Reviewerly/exHarmony huggingface dataset:
Research on pedestrian detection system based on intelligent terminal with limited computing ability

Abstract Pedestrian detection technology is the use of computer vision technology to determine whether there are pedestrians in static images or video images, and accurately mark pedestrians. Intelligent vehicles, intelligent monitoring, human behaviour analysis and other aspects are related to pedestrian detection technology, so pedestrian detection technology has been widely concerned by all walks of life. However, in some application environments, the network setting is flexible and the location of the device can be changed at any time, so it is difficult to establish a pedestrian detection system by wired way, and the pedestrian detection system can only be established through WSN. It is necessary to consider how to complete pedestrian detection through intelligent terminal under the condition of limited computing power. Many researchers have put forward a lot of algorithms, which are constantly optimized, but there are still many problems to be solved in practical application. In this paper, aiming at the intelligent terminal in WSN environment with limited computing power, a pedestrian detection system based on HOG+SVM is designed, which can detect moving pedestrians in video in real time. The detection system is mainly composed of detection algorithm and video acquisition module. After the test of INRIA and CVC data set, the accuracy of the detection algorithm in this paper is 89.06%. This method is tested under the intelligent terminal with limited computing power, with a good detection effect.
Construction of Adverbial-Verb Collocation Database Based on Large-Scale Corpus

This paper constructs a high-quality adverbial-verb collocation database based on a large-scale corpus. First, we established a knowledge system of adverbial-verb collocations based on previous studies and linguistic rules. Then, we designed and implemented a knowledge acquisition model of adverbial-verb collocation based on a large-scale corpus. Finally, we evaluated and analyzed the extracted results. The main purposes are to obtain high-quality adverbial-verb collocations by formal means and to provide data support for natural language processing and theoretical and applied linguistic research.
Real Time Performance Comparison of Multi-class Deep Learning Methods at the Edge

Nowadays, machine learning algorithms are used intensively for the detection and identification of various entities on an image with high level of accuracy. Deep learning provides even higher performance results in image processing. However, deep learning requires high computing effort for classification and thus limiting the usability of method in AI at the edge especially in the presence of different classes. In this paper, deep learning is applied as a solution to the problem of detecting and identifying more than one entity of different classes in the image in real time at the edge. In the study, a multi-class traffic sign data set was used. The data set consists of six different sign classes and 6000 high resolution images (1280 × 720). Performance of two models, DetectNet and MobileNet (MobiNet) models were compared. MobileNet had better accuracy compared to DetectNet since it has higher number of parameters. But high number of parameters lessens the real time performance of the model at the edge. According to the tests performed on the Nvidia Jetson TX2 embedded system, the DetectNet model can provide 6–7 FPS performance while the MobileNet 160 version of MobileNet model with $$ \propto \; = \;0.25 $$ has achieved only of 1–2 FPS.
Intelligent Production of Serious Games in Intelligent Tutoring Systems Based on Adaptive Workflows

We propose in this paper a new approach of designing of intelligent tutoring systems that is based on serious games and adaptive workflows. Our objective is to implement a system that allows the intervention of the human tutor to follow up the student progress and solve blocking situations. We also aim to motivate students and make learning fun and challenging through the implementation of serious games in the evaluation process.
Design Guidelines and Evaluation

This chapter focuses on qualityQuality factorsFactors, designDesign principlesPrinciple, and guidelinesGuideline that must be taken into account when stakeholdersStakeholders are creating or evaluatingEvaluating wearableWearability solutionsSolution. The patterns and interactionInteraction paradigmsParadigm described were extracted from the scientificScientific literature and industrial domainsDomain as well. By explaining what approachesApproaches can be employed to evaluate wearableWearability technologiesTechnology, using different methodsMethods for dataData collectionCollection and different criteriaCriteria for assessmentAssessment, this chapter provides readersReaders with a comprehensiveComprehensive list of design principlesPrinciple that can inform decisions from designersDesigners, developersDevelopers, and investigatorsInvestigators when creating novel wearableWearability technologiesTechnology or evaluatingEvaluating existing ones. While some of the design principlesPrinciple are targeted at industryIndustry expertsExperts, others focus on scientificScientific research. Concerning evaluationEvaluation, this chapter also provides readersReaders with examplesExample of scalesScale, questionnairesQuestionnaire, metricsMetrics, and questions that can be employed when assessing technologyTechnology to better understand the userUser perspective and experienceExperience when interactingInteract with a deviceDevices. TheoreticalTheoretical definitions as well as illustrative examplesExample are presented. This chapter concludes with designDesign challengesChallenge and considerationsConsiderations that must be taken into account during the interactionInteraction design and evaluationEvaluation.
How Visualization Supports Creative Cognition for Devising Customer Insight

