Reviewerly/exHarmony huggingface dataset:
Dynamic tracking method for multi-frame moving target based on symmetry algorithm

Aiming at the problems of poor tracking effect and low precision of traditional methods, a symmetric algorithm is introduced to dynamically track moving objects in multiple frames. Symmetric encryption algorithm is used to hide the reversible information of the image, and the correlation between ad jacent pixels of the encrypted image is increased through high-bit flip prediction. Generate sparse pixel classification matrix to improve compression efficiency and reduce the occupation of effective bit-plane space. Embed secret information in the most significant bit of the encrypted pixel based on the hidden key to generate an encrypted-portable image. The receiving end can generate two kinds of decrypted images, one is consistent with the original image, and the other contains the secret information. Has hidden key to extract secret information from ciphertext image and plaintext image separately. Under the hiding of target image information, a window adaptive adjustment algorithm is introduced to track the target, and a comprehensive predictor is designed to predict and track the target when the target is temporarily blocked. The experimental results show that the target tracking accuracy of this method can reach 99% and the prediction accuracy can reach 100%. The proposed method has strong security performance and high tracking prediction accuracy, and is a feasible target dynamic tracking method.
Comprehension Correlates of the Occurrence and Deletion of <i>“de”</i> in Mandarin “N1 <i>(de)</i> N2” Structures

Based on corpus materials and online semantic judgment surveys, this paper investigates the comprehension differences related to the occurrence and deletion of “de” in the Mandarin “N1 (de) N2” structure. By applying PCA and LME modeling on the survey data, we provide a multi-level database of semantic measurements for a set of Chinese “N1 (de) N2” structures as well as a quantitative analysis regarding the correlation between structure-level and constituent-level semantic features. The research shows the following: (1) The “de”-occurring structure is more likely to be interpreted as indefinite than the “de”-deletion structure. (2) Animacy of N1 is positively related to the grammaticality of the “de”-occurring structure, while animacy of N1 is negatively related to the grammaticality of the “de”-deletion structure. The research findings provide evidence for prototype effects in the process of language comprehension. We propose that in natural comprehension, there is a high-animacy bias for N1 regarding the “de”-occurring structure; while a low animacy interpretation for N1 is more prototypical for the “de”-deletion structure. Accordingly, the “de”-occurring structure tends to be interpreted as a possessive, while the “de”-deletion structure is more likely to be interpreted as a modifier-head structure.
A Multi-agent Framework for Visitor Tracking in Open Cultural Places

Safarini, Muhammed Safarini, Rasha Thaher, Thaer Rattrout, Amjad Sabha, MuathReal-time tracking of pedestrians has attracted tremendous attention for many purposes, especially studying their behavior. The perfect tracking is still a challenging open research. In this paper, a novel approach was introduced by employing a multi-agent vision-based system to achieve an accurate real-time tracker. The proposed approach is recommended to be used in open cultural places where there may be some obstacles including buildings, columns, and many others. The proposed model efficiency was assessed on four real Multiple Object Tracking (MOT) challenge benchmarks in terms of Multi-Object Tracking Accuracy (MOTA). Simulation results reveal a high efficacy of the proposed model compared with the state-of-the-art techniques.
Combined kernel KELM prediction model based on quantum particle swarm optimization

Extreme learning machine is a new type single hidden layer feedforward neural network, which overcome the weakness of traditional error hack propagation method, and increase the training speed of algorithm as well as decrease the adjust time of parameter. Kernel idea was used to extreme learning machine by kernel extreme learning machine to instead random map. Kernel extreme learning machine has more looser restrictions and fewer parameters as well as more simple computational complexity and better generalization, in response to the insufficient fitting ability of kernel extreme learning machine, a kernel extreme learning machine based on multi-scale wavelet kernel and polynomial combination was presented in this paper. The presented algorithm optimize combined kernel parameters, weights, and penalty factors, and test on the UCl dataset. The experiment shows that the combined nuclear learning machine owns higher prediction accuracy and other advantages compared with SVM and single core learning machine.
An Improved Low-cost Binocular 3D Reconstruction Algorithm Based on Open AI Platform

