Reviewerly/exHarmony huggingface dataset:
A Knowledge-based Model of Expert System to Assist Exclusive Breastfeeding Mother

Exclusive breastfeeding is crucial in the early newborn nutrition fulfillment with many benefits such as preventing mental and behavioral disorders, supporting brain development and intelligence, and providing more than 100 enzymes. In this study, we develop a knowledge-based model which can assist exclusive breastfeeding mother against clinical finding found during the first six months period. The model takes the information of experienced symptoms/conditions (in both the mother and the newborn), gives the calculation of certain clinical finding's risk factor based on Certainty Factor (CF) given, and gives recommendation of follow up to the user. Therefore, the newborn's mother can monitor their health condition and use this model as a precaution to get health assistance as soon as possible through health service providers in her surroundings. The knowledge base and its CF are built from domain experts knowledge acquisition and Indonesian related health literature. The model is evaluated based on 25 different random trials against experts' assessment. The precision from this evaluation reaches 88% by taking given symptom/condition only, which shows that the developed model performs very well in detecting clinical finding through CF-based risk factor calculation. However, in the end of this paper we also point out the way to increase higher precision to close to perfection by drilling the information of symptom/condition given.
Intelligent Pedagogic Agents (IPAs) in GEA2, an Educational Game to Teach STEM Topics

In this work we present an Intelligent Pedagogic Agent (IPA) we developed to act within the Gea2: A New Earth educational game. The IPA was designed and developed based on a pilot study, where information was gathered from students and teachers of four classes of two high school institutions. The IPA has the ability to be conversational in Natural Language, and is also capable of autonomous intervention through the use of unsolicited hints during learner’s gameplay. The autonomous intervention is based on both the detection of player’s emotions, and the state of player’s game.
Combining the Active Learning Algorithm Based on the Silhouette Coefficient with PCKmeans Algorithm

The paper discussed how to increase the effectiveness of semi-supervised clustering algorithm by integrating active learning and semi-supervised clustering to guide the model. To solve this complexity problem of the clustering algorithm, this paper presented an active semi- supervised k-means clustering model based on silhouette coefficient by utilizing a pair-wise constraint clustering method in PCKmeans and actively selects valuable samples to establish constraints. Our method was based on silhouette coefficient. The model was iterated until the number of queries reaches a threshold or the clustering algorithm achieves an acceptable performance. The method optimized the semi-supervised k means by using Local Sample Density (LDS) sampling strategy in order to ensure the stability of the algorithm. Furthermore, a distance-based sampling method, which reduced the queries quantity as well as increase the number of constraint samples, was introduced to optimize the process of establishing pair-wise constraints. These two methods significantly promoted the effectiveness of clustering algorithm. The experimental results indicated that our model outperforms the compared models in MI and ARI with 5% and 6% boost than k-means, PCKMeans, Min-Max, and LDS models.
FrogPlay as Gamification Tool in Motivating and Engaging School Students: An Analyses Review

In this 21 st century, the use of educational technology has been evolved and civilizing the educational sector.Hence, the Malaysia Ministry of Education (MoE) has been revamped educational curriculum in order to enrich and incorporate information communications and technology (ICT).When integrating ICT into teaching and learning sessions, proved through some educational technology research, it has shown an improvement in the motivation and engagement of students.Furthermore, the full extent capability of educational technology could not be optimized since integrating ICT in education blended with several problems such as misconception about technology use in teaching and learning amongst the teachers, difficulty in accessing to the technology itself and disengagement of students in the classroom during learning sessions.As an alternative to overcome difficulties integrating ICT in education, by integrating games and technology in education has produced offshoots such as game-based learning, serious games and more recently gamification.Gamification refers to the presence of gameful experience to existing systems (example: education) as opposed to creating entirely new or full-fledged games.Gamification paves the way for easier implementation as a tool in motivating and engaging school students.Therefore this paper will discuss the role of FrogPlay as gamification tool in motivating and engaging school students; based on analyses review of several kinds of literature.
Enhanced Processing of Input Data in Clustering Techniques of Data Mining Algorithms

