Reviewerly/exHarmony huggingface dataset:
Seismic Image Pattern Analysis Using Fuzzy Logic Controller

Seismic wave is the wave of energy that travels via the earth as a consequence of an earthquake or a volcano that occurs with low-frequency acoustic energy. By analyzing the Seismic earthquake patterns it is possible to find whether the earthquake will occur or not. In this project, the seismic data are analyzed by a Fuzzy Logic Controller. But the seismic data are affected by some unwanted artifacts. The de-noising method has the capacity to reduce the artifacts. Here the Contourlet transforms scheme gives better noise suppression in seismic images and the signal to noise ratio is improved. Further, the de-noised seismic image is segmented using Wavelet transforms for feature extraction. The extracted features can be analyzed by fuzzy Logic Controller which shows whether the seismic image is normal or abnormal. This analysis gives better results in the earthquake analysis and also in the construction of sensitive power plants.
Design of Multiplicative Inverse Value Generator using Logarithm Method for AES Algorithm

Advanced Encryption Standard (AES) algorithm is one of the most widely used symmetric block cipher that is utilized in data protection through symmetric cryptography algorithm, as it offers high efficiency and ability in securing information. In AES, the SubByte/InvSubByte transformation costs an amount of memories for the lookup tables (LUT) that leads to area usage in hardware. As per mathematical equations, the SubByte/InvSubByte is able to share the same resource which is the multiplicative inverse value table. Instead of using LUTs, the multiplicative inverse values can be generated on-the-fly as needed. Therefore, this paper presents the custom hardware design and implementation of multiplicative inverse value generator using logarithm method specifically for AES algorithm. The logarithm method benefits the usage of antilog and log values in obtaining the multiplicative inverse value. The EDA tools as Intel Quartus Prime, Synopsys Design Compiler and IC Compiler are used to perform functional simulation, on synthesis analysis and block-level implementation. Detailed comparison is made between this work and previous implementation. The newly designed multiplicative inverse value generator has achieved the improvement on speed and area as compared to previous implementation. The area of the design is reduced around 8.5% with current improvement of 63.091 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , while the speed has increased to 2.54 ns with a positive slack of 0.11 ns, which is shorter than the previous implementation. The new design also outreached the performance of the common LUT-based design.
Joint Reinforcement Learning Method Based on Roulette Algorithm and Simulated Annealing Strategy

A combined Q and Sarsa algorithm based on united simulated annealing strategy proposed in order to solve the problems that the convergence speed of traditional reinforcement learning algorithm is slow. It could balance the relationship between trial-error and efficiency among different methods by a random dynamic adjustment factor method. The roulette algorithm is used to improve Q-learning, and the simulated annealing algorithm is used to replace the selection strategy of Sarsa algorithm, and the overall convergence rate of the algorithm is controlled by the annealing rate. Finally, the task of reward function is subdivided, and the reward function based on action decomposition is designed. The simulation results show that the improved path planning method can effectively reduce the time cost and the collision times of the first path finding.
Quantum logic at a distance

A quantum gate is realized by optically coupling trapped atoms separated by 60 meters
CHILD LABOURING ACTIVITIES AND ACADEMIC INEFFICIENCY OF SECONDARY SCHOOL STUDENTS IN ILORIN METROPOLIS

Child abuse is an act of inflicting serious, physical injury and emotional torture on a child or person, which is intentional or unintentional and could endanger the physical, health, emotional, social, moral and educational wellness of the child. The contributing factor of child abuse can be identified as poverty and ignorance on the part of the parents or guardians and others. Observing the alarming endless cry of students and those hurt in the process and maltreatment, the researcher was therefore interested in investigating child abuse as it affect the academic efficiency of secondary school students in Ilorin metropolis. To achieve this purpose three (3) research questions were formulated to guide the study. A sample of 150 respondents was randomly selected for the study. The selection was done through simple random sampling technique. The questionnaire was the main instrument used for data collection. The simple percentage distribution was used to analyze data collected through the administration of questionnaire. The result of the analysis revealed that child laboring activities greatly influence secondary school students’ academic performance in Ilorin metropolis. Based on the findings recommendations were made on the need to educate and intensified campaigns, especially on creation of awareness on the menace of child laboring activities – physical and sexual abuse as they deaden the future of the child, family and society.
그림책을 활용한 활동이 유아의 언어발달에 미치는 효과에 대한 메타분석