A simple, low-cost and good reconstruction-model method has always been a hot and difficult problem in the field of 3D reconstruction. A binocular 3D reconstruction algorithm based on image object detection technology is proposed. First, two low-cost web cameras are employed to acquire images. After the camera calibration is completed, FAST algorithm is applied to detect the feature points of the two images. Then, the image object is obtained by open AI platform. At the same time, image object is used to determine points: only the feature points located in the object area of the images are described by SIFT. In the next steps, the matching, calculation and triangulation of the world coordinate system are also completed by using these feature points. Finally, OpenGL is utilized to complete the 3D visualization of the object. The simulation result shows that the improved 3D reconstruction algorithm can effectively eliminate the 'glitch' caused by mismatching of feature points. The improved algorithm can greatly improve the accuracy of feature point matching without increasing the hardware cost. Feature-point matching accuracy and intuitive 3D reconstruction-model are demonstrated to show the feasibility and validity of this approach.
Improvement of Safety Assessment by UsingTOPAZ Process for UAV Conflict Resolution

To improve the safety of UAV operating in airspace, TOPAZ method is introduced for the safety assessment of UAV flight conflict resolution technology. Firstly, the conflict scenes including 45°, 90° crossing encounter for single-aircraft and simultaneous crossing encounter for doubleaircraft are added to construct the airspace operation environment for UAV. Then a typical UAV conflict resolution algorithm (a modified GA algorithm) is selected as the safety assessment object and is systematically evaluated according to the process of TOPAZ. Finally, a dynamic heuristic factor is introduced to modify the selected algorithm and validate the improvement of safety level of the algorithm by TOPAZ.
Image Retrieval Based on Texton Frequency-Inverse Image Frequency

In image retrieval, the user hopes to find the desired image by entering another image as a query. In this paper, the approach used to find similarities between images is feature weighting, where between one feature with another feature has a different weight. Likewise, the same features in different images may have different weights. This approach is similar to the term weighting model that usually implemented in document retrieval, where the system will search for keywords from each document and then give different weights to each keyword. In this research, the method of weighting the TF-IIF (Texton Frequency-Inverse Image Frequency) method proposed, this method will extract critical features in an image based on the frequency of the appearance of texton in an image, and the appearance of the texton in another image. That is, the more often a texton appears in an image, and the less texton appears in another image, the higher the weight. The results obtained indicate that the proposed method can increase the value of precision by 7% compared to the previous method.
An Empirical Study of Visual Data Analytics in-Line of Indexing Approach

The enterprises are looking for the depth solution, which can help them in their problem solutions. The simple result charts and graphs are not enough for the same. They want to explore more in details information, for that, we have opted the visual analytics, which is the best performance option. In this paper we are discussing the basics of the visual analytics, some work of visual analytics and its importance in coming ages which gives us motivation to investigate in the future work. The manuscript is consisting of four sections. e.g., Sect. 1 is discussing about the overview or the theme, Sect. 2 is lightning on the current work is carried out by some researcher. In the Sect. 3 we are trying to torch the light on recent issues and importance, finally in Sect. 4 we are concluding the manuscript with conclusion and future work followed by Sect. 5 as references.
Evolutionary Computation and Meta-heuristics

This chapter presents several methods of evolutionary computation and meta-heuristics. Evolutionary computation is a computation technique that mimics the evolutionary mechanism of life to select, deform, and convolute data structures. Because of its high versatility, its applications are found in various fields. Meta-heuristics described in this chapter are considered as representatives of swarm intelligence, such as particle swarm optimization (PSO), artificial bee colony optimization (ABC), ant colony optimization (ACO), firefly algorithms, cuckoo search, etc. A benefit of these methods is global searching as well as local searching. Existence of local minima or saddle points could lead to a locally optimum solution when using gradient methods such as the steepest descent search. By contrast, the methods described in this chapter can escape from such local solutions by means of various kinds of operations. Methods of evolutionary computation and meta-heuristics are used in combination with deep learning to establish a framework of deep neural evolution, which will be described in later chapters.
Robotic Imitation by Markerless Visual Observation and Semantic Associations

In this paper we present an approach for learning to imitate human behavior on a semantic level by markerless visual observation. We analyze a set of spatial constraints on human pose data extracted using convolutional pose machines and object information extracted from 2D image sequences. A scene analysis, based on an ontology of objects and affordances, is combined with continuous human pose estimation and spatial object relations. Using a set of constraints we associate the observed human actions with a set of executable robot commands. We demonstrate our approach in a kitchen task, where the robot learns to prepare a meal.
Compact Representation Learning Using Class Specific Convolution Coders - Application to Medical Image Classification