Techniques of data mining and its applications have become significant in almost all domains. Each technique has its own significance in a given problem context. Likewise, clustering technique is in use to group elements with similar properties together. Objective of this research work is to apply multithreading approach on grouping the elements into various clusters. This multithreading technique determines the target cluster for multiple elements simultaneously. Thus, this approach improves overall performance of clustering technique which involves many iterations required for finding out target cluster. Implementation approach in this work involves, partitioning the input data elements and associating each of these partitions to multiple threads. Each thread is responsible for picking up the input element from the respective partition and determining the destination cluster. This implementation technique uses master–slave design. Master component makes various partitions of input data elements, creates instances of slave components, and invoke the services offered by slaves. Each slave runs in separate thread of computation and performs the task of determining target cluster of the given input element from the corresponding partition. Slaves will terminate its task once it finishes the determination of target cluster for the data elements present in the respective partition. We have seen that the above-designed technique does well when it comes to consumption of computational time. Partitioning of input data elements and number of data points in a partition is decided based on number of cores available for multithreading. It consumes less computational time as each partition is processed simultaneously on different available cores. Thereby, it eliminates the large computational time needed for existing techniques. We did an experiment sample dataset from the data mining open-source library source spfm. The size of the datasets we have used is closed to 50 MB. Our test environment created maximum of four simultaneous threads for the processing of entire input data points. When observed with the current approaches, experimental results tell that our approach provides a performance gain of 30% on an average.
Search Query Clustering Comparation On E-Commerce Using K-Means And Adaptive DBSCAN

Search queries on electronic commerce vary greatly depending on user behavior and preference. They are funneled through queries that imply buying interest. The accuracy of the search query clustering comparison can be enhanced through either k-Means or adaptive DBSCAN which had not been conducted by previous studies. Clustering helps to derive business knowledge models, especially taxonomic search features. This study used dataset which consisted of 2.074 records as a result of pre-processing, the accuracy was obtained 92.63% using adaptive DBSCAN and 91.75% using k-Means. The taxonomic results help to produce more informative query outputs that can be used to improve the appearance of electronic commerce and search features. Thus, increase of customers satisfaction and conversions can be achieved.
A Decision-making Model under the Assumption of Insufficient Knowledge and Resource

The classical decision theory and the Markov decision model are not proper models of decision-making process of people in a realistic environment. Although some scholars from the field of management have been trying to construct heuristic-primed decision-making theory inspired from cognitive psychology, they have not reflect the principle of the heuristics-primed decision process described in cognitive science. This paper proposes a decision-making model under the assumption of insufficient knowledge and resource, and the beliefs, desire and Intention of the model are handled by the Non-Axiom Logic, which is the logic part of the Non-Axiomatic Reasoning System, NARS. The model was used to represent a firefighting process of a firefighting commander, and the testing cases have been correctly processed. The proposed model will provide a solution for building an improvisional decision-making model in the real environment.
Private Information Retrieval from Non-Replicated Databases with Optimal Message Size

In this paper, we consider the private information retrieval problem over a storage system where the files are not replicated at all databases. This kind of storage system represents a non-replicated database structure and was first introduced by Banawan-Ulukus. Specifically, we assume that M out of K messages are stored across R databases. When R=M=2, the storage system can be modeled by a cyclic graph structure. For this case, we show that the message length of one bit is sufficient to achieve the capacity. Our achievable scheme uses the method of Tian-Sun-Chen where an asymmetric structure for answers from databases is proposed. A main characteristic of our proposed scheme is the fact that the query structure does not depend on the number of databases implying the scalability of the scheme. Finally, we discuss an example where our proposed scheme can achieve a larger retrieval rate than the one derived by Banawan-Ulukus.
Dynamic gesture recognition based on temporal modeling

In order to solve the problem of dynamic gesture recognition in traditional methods, a method of dynamic gesture recognition based on time series is proposed. The video is intercepted at several frames, the gesture information is segmented by modeling skin color, and a set of pictures corresponding to dynamic gestures are fused. The fused pictures contain time series information. Classify pictures based on convolutional neural network to realize the recognition of dynamic gestures. The experimental results show that this method has greatly improved compared with traditional recognition methods..
Establishment of Effectiveness Evaluation Indicator System based on 1D-CNN

Towards the decisive influence of substantial subjective factors in constructing effectiveness evaluation indicators, this article introduces a parallel one-dimensional convolutional neural network based on sample data. Then an objective method of constructing effectiveness evaluation indicator system is firstly put forward, including utilizing the multi-dimension convolution kernels to extract the raw data characteristics and concatenating a new indicator system. The validity and observability of the new indicator system based on sample data are verified by the simulation of indicator features extraction and effectiveness evaluation.
Measuring the Impact of Using Different Tools on Classification System Results

Abstract A huge amount of textual data is available on the web. These data need to be classified under labels or classes to make the search more efficient and easier. Achieved by using automatic classification is used for this task. Many factors impact on the performance of the classifier system, such as the amount of using dataset, the data dispersion degree, preprocessing tools, feature extraction methods, terms weighting, and data reduction. So, researchers constantly compete to build a robust classifier with good performance. This study focuses on the effect of using different tools in preprocessing and term weighting stages. The experimental results applied on two different languages (Arabic and English languages). Also, the experimental results were compared with the recent related works.
A Toolbox for Isophase-Curvature Guided Computation of Metrology Holograms