It is difficult to find out what consumers actually demand, in a society with various information and products. Under such circumstances, the importance of finding consumer insight is increasing. However, it is difficult to find out consumer insight. In this paper, the effect of using visuals for the discovery of consumer insight was verified compared with using text. The experiment was conducted with two groups. Each group came up with ideas on two themes. At that time, the following conditions were set: one is to use visuals and the other is to use text. Then, the contents were analyzed using Finke’s Geneplopre model. The result showed that, the number of ideas with visuals were fewer than the number of ideas with text. However, it was implied that using visuals allows for deeper and wider interpretation of ideas, which could lead us closer to true consumer insight.
Enhanced Sentiment Classification Using Recurrent Neural Networks

Sentiment Analysis and classification becomes a key trend in the human world in analyzing the nature and quality of the product, people's emotion, inference about products, and movies. Sentiment Analysis is the process of classification as it classifies the inference or review into positive or negative. Since the data that are labeled are very expensive and difficult to gather, it is hard. Also, the sarcastic data and homonyms are difficult to be identified. Hence the assumption of reviews will be wrong. The solution to identify the sarcastic words and the words with different meanings happens with the help of Recurrent Neural Networks.
Autonomous decision making for pseudo-Lagrangian drifter deployment from sUAS

This paper presents an autonomous decision making framework for deploying pseudo-Lagrangian air-launched drifters from a small unmanned aircraft system to take in-situ measurements of atmospheric conditions inside severe storms. An estimate of the wind field is leveraged by creating a stochastic model of the wind over which the system reasons. Aircraft state transition uncertainties due to strong wind uncertainties are included. The decision making problem is formulated as a Markov decision process, and multiple online solvers are implemented and analyzed.
To the concept and structure of a competent reader formation in a school course of ukrainian literature

The article substantiates the integrated character of reading competence, clarifies and deepens its concepts and features in the process of teaching Ukrainian literature. The issue of the article is actualized in the context of the competence orientation of the New Ukrainian School (NUS). Reader's competence is explained as an integrated component of students' academic achievement, a system of knowledge and skills, values, perceptions and attitudes of the individual, the ability to adequately act in the future professional field and different life situations. The level of this competence determines the depth of reading the work and understanding its content and meaning, is an indicator of personality development. It is proposed to distinguish the reader's competences related to conscious and expressive reading of the work, the cognitive, which depend on the intellectual and emotional activity of students over the text, communicative, filling the reader's work with broad subject connections, and value related to comprehension of the influence of what have been read on the character of the reader. The way of forming reading competence in the process of working on the text of a literary work is highlighted: a) fast and expressive, conscious reading of the work, its rereading and retelling; b) analysis and characterization of the figurative components of the work in their interaction; c) deducing figurative and conceptual generalizations from what have been read. Reader's competence is presented as a concept, each component of which relates to the appropriate stages of students' work on a work of art. Synonymic connections between the concepts of reading, subject and literary competence are revealed, which is in line with the features of the competent reader as defined in the renewed school curriculum for literature (2017). Important conditions for the formation of a competent reader are the continuous development of his interest in literature, interested reading of works of fiction, scientifically grounded literary analysis and awareness of the personal meaning of the work that have been read. The system-structural approach to the formation of pupils' reading competence which provides the purposeful application of the system of teacher's educational actions not only in the content of a particular work, but also in the ways of reading activity over its text is approved.
A GAN to Fight Video-related Traffic Flooding: Super-resolution

Image and Video Super-Resolution problems become relevant in applied Deep learning due to recent results on using Convolutional Neural Networks and Adversarial Training method to solve such problems. This body of work has focused on conceiving or improving super-resolution methods, and validating them. Little attention has been devoted to their application. Video streaming has the highest popularity among Internet users being responsible for the most significant portion of today's Internet traffic. In this paper, a single image super-resolution model is applied to conceive a video super-resolution model. The designed model was tested against a video base made up of 220 clips, and each clip was encoded in four resolutions. The numerical results showed that the conceived model output is virtually indistinguishable from its ground truth and its use in the context of video distribution decrease by almost 84.5% the related traffic.
In which Clause do Subordinate Conjunctions Prosodically Belong?

