Reviewerly/exHarmony huggingface dataset:
Data Saves the Whales!

Data Saves the Whales! was developed as part of the Advanced Manufacturing and Prototyping Integrated to Unlock Potential (AMP-IT-UP) project, funded by the National Science Foundation through its Math and Science Partnership program. A central outcome of the AMP-IT-UP project was the development of 1-week modules for core middle school math and science courses that were aligned with best practices as put forth in the Next Generation Science Standards and Standards of Mathematical Practice. Data Saves the Whales! is a middle school mathematics module that is set within a marine ecosystem. It focuses on independent and dependent variables and Cartesian coordinate graphing, and on math practices related to data representation—i.e. how data can be represented in different ways to communicate various messages to an audience. Students engage in a simulated investigation to collect oceanographic data, meant to mimic the experimental procedures used by marine scientists when collecting samples, and learn different methods of graphing the data to effectively communicate their findings. This module features work conducted by the research team of Dr. Ellery Ingall, Professor of Earth and Atmospheric Science at Georgia Tech, and integrates math skills with marine science and the concepts of food webs and the interdependence of organisms.
Convert Arabic Letters Voice into Gesture

Abstract This paper suggest approach to solve the problem of social communication between blind and dumb by converting voices of 28 Arabic letters (ي,.........,أ) into gesture (images) by extraction features by using Mel-frequency Cepstral coefficients (MFCC)and classify the types of letters by using; J48, KNN, and Naive byes (NB). Several features are extracted from speech voice of Arabic letters voices. The dataset collected by recorded voices from twenty different persons, each person recorded ten voices for each twenty eight letters so the total dataset are 5600 voices (200 voices for each 28 letters). Mel-frequency Cepstral coefficients are extracted from 5600 voices of letters which convert the voices into a signal and extract features vector to classify later by using J48, KNN and NB algorithms, which may vary in time or speed signals. The experimental results shows that the best accuracy of speech recognition algorithm by using the J48 algorithm with a performance ratio of 100% while KNN is the 94.023% and Naive byes is the 20.012%.
A Face Quality Evaluation Method Based on DCNN

In response to the problem of low recognition rate caused by low quality face image in face recognition, a face quality evaluation method based on deep convolutional neural network is proposed. Firstly, the depthwise separable convolution method is adopted to establish a depth-convolution model which contain 8 blocks to achieve face feature extraction. Secondly, the last layer of the convolution module connects 8 output branches, and uses regression and classification methods to predict the probability of 8 attributes, namely yaw, pitch, norm, opening mouth, occlusion, blur, dim, and closed eyes. The proposed method also proves that norm value can help analyze the quality of face image. Finally, the weight optimization is realized by taking the video pass rate as the optimal objective function. the quality score of face image can be calculated by weighting each branch The higher the score, the better the quality of the face image. In this paper, an end-to-end face quality evaluation model is implemented by using depthwise separable convolution method. The parameters of the model are less than 60000, the operation speed is fast, and the evaluation results are accurate; and it can filter out low-quality face image in real time, and recommend high-quality face image to face recognition model.
Channel-Theoretic Account of the Semantic Potentials of False Diagrams

People make mistakes. Whether because lines are misdrawn, data are mistabulated, or because coffee is spilled on documents, diagrammatic representations may not be entirely correct. Yet experience tells that such diagrams are not entirely useless. In this paper, we describe a semantic theory of representation, which naturally explains the utility of erroneous diagrams. In particular, the theory captures the possibility of obtaining true pieces of information from erroneous representations in a reliable manner. We identify two dimensions along which there are choices in how to read a representation. In one dimension, we may read only part of the representation, avoiding the erroneous information. We call this partial reading. In the other, we focus on abstract properties of the representation, ignoring errors in the precision of the information represented. We call this abstract reading. Along either or both dimensions, true information can be obtained from erroneous diagrams. The theory is based on Barwise and Seligman’s channel theory, and captures these different modes of readings in terms of multiple representation systems in which a diagram carries information about its target. On this theory, one and the same diagram can be accurate in one system and inaccurate in others, and the reader switches systems when they read the diagram in different modes.
Pet immunity for PIR sensors using deep learning

Despite the ubiquity and wide spread success of deep learning for various tasks ranging from image classification to text generation, the adoption in the world of embedded systems and IoT seems relatively slower. In our work, we investigate possible application of this area of research to tackle the problem of home surveillance, namely pet immunity of passive infrared sensors (PIR). The classification whether the sensor was triggered by a person or a domestic pet is based on sequence of images taken by a camera attached to the sensor. The main challenge are extremely restricted resources of the on-board ARM CPU, especially memory in the order of kilobytes. We propose a simple approach based on background subtraction and classification using convolutional neural network. We demonstrate how despite its simplicity, it achieves scores that are competitive with much larger models employing pretrained open source detectors.
Airport Detection on Optical Satellite Images Using Machine Learning Method