We describe the algorithmic foundations of an open-source numerical toolbox, written in the Octave language, for the creation of computer-generated binary and multi-level holograms used in interferometric form error measurements of complex aspheric and free-form precision surfaces and wavefronts. In a typical measurement setup for this type of surface, a hologram is used to generate a test wavefront that has the design shape of the surface, which is then compared to a fabricated part using an imaging laser interferometer. The optical function of the hologram in the measurement is generally modeled with optical ray-tracing software and it can be encapsulated by a scalar optical phase function φ : R2 →R. The toolbox converts phase functions into equivalent binary holograms that generate the desired test wavefronts for an interferometric form error measurement. The algorithms in this toolbox take advantage of the relationship between the local properties of phase functions and the local geometry (curvature) of isophase lines. It forms the core of an effcient algorithm for the computation of optical holograms. Holograms are created in a format that can be processed by most laser-or e-beam lithography systems. While the toolbox is chiefy aimed at the creation of hologram layouts needed for measurements of precision surfaces and wavefronts, we show that the isophase-following algorithm is easily extended to phase functions with singularities and discontinuities. Such phase functions result in holograms with zone bifurcation and they can be used to generate helical wavefronts. Light beams with helical wavefronts have applications beyond surface and wavefront metrology. The toolbox also includes a family of functions for the effcient estimation and evaluation of Zernike polynomials, which are widely used in optical applications.
Evaluation of climb stability of UAV based on AHP-Enteopy Weight method

The arrival of the "5G" era has greatly promoted the development of drones in the express delivery industry. Whether drones can be fully integrated into the express delivery industry is a matter of stability. Based on fuzzy mathematics theory, this paper mainly uses the fuzzy comprehensive evaluation method to propose a new, quantitative evaluation method for the stability of the UAV during the climb phase. In the process of fuzzy evaluation, the design of weights is an extremely important content. This paper combines the analytic hierarchy process with larger subiective components and the entropy weight method with larger objective components. The weight is realized by the minimum relative information entropy theory Design optimization. The experimental results show that the stability evaluation algorithm designed in this paper has high accuracy and is consistent with objective facts, which can play a more profound role in promoting the development of the express delivery industry.
A Texas Hold’em decision model based on Reinforcement Learning

Texas Hold 'em is a typical example of computer incomplete information game. The traditional machine learning method has been unable to deal with the huge search state space of Texas Hold 'em. In this paper, the value-based reinforcement learning algorithm is adopted, which can deal with the huge amount of data without manual extraction of data features, and can realize the unsupervised training of the model. However, the value based reinforcement learning algorithm has the problems of too complex acquisition process and over estimation of DQN algorithm. Therefore, this paper introduces DQN-S model, which combines reinforcement learning algorithm with Monte Carlo game search, and integrates Sarsa algorithm to solve the above problems to a certain extent. Finally, experimental data show that the over estimation effect of DQN algorithm is weakened to a certain extent after the Sarsa algorithm is incorporated. The average return of each game of DQN-S model and model is more than 5 chips, and the average return of each game of DQN-S model is more than 3 chips in the game with its strongest training version.
Research on Energy-saving Regulation Model of Climate Compensation for Central Heating Station Based on Artificial Neural Network

Heating plants are usually based on heating specifications, local climatic conditions, building structure types, actual conditions of central heating systems, meteorological data and heating operation experience in the past years. The central heating system is a complex multi-variable nonlinear strong coupling system. So far, there is no precise model to get each parameter. The empirical parameters have proven to be effective and reliable in practice, but they are not necessarily optimal and comprehensive, and they cannot be directly used by computer control systems. In recent years, many new algorithms for fitting nonlinear systems have emerged, among which artificial neural network algorithm is one of the most successful algorithms. One of the essences of neural network algorithms is machine learning-the network is trained through continuous and extensive use of historical observation data and even empirical data to make the model optimally approximate the real model. Therefore, this paper analyzes the principles and objectives of heat exchange station regulation, clarifies the control parameters, and focuses on the control model based on neural network to identify the heat output of the heat exchange station and the outdoor atmospheric temperature changes. The advantages and disadvantages of the BP neural network (BPNN), general regression neural network (GRNN) and radial basis neural network (RBF) models were compared. Finally, the correctness of the model was verified with the actual meteorological data and heating data of Karamay in province Xinjiang using Matlab.
Overview of Face Attribute Generation