Objective: This study was aimed to analyze the effects of activities using picture books for young children’s language development and to identify factors that caused differences in these effects by applying meta-analysis.Methods: We conducted a homogeneity test of effect sizes on 21 Korean studies published in academic journals from 1990 to February 2018 and calculated the effect size by applying a random effect model. Additionally, we conducted a meta-ANOVA to investigate whether the effect sizes differed by types of language development, picture book activities, and environmental variables-such as place, time, and agent.Results: The results indicated that the effect sizes of the 21 studies were heterogeneous and the total effect size was 0.90, which was significantly large according to Cohen s standard. The effect sizes also varied by types of language development, picture book activities, and environmental variables.Conclusion/Implications: To increase the effects of activities using picture books for young children’s language development, this study suggested the importance of picture book activities to be integrated with other play areas, teaching methods, and other print materials for the development of literacy abilities, and the link between home and early childhood education institutions.
A Communication Signal Recognition Method Based on Improved Entropy Cloud Feature

Due to the completing of communication environment, the signal is affected by time-varying noise during transmission, which leads to the characteristics of the signal is unstable. Aiming at this problem, this paper proposes a communication signal modulation recognition method based on improved entropy cloud characteristics. Firstly, the Shannon entropy, index entropy and norm entropy of the signals are extracted. Secondly, using these entropy features and integrated cloud models to get improved entropy cloud features. Finally, extreme learning machine based on particle swarm optimization and principal component analysis (PSO-ELM-PCA) is applied to signal recognition. The results of simulations show that the approach in this paper still has a good classification result at dynamic SNR (Signal to Noise Ratio).
LSTM-Based Boost-Phase Ballistic Missile Tracking

Ballistic missile (BM) tracking during the boost phase is the basis of early missile defense. Traditional BM tracking algorithms either perform dynamic modeling with a certain degree of accuracy on the target movement, or rely on the template information of BM's trajectory or acceleration. When the dynamic modeling is not accurate enough or the prior template information cannot be obtained, both the tracking accuracy of the two methods will be reduced. In order to solve this problem, we propose a boost-phase BM tracking method based on the long short-term memory (LSTM) network. Specifically, a BM trajectory database is first established to provide sufficient offline data for network training. And then a LSTM-based network suitable for boost-phase BM tracking is developed, which consists of three bidirectional LSTM layers, a fully connected layer and a linear output layer. Simulation results demonstrate that the proposed method has better comprehensive performance in terms of tracking accuracy and computational complexity compared with the existing BM tracking algorithms.
Virtual Reality consistency with Common Concerns of Humanity: an overview

This paper presents an overview of the consistency of Virtual Reality (VR) with the Common Concerns of Humanity (CCH). Since VR has become widely available and applicable on different areas, it remains unclear if its impact is bad or good on humankind. State of the art of VR impact provides different overviews taken from a single area's point of view i.e. without providing a holistic point of view on humanity, beyond individual interests. The paper reviews the history of VR; the history and issues of common concerns of humanity; and investigates if ethical VR development and use are not contradictory with Common Concerns of Humanity.
Visual Servo Control of Omnidirectional Mobile Manipulator

The mobile manipulator with vision sensor is of great significance to the future factory. How to make the mobile platform part participate in the process of trajectory planning efficiently, and how to reduce the camera calibration error and the influence of external factors on the visual servo accuracy are some problems that need to be solved. Uncalibrated Vision Servo (UVS) based on image has great flexibility and adaptability, therefore, it will solve many problems when it is applied to omni-directional mobile manipulator. A complete constrained mobile manipulator model is established. In this paper, we design a visual servo controller based on the centralized control strategy, and estimate the mapping relationship from the image feature space to the manipulator joint and the moving space of the mobile platform by online identification of the composite Jacobian matrix. Finally, the simulation experiment of Moving plane servo control in space is carried out.
Preparation of applicants of the second (master) level of higher education for the application of advertising and information technologies

The article reveals the peculiarities of training applicants for higher education of the second (master's) level of higher education in the use of advertising and information technologies in future professional activities. Tasks, types and technologies of advertising and information activities in the social sphere are outlined.
Holding All the Aces? Hate Speech: Features and Suppression in Croatia