Abstract This paper deals with the position of three Czech subordinating conjunctions že ’that’ , když ‘when’ , and až ‘when’ within the prosodic word, using the phonetic annotation in the ORTOFON corpus. The position of subordinating conjunctions is traditionally described as initial within the subordinate clause, but the situation in spontaneous speech is not so clear. This paper shows the functional differences between the various positions within the prosodic word and presents the words which are most frequently combined with the selected conjunctions.
Text Representation Method Combining Multi- level Semantic Features

The text vector representation transforms text from unstructured to structured, from high dimensional to low dimensional, and from sparse to dense, which is the basic task of text analysis. The senLDA model obtains the multinomial distribution of topics on the document based on the sentence, but due to the lack of semantic information for words, there is incomplete coverage of the high-value information and thus affects the effect of text representation. Aiming at this problem, a method that combines senLDA with Word2Vec's word-level features is proposed, which fuses three-level semantic features of words, sentences and documents to realize the text representation. F1 value of three datasets were increased by 11.41%, 17.88%, 17.63% respectively compared to the senLDA method, and increased by 4.65%, 7.73%, 8.62% respectively compared to Word2Vec.
A Novel Method to Access Scientific Data from IRNSS/NaVIC Station Using Cloud Computing

When data generated is in continuous form, no matter how small it is, there ought to be a provision to store it in a place where safety is ensured and storage capacity never runs out. Also since the data is very crucial and we cannot take a chance of data falling in wrong hands, permission to access the data is given only to the authorized persons, thereby ensuring safety of the data. These things can be achieved using cloud computing which is the best platform for maintaining, storing, and monitoring the data.
PLC programming to optimize water flow in secondary cooling zone

Abstract In the paper was made PLC programming, with which an adaptive system was implemented, was used the Simatic Manager Step 7 software, which is specific to the chosen PLC Simatic S7-300. Input data, computation relationships and correlations between the different sizes were retrieved and processed from the database of the two human operators, commanding the opening of the valve automatically. With this software, it was possible to eliminate the human factor in controlling parameters specific to the continuous casting secondary cooling zone.
Automatic Mining of Cause-Effect Discourse Connectives for Russian

Study of cause-effect discourse connectives can help in automated discourse processing and automatic identification of argument units. Besides conjunctions and other functional words and expressions, there are different multi-word expressions containing content-words (e.g. is etogo sleduet ‘it follows that’) that have the function of cause-effect connectives. We present a method for connectives mining for Russian language. Firstly, the seed list of 143 multi-word connectives was manually extracted from the Ru-RSTreebank corpus. Two Word2Vec models, trained on the news corpus, were used to detect new multi-word connectives. Before first model training, connectives from the seed list were glued to build multi-word tokens. Before second model training, in addition to it, all 3-grams in corpus, that correspond to the specific proposed patterns based on anaphoric expressions, were also glued in the same way. The method based on the second model gives a satisfactory result and lets expand connectives list for cause-effect discourse relations, after manual editing (286 new connectives).
A Multi-Target Tracker based on Gaussian Mixture PHD Filter with Jump Markov System Models

A Gaussian mixture probability hypothesis density (GM-PHD) filter with jump Markov system (JMS) models was proposed to track multiple maneuvering targets that change between several models (or modes) matched to specific maneuvers of targets. However, this approach provides only the state estimates of maneuvering targets at each time step, which is not enough to be utilized in real target tracking systems that require the trajectories of the targets. In this paper, we propose a new multi-target tracker based on the GM-PHD filter with JMS models, referred to as the GM-PHD tracker with JMS models, which provides both the state estimates of maneuvering targets and their identities (or labels). The performance of the proposed tracker is demonstrated via an illustrative example in the air traffic control application.
An Adaptive Parameter Control Scheme of Core Devices in DV-QKD

In a discrete-variable quantum key distribution (DV-QKD) system, the optical devices, such as the single photon detector (SPD) and optical fiber, can be affected by the external environment. This will lead to the reduction of the efficiency of QKD system. In order to reduce the impact of the changes of external environment and maintain the system efficient and stable, it is necessary to control the parameters of SPD and phase modulator (PM). In this paper, an adaptive parameter control scheme of core devices in DV-QKD system is proposed to keep the QKD system efficient and stable. The scheme has been implemented and tested in a “Plug&Play” DV-QKD system developed by our research group. The experimental results demonstrate the effectiveness of our scheme. Besides, the scheme can also be modified accordingly to suit for other types of DV-QKD systems.
MRClose

Frequent itemset mining is one of the most important tasks of data mining, while closed frequent itemset is a lossless compression of frequent itemset with many advantages such as high efficiency, no redundancy and so on. In the era of big data, closed frequent itemset mining algorithms which are running on a single computer can't adapt to the demand of huge amounts of dataset, so the parallel algorithm is needed. This paper analyzes three key issues in closed frequent itemset mining including search space partition, pruning strategy and strategy of global closed itemset checking, proposed a parallel algorithm for mining closed frequent itemset named MRClose which is based on MapReduce. Experiments show that MRClose algorithm has achieved good results in both performance and result compression.
Wonderful Clips of Playing Basketball: A Database for Localizing Wonderful Actions