A method for detecting airport on remote sensing images based on machine learning method is proposed. First, Canny operator and Hough Transform are applied on optical satellite images of Google Earth to detect edge and extract straight line. Using the character that airport regions are more likely to have parallel lines, the total length of parallel lines that are close enough are accumulated, and the region which has the longest total length of parallel lines is selected as an airport region. And the feature vectors of airports and non-airports are calculated respectively in the accumulating process. Finally, an airport detection model is learned from the features vectors with support vector machine learning method. The proposed method has been tested on remote sensing images and compared with other methods. The result shows that the method can generate a considerably high detection rate and low false alarm rate on optical satellite images in a reasonable time.
An Improved Optimization Algorithm Applied in Image Processing

Aim to improve the default of Harmony Search (HS) algorithm which is easily trapping into local optima while doing global search. An improved optimization algorithm presented in this paper. Mining historical iteration information to set up an instructive dataset, then the global search mechanism is combined with a local search strategy. After that, an integer planning study based on distance strategy and the fuzzy constraint processing in spatial division of solution space. The proposed model combines with FCM applied in image processing. The experimental results show that the proposed method can get more accuracy and less CPU operation time versus FCM and s-FCM.
Dual Triggered Correspondence Topic (DTCT) model for MeSH annotation

Accurate Medical Subject Headings (MeSH)annotation is an important issue for researchers in terms of effective information retrieval and knowledge discovery in the biomedical literature. We have developed a powerful dual triggered correspondence topic (DTCT)model for MeSH annotated articles. In our model, two types of data are assumed to be generated by the same latent topic factors and words in abstracts and titles serve as descriptions of the other type, MeSH terms. Our model allows the generation of MeSHs in abstracts to be triggered either by general document topics or by document-specific "special" word distributions in a probabilistic manner, allowing for a trade-off between the benefits of topic-based abstraction and specific word matching. In order to relax the topic influences of non-topical words or domain-frequent words in text description, we integrated the discriminative feature of Okapi BM25 into word sampling probability. This allows the model to choose keywords, which stand out from others, in order to generate MeSH terms. We further incorporate prior knowledge about relations between word and MeSH in DTCT with phi-coefficient to improve topic coherence. We demonstrated the model's usefulness in automatic MeSH annotation. Our model obtained 0.62 F-score 150,00 MEDLINE test set and showed a strength in recall rate. Specially, it yielded competitive performances in an integrated probabilistic environment without additional post-processing for filtering MeSHs.
Harfu Jar Detection System In Al-Quran Using Pierce Similarity Algorithm as a Basic Learning Media of Arabic Language

Arabic language has a very broad language structure, we need to know the most dominant Arabic language found in the Al-Quran, so to know the interpretation (Tafsir) of the Al-Quran, we must learn Arabic language. One of the basic parts of Arabic language is nahwu science, nahwu is the study of laying row in Arabic such as the kasra, dhamma and fatta. Jar letters is one of basic part in nahwu science. In this research, a detection system about Jar patterns will be developed with image processing approach. This system was built using Delphi XE with 7 sample of Jar letters where it will be used in the training process. The process of detecting Jar patterns is using a method that will find the distance value from training and testing process on the Al-Quran image. Training process stage begins with the bitmap extension file of the original image,then change the size to gray scale level and convolution edge detection so that it will produce a vector value for each Jar pattern. Testing process will be use the Pierce Similarity Algorithm to measure the distance value of the Jar pattern to be recognized. The percentage of system detection results obtained, the pierce similarity method is able to recognize a Jar pattern of 60-80%.
Amalgamation of Machine Learning and Artificial Intelligence for Breast Cancer Detection

Breast cancer is the most communal form of cancer among women. It is becoming one of the major causes of female death around the world. The only key to reduce its morality rates is early detection of the disease. Mammography screening is considerable technique for the detection of breast cancer. But it is not always satisfactory at early stages. With the period of time, medical data expanded which can be molded to utilize as an alternative for the recognition of disease. Historical medical data can minimize morality rate by analyzing new correlation and pattern with the help of machine learning (ML). ML and artificial intelligence aided early detection of breast cancer and can be lifesaving option in the world of medical domain. In this chapter, role of ML as an effortless detection technique of breast cancer is presented. Several ML approaches such as logistic regression, support vector machine, decision tree, Naïve Bayes, neural network (NN), etc., are used for the classification of extracted features from the mammograms as benign or malignant tumors. Breast cancer detection systems constructed are further compared through several matrix and measures to attain best model. NN model has been observed to fabricate preeminent result and therefore considered as superlative model. It can be considered as imperative tool for detection and diagnosis of the disease in an efficient and fastest mode than other traditional approaches and can be obliging to doctors.
Surface Solar Radiation Modeling from Landsat 8 OLI/TIRS Satellite Data