Medical image classification using deep learning techniques rely on highly curated datasets, which are difficult and expensive to obtain in real world due significant expertise required to annotate the dataset. We propose a novel framework called Class Specific Convolutional Coders (CSCC) to tackle the problem of learning highly discriminative, compact and non-redundant feature space from a relatively small amount of labelled images. We design separate attention-driven convolution network based feature extractors for the categories. These feature learning modules are further intuitively combined so as to make the whole image recognition system end-to-end trainable. Results on different medical image classification tasks show the advantages of our contributions, where our proposed methods outperforms the benchmark supervised deep convolutional networks (CNNs) trained from scratch.
Blockchain as a Service for Cloud Storage (BaaS)

With the help of cloud computing users can remotely store the data. Most importantly cloud provides data storage facility. It becomes difficult when there are many users for sharing the data. The group head and the members associated with the group can access the data they can edit as well as delete the data while ensuring the security of the data from unauthorized cloud server, Since the membership of the cloud keeps on changing frequently. Hence to protect cloud from these untrusted source and unauthorized access, Advanced encryption techniques and the various algorithms is introduced. However, in terms of security and competence the system lacks behind. Due to the information sharing with the multiple group members the data files which has the content gets stored in some part of the cloud for indefinite period of time and there is this huge possibility that the owner of the cloud may alter the information which is stored by the user. Hence to retain trust on cloud provider and maintain the authenticity of the data deletion of the file is required on daily basis. To improvise this flaw of the system we are introducing new software or say technology which can be relied on in terms of data sharing and storage in the groups within the cloud.
Latent Markov Chain Encoding for Abnormal Landing Event Detection

Landing is a high-risk stage compared to other flight stages. The goal of this research is to detect abnormal landing events at early phase from live video surveillance alone. A weakly supervised feature coding method is proposed to enforce features shift in a predictable pattern. On top of this, a latent Markov chain model is proposed for abnormal events detection. Finally, a novel recurrent neural network (RNN) with relatively simple structure is proposed which integrates all proposed methods into one end-to-end model. Experiment results shows that this model is able to generate interpretable features from intermediate layers. The proposed model is capable to provided competitive abnormal event detection results.
Poster Abstract: Topological Analysis for Knowledge Discovery from Building Sensor Data

Distributed sensor networks provide greater detail and potentially valuable insights into the behavior of complex systems, for instance retrofitting buildings with heating, ventilation and air conditioning schemes to improve efficiency and user comfort. Extracting useful knowledge from this data is challenging particularly where the underlying system is not well understood. This work demonstrates an application of Q-Analysis, a computationally simple topological approach for summarizing large sensor data sets and exploring the relationships between the different variables. The technique is applied to building energy management data from a large building that has been retrofitted with a sensor network. The technique is shown to be effective at extracting useful knowledge of the underlying system, which are highly specific and complex in nature, providing an understanding of the floor behaviors rather than the individual terminal unit performances.
Fuzzy-controller simulation model as a tool of optimization of a limited availability ground handling system at a hub airport

The task of optimizing the optimal quantity of technological resources of a hub airports functional subsystem, executing an individual process step of ground handling, is considered. This problem is relevant to hub airports. The analysis is limited to limited availability systems in which handling of certain orders can be performed only by certain resources which are used in reference to automated and mechanical equipment, industrial equipment, personnel, etc. The optimization problem is formulated in a probabilistic statement. An approach to solving the problem is described. We suggest using a simulation computer model as an optimization tool. The model takes into account the features of passenger flows and the operating process of a hub airport and includes a fuzzy controller that reflects the logic of the airport operator who controls the ground handling process. The paper describes a model example of solving an optimization problem indicating the possibility and expedience of using a fuzzy controller as a model for the strategy of a human operator. Time dependence of the quantity of technological resources of the functional subsystem is the main result of optimization. This dependence is applicable at the stages of making decisions concerning increase of its capacity rate, operative resource management, planning staff shift work, solving a number of other tasks, especially relevant for hub airports with intense but non-uniform flows of aircraft and passengers.
Card Game Bluff Decision Aided System