Video highlight detection, or wonderful clip localization, aims at automatically discovering interesting clips in untrimmed videos, which can be applied to a variety of scenarios in real world. With reference to its study, a video dataset of Wonderful Clips of Playing Basketball (WCPB) is developed in this work. The Segment-Convolutional Neural Network (S-CNN), a start-of-the-art model for temporal action localization, is adopted to localize wonderful clips and a two-stream S-CNN is designed which outperforms its former on WCPB. The WCPB dataset presents the specific meaning of wonderful clips and annotations in playing basketball and enables the measurement of performance and progress in other realistic scenarios.
Saliency and Burstiness for Feature Selection in CBIR

The paper addresses the problem of visual feature selection in content-based image retrieval (CBIR). We propose to study two strategies: the first one is using visual saliency, that selects the most salient features of the image and the second one exploits burstiness, that detects and processes the repeated visual elements in the image. To detect and describe the visual features in images, we rely on a deep local features approach based on convolutional neural network. The two strategies are evaluated for image retrieval on different datasets, according to two criteria: quality of retrieval and volume of manipulated features.
Stochastic modeling of nonlinear random vibrations using heavy-tailed mixture distribution

In this study, we propose a simple probability density function (PDF) model of Gaussian-Laplacian mixture type, which is capable of parameterizing a heavy-tailed data easily. We construct our model of a convex combination of Gaussian and Laplacian PDFs and derive a minimal parameterization of it. Next, we conduct least-squares fitting of our model to a heavy-tailed data generated by a random Duffing oscillator and obtain over 94% of residual sum of squares (RSS) fitness. The resulting model is applied to predicting transient moment responses and yields over 97% of RSS fitness to Monte{Carlo simulation results of the original system.
Towards a Framework for Performance Testing of Metaheuristics

Metaheuristics have been very successful in solving difficult optimization problems by employing randomness in their search mechanism. They find near-optimal solutions in reasonable amount of computation time and resources. Testing of metaheuristics is performed by computing descriptive statistics as well as by comparative testing with other State-of-the-art methods. However, performance guarantees for Metaheuristics are not provided, which limits its commercial potential. An attempt has been made in this paper to propose a framework for Performance Testing of Metaheuristics.
Blind Bandwidth Extension with a Non-Linear Function and Its Evaluation on Automatic Speaker Verification

This study evaluates the effects of some non-learning blind bandwidth extension (BWE) methods on state-of-the-art automatic speaker verification (ASV) systems. Recently, a non-linear bandwidth extension (N-BWE) method has been proposed as a blind, non-learning, and light-weight BWE approach. Other non-learning BWEs have also been developed in recent years. For ASV evaluations, most data available to train ASV systems is narrowband (NB) telephone speech. Meanwhile, wideband (WB) data have been used to train the state-of-the-art ASV systems, such as i-vector, d-vector, and x-vector. This can cause sampling rate mismatches when all datasets are used. In this paper, we investigate the influence of sampling rate mismatches in the x-vector-based ASV systems and how non-learning BWE methods perform against them. The results showed that the N-BWE method improved the equal error rate (EER) on ASV systems based on the x-vector when the mismatches were present. We researched the relationship between objective measurements and EERs. Consequently, the N-BWE method produced the lowest EERs on both ASV systems and obtained the lower RMS-LSD value and the higher STOI score.
Analysis and Geometry on a Reduced Complex Space

This chapter focuses on three fundamental tools for working with reduced complex spaces.
Live Class Monitoring Using Machine Learning

Facial recognition is a propitious tool for consumers of social infrastructure electronics. Digital cameras and mobile phones are being successfully used in recent years for digital processing of facial imagery. In this research paper, we have presented a Live class monitoring system using a face recognition algorithm which is capable of (a) processing image of a student in real-time camera-input environment using OpenCV, (b) saving the details in the backend using SQLite, and (c) keep updating the presence and absence of the student on the basis of trained dataset in Live database using Firebase. A person with the authentication, i.e., person with a dedicated QR code, can also monitor the presence and absence of the students in a class through the mobile application developed using App Inventor which is Google’s open-source platform. The dedicated mobile application will show the image of the student if he/she is present in the class and will gray out the image of the student if the opposite happens, i.e., if the student is not present for a specified time period. OpenCV library is used to import some important functions for recognition. Haar classifier and local binary pattern histogram has been used to detect the face. To store the details, SQLite3 has been used. The algorithm is applied real-time condition. The language used is Python3.