At present, estimating surface solar radiation by exploiting satellite data is a very promising method. Indeed, satellite observations provide an alternative means to derive solar radiation due to their spatially continuous observations for large areas. In the present work, we propose a method to estimate solar radiation at earth's ground using Landsat 8 OLI/TIRS data. This method will eventually allow us to create a space maps of the solar radiation distribution received on ground. The chosen study area is Oran (35° 42'23"N, 0° 34'43"W) in Algeria.
The intertwining of reputation and sharing – The significance of standardization in preparing research data and the impact of project organization

Despite efforts to increase scientists’ willingness to share research data political stakeholders and funding agencies, there is still a discrepancy between scientists’ attitude toward data sharing and their actual practice. In a first step, this paper takes a close look at scientists’ definition of research data and the influence of project organization on scientist’ willingness to share data by analyzing interviews with scientists of three different disciplines. As the analysis shows, talking about “data sharing” should always happen in the context of data preparation and its various steps. Additionally, the influence of external factors such as a special form of project organization seems to be limited.
Privacy-Preserving Collaborative Deep Learning Using Verifiable Multi-Secret Sharing Scheme

Collaborative deep learning is an approach used to handle the amount of training data needed to build a better deep learning model. In collaborative deep learning, the central server collects user data and run the deep learning algorithm to get more accurate models. However, centralized training data collection can cause serious like privacy leakage problems and damage to the integrity of training data. In this paper, we introduce the privacy-preserving collaborative deep learning model using verifiable (k, t, n) multi-secret sharing based on the Elliptic Curve Diffie Hellman and SHA3-256 as a hash function. Where all training data will be formed into n shares using a session key generated from the private key and public key Elliptic Curve Diffie Hellman to protect the privacy and avoid all training data using SHA3-256 for the verification process before sending to the server. The test results show the integrity of damaged training data and colluding participants can be verified. In addition, the accuracy of the model produced using or without using Verifiable Multi-Secret Sharing Scheme has the same value. Therefore proposed model can protect the privacy and integrity of training data and maintain the accuracy of the deep learning model.
A Fast Trajectory Optimization of Pinpoint Landing by Chebyshev Pseudospectral and SOCP

In this paper, a fast trajectory optimization based on Chebyshev Pseudospectral and SOCP is proposed to solve a pinpoint landing problem with unknown terminal time, nonconvex control constraints and nonlinear dynamics. The rapidness of the solution is improved by reducing the number of decision variables and changing the way of determining the terminal time. The unknown terminal time, namely the time-of-flight, is considered as a control variable and solved directly, rather than determined by the golden section search. Chebyshev Pseudospectral is adopted to discretize the problem into a SOCP for fewer nodes and higher accuracy. Additionally, Lossless Convexification and Successive Linearization are applied to address the nonconvex constraints and nonlinear dynamics. Simulations demonstrate the superiority in efficiency and accuracy of the proposed method.
Securing Hill encrypted information With Audio steganography: a New Substitution Method

Abstract Securing data is an essential matter in communication systems, data must be protected from interceptors and eavesdroppers. In this paper, a hybrid system was produced to secure a plain text message encrypted with the Hill encryption method and embedded within an audio file in a random distribution using audio symbol sign to represent message bits. The audio file is restricted to be *.wav stereo file, Two secret keys are needed for this system encryption key and the seed of generating hiding positions. PSNR,MSE,SSIM are calculated between cover before and after embedding and the results reflected the imperceptibility requirement of the system. Also the elapsed time for securing messages was quite low for different sizes of text and the encryption process was less time than hiding.
Age Distribution Adjustments in Human Resource Department Using Shuffled Frog Leaping Algorithm

Shuffled frog leaping (SFL) algorithm is a recently introduced member of memetic algorithms family. It inherits the features of Particle Swarm Optimization and Shuffled Complex Evolution algorithms. Its intensification component of search is similar to Particle Swarm Optimization while the inspiration for diversification is inherited from the global exchange of information in Shuffled Complex Evolution. In this study SFL algorithm is implemented to a discrete problem of human resources distribution as per the present age group to the desired age group distribution. This problem is a challenging part of human resource planning in human resource department of an organization. The simulated results presents that SFL algorithm is able to find optimal adjustment magnitudes of the employees at the selected age groups. The results are also compared with Genetic algorithm.
Sabit Gecikmeler İçeren Nötral-Tip Hopfield Yapay Sinir Ağlarının Kararlılığı için Yeni Bir Kriter

