ann E. Gertych, Arkadiusz3-D histology has become an attractive technique providing insights into morphology of histologic specimens. However, existing techniques in generating 3-D views from a stack of whole slide images are scarce or suffer from poor co-registration performance when displaying diagnostically important areas at sub-cellular resolution. Our team developed a new scale-invariant feature transform (SIFT)-based workflow to co-register histology images and facilitate 3-D visualization of micro-structures important in histopathology of lung adenocarcinoma. The co-registration accuracy and visualization capacity of the workflow were tested by digitally perturbing the staining coloration seven times. The perturbation slightly affected the co-registration but overall the co-registration errors remained very small when compared to those published to date. The workflow yielded accurate visualizations of expert-selected regions permitting confident 3-D evaluation of the clusters. Our workflow could support the evaluation of histologically complex tumors such as lung adenocarcinomas that are currently routinely viewed by pathologists in 2-D on slides, but could benefit from 3-D visualization.
Network interaction of educational institutions: the practice of using in extended environmental education and upbringing

Abstract The experience in organizing the network interaction of educational institutions in the field of extended environmental education and upbringing is revealed. The model of the regional network interaction is presented, which is the model of the “resource concentrated network”, the central link of which is the resource center - Volgograd State Socio-Pedagogical University. The specificities are determined and substantiated, inhered of the implementation of the regional model of the network interaction of educational institutions in conditions of the digital educational environment of the University, in extended environmental education and upbringing of students. The obtained results of testing of the regional model of network interaction in extended environmental education and upbringing of students, allow us to speak about the effectiveness of the selected model.
Limit of Gaussian operations and measurements for Gaussian state discrimination and its application to state comparison

We determine the optimal method of discriminating and comparing quantum states from a certain class of multimode Gaussian states and their mixtures when arbitrary global Gaussian operations and general Gaussian measurements are allowed. We consider the so-called constant-$\hat{p}$ displaced states which include mixtures of multimode coherent states arbitrarily displaced along a common axis. We first show that no global or local Gaussian transformations or generalized Gaussian measurements can lead to a better discrimination method than simple homodyne measurements applied to each mode separately and classical postprocessing of the results. This result is applied to binary state comparison problems. We show that homodyne measurements, separately performed on each mode, are the best Gaussian measurement for binary state comparison. We further compare the performance of the optimal Gaussian strategy for binary coherent states comparison with these of non-Gaussian strategies using photon detections.