Main goal of the paper is to create and evaluate poker player bluff aided system. To achieve the goal there is a need to create poker game system and learning environment. There are several assumptions and limitations regarding paper subject. Poker game variant is decided to be Texas Hold’em No Limit. This variant was chosen mainly because it is probably most popular poker variant nowadays, it is very entertaining and it has simple rules. Another assumption is that the game is for two players only. This is connected with several factors. Most important is the reduction of computational complexity. Other reasons are: reduction of game system and learning system development complexity, fitness function definition, more complex fitness score assessment. Results evaluation is, due to problem abstraction, limited to analyzing and reasoning of the decisions. AI learning will be performed without external source of knowledge, that is without using historical data containing professional poker players decisions. This system will not use existing poker game solutions as it is an attempt of creating AI with use of live score assessment only. Important thing is, that this system is not meant to model complete poker player but focuses on bluff actions only.
Preliminary Study of Museum Education Research Trends Based on the Published Academic Papers and Dissertations from 2014-2019

This work aims to analyze the trends of museum education researches in Korea from 2014 to 2019. Accordingly, this involved the collection of target researches published in universities and academic journals, a task that was accomplished by the Korea Education and Research Information Service (KERIS). A total 316 researches (90 academic papers and 226 theses) will be thoroughly scrutinized and assorted into several different research types. In particular, 222 papers out of the 316 focused on museum educational programs while 99 of them targeted the research role and function of museum education. Therefore, this paper will mainly highlight those 222 papers concerning museum educational programs. Among them, three main subjects were discovered: the development of programs, the organization of programs, and the analysis of programs. Upon reviewing these subjects, five different distinctions have been established. First, Gyeongin National University of Education is the primary university to publish with regard to the development of programs. Second, in terms of organizing the programs, Kyung Hee University is the top university; this institution is also renowned for researching other sectors. Third, Chung-Ang University is the main institution behind the publishing of analyses of museum programs. Fourth, the main methodologies are derived by developing and analyzing programs that are case studies, whereas qualitative and quantitative methodologies are chosen through the organization of the papers. Finally, the targets of the papers are usually pupils (mainly primary schools) and students in the secondary grade. The papers meant for the students seem to integrate history and arts curriculum into the museum education, and this designated target and curriculum should be refined in the future. Notably, the papers are mainly selected by national and public museums for their research where are located in the capital areas.
Contribution of internet resources to mastery genetic concept on prospective teachers

Abstract The use of the internet and smartphones in learning is interdependence because of its function which provides various learning resources. This study aims to determine the role of the internet as a learning resource for prospective teacher on concepts mastery, using a correlation design with Pearson correlational statistical tests to describe and measure the degree of association between the two variables, namely the prospective duration of the online teacher and the acquisition of concepts. The subjects of this study were semester 5 (five) students majoring in biology education totalling 99 students at one of the university in West Java. The study was conducted on Genetic course in mobile learning with Gen 21 CS application in 3 (three) Genetic course activities. The results showed that online duration correlated with mastery of concepts. The highest correlation with a correlation value of 0.37 in the third activity, with the value of the level of significance of the category is low, which means that besides the duration of online there are other things that contribute to concept mastery.
Group Authorization Using Chinese Remainder Theorem

The paper presents a new way of group authorization for a population of users of a given IT system. Group authorization is highly desirable in applications where the submission of individual rights is not sufficient: service support (service-owner relationship), anonymous verification of belonging to a particular group (e.g., disabled persons, city inhabitants), mutual authentication of unknown-in-advance persons, discounts for groups (e.g., family travelling), etc. The solution uses the Chinese Remainder Theorem, which for any congruence system (with a collection of pairwise relatively prime moduli for the whole set) allows determining one number, which is its solution. After assigning a congruence (or their collection) to a given person, one can use such a connection to dynamically calculate the congruence system for all group members at a given place and time. The correct result for each member means the successful verification of the rights of the group as a whole. Calculations are repeated after each change in group composition. The proposed solution not only increases the functionality of the authorization system but also increases the degree of anonymity in the authentication process, as the user identification is replaced by the identification of the group of which they are members.
Concatenated Global Average Pooled Deep Convolutional Embedded Clustering

Deep Clustering learns cluster friendly salient features in embedded space. In our previous work of Global Average Pooled Deep Convolutional Embedded Clustering (GAPDCEC) algorithm, the last convolution layer feature maps are pooled to build the embedded space. This considers only spatial information retains in the last convolution layer of the encoder, which unable to capture discriminative features from entire convolutional layers. To address this issue, we propose a solution using concatenation of all convolutional layer outputs and then Global Average Pooling (GAP) is applied on the concatenated feature maps in the encoder. This will encourage the network to learn cluster friendly features of all convolutional layers. Our experimental results prove the efficiency of proposed Concatenated Global Average Pooled Deep Convolutional Embedded Clustering (CGAPDCEC).