This paper makes some contributions to the stability problem of neutral-type Hopfield neural network model having a constant time delay in states of neurons and a constant neutral delay in the time derivatives of states of neurons.With the help of a suitable Lyapunov functional, a novel stability criterion is derived for neutral-type Hopfield neural network model.This stability criterion only requires to check the positive defineteness of the matrices involving the system elements of this type of neural networks.The presented stability condition proved to be independently of these time and neutral delays.Therefore, this condition can be easily justified by applying the properties of some certain matrices.A numerical example for this type of neutral systems is studied to show the applicability of the presented stability result.
Research on the StarCraftll Decision Method Based on Hierarchical Reinforcement Learning

In order to solve the problem of "dimensional disaster" in traditional reinforcement learning algorithms when making decisions in a complex environment, this paper proposes a decision-making algorithm based on hierarchical reinforcement learning. Select the famous real-time strategy game (RTS) StarCraft II (SCII) as the research environment. The game contains a lot of strategies, these strategies affect the trend of the game, at the same time strategies change over time and as circumstances change. In order to solve the problem of decision-making difficulties caused by the huge strategy space and state space during the experiment, state space and strategy space are treated in different hierarchical manners by using hierachical reinforcement learning. It is verified by experiments that agents using hierarchical task structures have achieved good results in solving high-dimensional decision-making problems.
Human behavior recognition under occlusion based on two-stream network combined with BiLSTM

Human behavior recognition is an important subject in the field of computer vision, and it is a difficulty to recognize human behavior when the core part of human movement is blocked at some time of video. In order to solve the problem of occlusion in the process of human behavior recognition, this paper proposes a two-stream network structure incorporating bi-directional long and short time memory network (BiLSTM). A BiLSTM network with two branches is used to analyze the spatial feature information of each frame better. In order to make full use of the information of long distance optical flow and extract the variation information of sample characteristics on optical flow, a discriminant loss function of optical flow is proposed. The validity of the proposed method is verified by experimental comparison with other methods on the data set of occlusion behavior. The recognition accuracy of this method is up to 78.6%, and it has good robustness to the occlusion environment.
Intelligent Design of Computer Bridge

Computer games are divided into complete information games and incomplete information games. Bridge is a type of incomplete information games. Due to the limited information that each player knows, the existing methods for solving complete information games cannot be directly applied to the bridge. Therefore, improving the ability of computer bridge agents is of great significance to the research in the field of artificial intelligence. This paper proposes a new bridge game system design structure and applies an improved Monte Carlo sampling algorithm to the computer bridge. Aiming at the problem that the state space information in the incomplete information game is huge and the optimal card can not be found in a reasonable time, a filtering mechanism is proposed to filter the sampling. And, a bidding module is designed, which can handle the stages of response and call when bidding. The experiment results show that the performance of the computer bridge system in this paper has certain reference value for incomplete information games.
Linear Weighted Median Filtering for Stereo Disparity Refinement

To solve contradictions between the error disparities removal and the edges preserving in the disparity refinement step, a novel linear weighted median filter is proposed and applied to the disparity refinement for stereo algorithms. First, the classic average aggregation strategy in the guided image filter (GIF) is replaced by a weighted aggregation based on the mean of square error (MSE), and a novel edge-preserving filtering named WAGIF is proposed. The WAGIF achieves filtered images with sharper edges than those via GIF by applying weighted aggregation. Then, the proposed WAGIF is applied to design a weighted median filtering. With the assistant of the local histogram technology, the proposed weighted median filtering has a linear computational complexity. Furthermore, the experiments show that disparity errors and holes in disparity maps are removed significantly by the proposed refinement approach while edges are preserved well. The accuracy of the final refined disparity maps is improved significantly, even the proposed approach is applied to some classic stereo algorithms.
Fast Matching for Consecutive Frames of UAV Video

In the past few years, UAV has been more and more widely used in many fields. However, data of UAV video is too large to be processed easily. This paper proposes a novel method combined with PID prediction theory, which is in order to improve the processing speed of consecutive frame matching of UAV video. The experimental results indicate that the average matching time of our method is less than half of that of ORB algorithm. to the best of our knowledge, it is the first time to combine PID controller with image matching methods for UAV video, which we believe is an important attempt in practical applications.