Face attribute generation is one of the important directions of current deep learning research, and it has applications in various fields. By generative adversarial networks, encoder-decoders, and other network models, we edit the attributes that do not exist on the face to generate the attributes we want. Research in this direction has enriched the development of the computer vision field, especially in terms of image diversification. This paper analyzes the problems of face attribute generation in generative adversarial networks and the corresponding advantages and disadvantages and development prospects of several current effective network models. By discussing the improvement schemes of each algorithm, the generated images are more real and clear, which provides a reference for future research.
Using Modified Conjugate Gradient Method to Improve SCA

Abstract This research is to improve Sine - Cosine Algorithm (SCA) that is like any other intelligent techniques that encounter some problem such as slow convergence and the dropping in local solution. To overcome these problems. SCA has been developed and improved through three directions, First: Hybrid of SCA with Modified conjugate gradient method (MCG) that has improved through that derivation of parameter of new conjugate factor (β new ) and attest its characteristic such as descent and global to construct improve algorithm called SCA-MCG. The second direction was a hybrid of SCA with classic optimization methods such as conjugate gradient (CG) algorithm to construct SCA-CG, and parallel Tangent (PT) algorithm to construct SCA-PT. Third combining both previous methods, using the Hybrid value with SCA to construct SCA-CG-PT Algorithm of high quality accounts in all directions mentioned above. To improve the initial population which randomly generated by using excellent characteristics of MCG-CG-PT as well as using this improvement as initial population for SCA. Numerical results have proved the efficiency of improved Algorithm and the results was excellent if we compared with SCA. In addition, we got optimum global values for most functions by achieving functions minimum.
Design and Implementation of a Novel Hybrid Rental Apartment Recommender System

Recommender Systems (RSs) have become an essential part of most e-commerce sites nowadays. Though there are several studies conducted on RSs, a hybrid recommender system for the real state search engine to find appropriate rental apartment taking users preferences into account is still due. To address this problem, a hybrid recommender system is proposed in this paper constructed by two of the most popular recommendation approaches — Collaborative Filtering (CF), Content-Based Recommender (CBR). CF-based methods use the ratings given to items by users as the sole source of information for learning to make a recommendation. However, these ratings are often very sparse in applications like a search engine, causing CF-based methods to degrade accuracy and performance. To reduce this sparsity problem in the CF method, the Cosine Similarity Score (CSS) between the user and predicted apartment, based on their Feature Vectors (FV) from the CBR module is utilized. Improved and optimized Singular Value Decomposition (SVD) with Bias-Matrix Factorization (MF) of the CF model and CSS with FV of CBR constructs this hybrid recommender. The proposed recommender was evaluated using the Statistical Cross-Validation consisting of Leave-One-Out Validation (LOOCV). Experimental results show that it significantly outperformed a benchmark random recommender in terms of precision and recall. In addition, a graphical analysis of the relationships between the accuracy and error minimization is presented to provide further evidence for the potentiality of this hybrid recommender system in this area.
Reconfigurable Pinart to help the Blinds Visualization

We developed a reconfigurable pinart model that can transform its topographical surface which is in three-dimension from a stereographic image captured from a webcam. The model relies on a pneumatic pump and solenoid valves to excite each pin stroke via our custom multiplex control system. The travel displacement for each pin stroke is ten centimeters. The reconfiguration time is less than a few minutes with our demonstration reconfigurable pinart prototype of one hundred pixels.
Efﬁcient Stereo Matching Approach Using Pixel Selection Patterns in Stereo Vision

Stereo vision ﬁnds a wide range of applications in areas of computer and machines, such as providing three dimensional perceptions, imitating humans. The crucial correspondence problem faced by most visual systems can be resolved by Stereo matching algorithms to a great extent. Broadly, the matching algorithms are categorized as area based and feature based. Area Based technique involves correlation based matching which is relatively fast and provides a dense disparity map. In this paper, some signiﬁcant techniques involved in area based matching are discussed. This paper describes the computational optimization strategy, which is based on a very effective pixel selection scheme. Normalized Cross Correlation (NCC) has the advantages of accuracy, strong robustness, but speed is its weakness, so online application is restricted. This method uses novel pixel selection schemes where disparity map produced using NCC is modiﬁed in such a way that provides accurate and computationally efﬁcient results. Finally, we provide experimental results obtained by the proposed methods on variable window sizes; we compare these data with those obtained using a well-known, fast, area-based algorithm SAD. The results show that the proposed method is computationally faster than the conventional NCC scheme.