Despite the undeniable value of freedom of expression in contemporary society, its limitations are considered to be of utmost importance in the context of protecting legitimate values. Intervention possibilities depend on conditions of legal regulation, protective purpose and necessary society extent. Throwing down the gauntlet at freedom of expression, hate speech seems to come out well, being not particularly suitable for legal regulation. Due to its conceptual complexity, it requires a layered social response that implies a common strategy of education, prevention and repression. The purpose of this paper is to present the features of hate speech and its suppression in the Republic of Croatia, where, similar to other countries, racist and intolerant hate speech in public discourse is on the rise. Therefore, after the opening remarks on freedom of expression and basic notions on hate speech, we present the legal response of the Republic of Croatia developed through several legal branches. Solutions have been improved under the influence of the European standards, but adjudication challenges are casting a shadow over their practical effect. We analyze the available national and European case law. Particular attention was paid to the stances of the European Court of Human Rights in relation to Croatia in freedom of expression cases. In addition, our research is presented, in which almost 750 respondents gave an insight into their views on exposure to hate speech, the manner and content of its expression, willingness to report it and opinions on the effectiveness of suppression in Croatia. Results have shown a high perception of the presence of hate speech, predominantly through online expression, with its content being grounded on national or ethnic origin and sexual affiliation. At the same time, the willingness in turning a blind eye was revealed, allowing its free play and slipping from (assessed ineffective) suppression hands.. Considering all this, concluding thoughts on the subject matter are given, guided by reflections on future emphases on hate speech suppression.
Automatic Computer Analysis of Digital Images of Triple-Antibody-Stained Prostate Biopsies

Background: Worldwide, prostatic adenocarcinoma is the most common tumour type among men.Aim: The aim of the present investigation was to develop a computer program to identify normal prostate biopsies and distinguish them from biopsies showing premalignant alterations (LGPIN, HGPIN) and adenocarcinoma.Method: Prostate biopsies (n = 2094) taken from 191 consecutive men during 2016 were stained with triple immunehistochemisty (antibodies to AMACRA, p63 and CK 5).Digital images of the biopsies were obtained with a scanning microscope and used to develop an automatic computer program (Cellda TM ), intended to identify the morphological alterations.Visual microscopic finding was used as a reference.Result: Of the 191 men, 121 (63.4%) were diagnosed as having prostate adenocarcinoma and 70 (36.6%)as having no malignancy on the basis of the visual microscopy.In comparison, computer analysis identified 134 (70.2%) men with malignant disease and 57 (29.8%) with non-malignant disease after exclusion of artifacts, which constituted 10.4% of areas (indicated as malignant disease).Discrepant results were recorded in 15 (7.9%) men, and in 14 of these cases, HGPIN and areas suggestive of early invasion were common.Thus, it was uncertain whether these cases should be regarded as malignant or not.The agreement between the visual examination and the computer analysis was 92.1% (kappa value 0.823, sensitivity 99.2 and specificity was 0.80).Conclusion: It seems that computer analysis could serve as an adjunct to simplify and shorten the diagnostic procedure, first of all by ensuring that normal prostate biopsies are sorted out from those sent for visual microscopic evaluation.
Research on Track Fusion Estimation Method based on Global State Filter

According to the characteristics of target motion and composite guidance, this paper establishes the state motion equation, designs the global state feedback model based on Kalman filter method and proposes a track fusion estimation method of the sea target by introducing the state diagonal matrix weighted fusion method. The method can significantly reduce the computational amount and effectively solve the byte overflow alarm or divergence problem caused by large computation amount in multi-mode composite guidance engineering. Simulation results of active and passive radar composite guidance show that this method can effectively reduce the computation amount and meet the time limitation requirement of composite guidance under conditions of ensuring the precision of track estimation.
Synthetic Data and Hierarchical Object Detection in Overhead Imagery

The performance of neural network models is often limited by the availability of big data sets. To treat this problem, we survey and develop novel synthetic data generation and augmentation techniques for enhancing low/zero-sample learning in satellite imagery. In addition to extending synthetic data generation approaches, we propose a hierarchical detection architecture to improve the utility of synthetic training samples. We consider existing techniques for producing synthetic imagery–3D models and neural style transfer–as well as introducing our own adversarially trained reskinning network, the GAN-Reskinner, to blend 3D models. Additionally, we test the value of synthetic data in a two-stage, hierarchical detection/classification model of our own construction. To test the effectiveness of synthetic imagery, we employ it in the training of detection models and our two stage model, and evaluate the resulting models on real satellite images. All modalities of synthetic data are tested extensively on practical, geospatial analysis problems. Our experiments show that synthetic data developed using our approach can often enhance detection performance, particularly when combined with some real training images. When the only source of data is synthetic, our GAN-Reskinner often boosts performance over conventionally rendered 3D models and in all cases the hierarchical model outperforms the baseline end-to-end detection architecture.
Background Subtraction Based on Online Tensor Decomposition

Background subtraction is the most popular technique for foreground detection to automatically separate foreground objects from background in video sequences. Low- rank subspace learning methods have attracted great attentions in recent years. Compared with the methods based on matrix decomposition which reshape the video data into a matrix, the methods based on tensor decomposition can take good advantage of the spatial information within frames. However, most existing methods based on tensor decomposition are batch-based approaches which generally require heavy memory cost and are unable to adapt to dynamic scenes. In this paper, we propose a background subtraction method based on adaptive online tensor decomposition. The proposed method deals with samples sequentially and also adapts to dynamic environments, which can help reduce the computational burden and memory cost. Besides, adaptive weighting parameter is employed to adjust the degree of attention to the sparsity of estimated sparse component, which can considerably improve the performance of foreground detection. Experimental results demonstrate that, the proposed method can effectively detect the foreground objects with less computational cost.
Time-controlled Regular Language Search over Encrypted Big Data

The rapid development of cloud computing and the arrival of the big data era make the relationship between users and cloud closer. Cloud computing has powerful data computing and data storage capabilities, which can ubiquitously provide users with resources. However, users do not fully trust the cloud server's storage services, so lots of data is encrypted and uploaded to the cloud. Searchable encryption can protect the confidentiality of data and provide encrypted data retrieval functions. In this paper, we propose a time-controlled searchable encryption scheme with regular language over encrypted big data, which provides flexible search pattern and convenient data sharing. Our solution allows users with data's secret keys to generate trapdoors by themselves. And users without data's secret keys can generate trapdoors with the help of a trusted third party without revealing the data owner's secret key. Our system uses a time-controlled mechanism to collect keywords queried by users and ensures that the querying user's identity is not directly exposed. The obtained keywords are the basis for subsequent big data analysis. We conducted a security analysis of the proposed scheme and proved that the scheme is secure. The simulation experiment and comparison of our scheme show that the system has feasible efficiency.
Research on Modeling and Simulation of Complex Equipment Support Mission Profile with Mission Constraints

At present, complex equipment rapid support and the multiple scientific analysis of equipment support are not competent with the combat needs. This paper used a UML- like language to establish the timing relationship model between support activities. Based on the corresponding activities, we used simulation experiments with association rules to obtain a complete support mission profile with mission constraints. The model can establish complex equipment support mission profiles of any numbers and types, highly scalable, and can be updated quickly following the equipment iteration.
Overview of the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies

This overview introduces the task of parsing into enhanced universal dependencies, describes the datasets used for training and evaluation, and evaluation metrics. We outline various approaches and discuss the results of the shared task.
A Novel Deterministic Mixture Particle Filter Based on Multiple Importance Sampling

Considering traditional particle filtering (PF) is based on the importance sampling (IS) methods, which have a poor performance when target density cannot be easily characterized, an algorithm based on deterministic mixture multiple importance sampling (DMMIS) is proposed in this paper. DMMIS methodology is a robust alternative that utilizes a set of proposal distributions as a whole mixture distribution to approximate the target probability distribution. In the simulation, a univariate non-stationary growth model with the Gaussian mixture noise is given, the numerical results show the proposed method has a better estimation accuracy while it needs less particles compared with the generic PF.
Tracking Underwater Target Using Angles-only Measurements

This Surveillance plays a critical role in the marine environment. Target is tracked even if measurements of the range are not available, using bearing and elevation measurements. This research intends to track the target and estimate the parameters of target motion and to reduce noise in the estimated parameters using an effective nonlinear filter, Unscented Angles-only Kalman Filter (UAKF). Mathematical modeling is presented in this research, and simulation is done using MATLAB software. The performance of the UAKF algorithm is shown to be efficient in tracking the target and observer maneuver is recommended to get convergence very quickly in underwater surroundings.
A System of Clothing Boundary Recognition Using Machine Learning For Life Support Robots

Machine learning and image processing are widely used in various fields, such as, robot vision, object recognition, and automated driving technology. This paper is, the use of cameras to acquire image information, and machine learning methods to analyze and identify the edge boundaries of clothes in the Difficulty in putting on and taking off clothes in the elderly and some patients, especially when going to the toilet. Then, clothing boundary recognition and assistive robots can ameliorate these problems by distinguishing where the edges of the pants are so that they can Determine the position of the waistband of the trousers, and perform dressing and undressing assistance work. The ultimate goal of this research is to be able to apply it to the system control of the clothing donning and doffing device.