Reviewerly/exHarmony huggingface dataset:
Mobile Robot Obstacle Avoidance System Based on GA-Aided OIF-Elman Network

Obstacle avoidance is one of the most basic and significant research directions of mobile robot. However, the present obstacle avoidance algorithms of many mobile robots are complex and unable to adapt to complex and changeable environments. This paper proposed an obstacle avoidance system based on GA-aided OIF-Elman neural network. The system can guide mobile robot to complete the movement and obstacle avoidance in the environment with obstacles. Based on the data collected by the robot’s six infrared sensors, the system adjusts their direction and changes the robot’s motion at the next moment. After data training, the information of network is saved as system file. Then put the system file in simulation software for test. The experimental results prove that the system designed by GA-aided OIF-Elman network is more effective for obstacle avoidance, compared with the performance of general Elman network, GA-aided OHF-Elman/Elman network.
Intelligent Music Abstraction Tool for improvising the Quality of Music Composition

All type of music pieces consist of two vital elements -rhythm structure and melody involvement.Another most important thing for automatic music improvisation is cadences that give symphonic shape to a music melody.Intelligent quality music composition is the relation of different musical elements and aggregation entity of these musical element objects.This paper introduces a new method for composing music using abstraction mechanism concept of software engineering.This paper focuses on the two main objects of music -Vocal and Instrumental and these two objects are tightly coupled and create the aggregation entity of Music Composition.The primary objective of this work is to explore the efficiency of Abstraction mechanism -Generalization, Specialization, and Aggregation to search for an optimum combination between vocal and different instrumentals with their different tempo to improve the quality of music and versatile intelligent music composition.
Pincello: An Affordable Electronics Kit for Prototyping Interactive Installations

Interactive artifacts and environments that involve more aspects of life other than work (e.g., social relations, entertainment, art etc.) are gaining more attention within the Human-Computer Interaction (HCI) field. This includes interactive art and installations. Do-It-Yourself (DIY) technologies, such as Arduinos with sensors and actuators, are often used in interactive installations. These technologies, however, are often oriented towards hobbyists and engineers. Moreover, easy-to-use commercial kits have a relatively high cost and a lack of flexibility. In this paper, we present Pincello: an affordable electronics kit for prototyping interactive installations. Pincello is not a commercial product, but rather a recommendation of components accompanied by meaningful hands-on documentation. We present and discuss the main components of the kit, including suggestions on how they can be used to allow different forms of interaction. We also present three case studies in which Pincello was used, involving 105 Human-Computer Interaction (HCI) students. Considering how the kit received positive feedback in the three case studies, and how it was successfully used to create 18 installations of varied themes, Pincello has shown to be a promising tool for the design and construction of interactive installations.
Prediction and Comparison using AdaBoost and ML Algorithms with Autistic Children Dataset

Autism Spectrum Disorder in children is a neural disorder behavior which could be detected and predicted using Machine Learning techniques.Autism in children is a kind of inability in socio-behavioral and in communicative behavior.If not detected between 20 to 60 months and treated, the treatment would be exceedingly difficult.The early the detection, early would be the treatment and therapy, which still would be challenging.Many different ML techniques are imposed but still its beneficial prediction is inadequate in predicting autism of small age groups.In this paper, we imposed three algorithms svm, random forest and AdaBoost algorithms to detect and predict autism in children.AdaBoost classifier is our proposed algorithm that combines weak classifiers to club and boost to strong classifier.For performance comparisons we calculate accuracy, precision, Fscore, and confusion matrix.Best accuracy yielded algorithm is implemented to predict autism in children.
Methodological Issues in Recommender Systems Research (Extended Abstract)

The development of continuously improved machine learning algorithms for personalized item ranking lies at the core of today's research in the area of recommender systems. Over the years, the research community has developed widely-agreed best practices for comparing algorithms and demonstrating progress with offline experiments. Unfortunately, we find this accepted research practice can easily lead to phantom progress due to the following reasons: limited reproducibility, comparison with complex but weak and non-optimized baseline algorithms, over-generalization from a small set of experimental configurations. To assess the extent of such problems, we analyzed 18 research papers published recently at top-ranked conferences. Only 7 were reproducible with reasonable effort, and 6 of them could often be outperformed by relatively simple heuristic methods, e.g., nearest neighbors. In this paper, we discuss these observations in detail, and reflect on the related fundamental problem of over-reliance on offline experiments in recommender systems research.
Single-Stage Refinement CNN for Depth Estimation in Monocular Images

Depth reconstruction from single monocular images has been a challenging task due to the complexity and the quantity of depth cues that images have. Convolutional Neural Networks (CNN) have been successfully used to reconstruct depth of general object scenes; however, proposed works use several stages of training which make this process more complex and time consuming. As we aim to build a computational efficient model, we focus on single-stage training CNN. In this paper, we propose five different models for solving this task, ranging from a simple convolutional network, to one with residual, convolutional, refinement and upsampling layers. We compare our models with the current state of the art in depth reconstruction and measure depth reconstruction error for different datasets (KITTI, NYU), obtaining improvements in both global and local error measures.
Calibration of a Pyranometer using Regression Analysis

Pyranometers are vital when it comes to solar power. Pyranometers are sensors that give us the reading for Irradiance. The main problem with many pyranometers is that they are inaccurate at certain times given that the calibration performed by the manufacturing companies will be done on a hardware basis but can improve the accuracy further on a software basis using soft computing techniques like regression. The growing energy deficit in the world has created a wanting for renewable energy sources like solar power and solar power has to be estimated accurately as it helps grid engineers make important decisions like voltage control and frequency control that are vital to the stable working of a solar grid but if pyranometers give us inaccurate readings for irradiance, solar power won't be estimated accurately so in this paper, regression model is proposed that has improved the accuracy of a particular pyranometer.
Rethinking Ethical Categories in the Age of Technology

Abstract Over time, ethical judgments evolve, but so do the phenomena they are applied to. For example, plagiarism is a modern concept. Before the early eighteenth century, works did not generally have references or acknowledgments, and ideas were freely exchanged. As writing became an occupation, copying others' words became “unethical.” As cut and paste, music mash‐up, and other technological forms of exchange make copying the works of others simple, the idea of plagiarism is eroding, and perhaps will eventually even be discarded. The same may be true with privacy. As with plagiarism, it was not really until the eighteenth century that our modern idea of privacy was established. To younger generations, raised on social media, online life is predicated on trading personal information for access. The undermining of former standards of privacy may suggest that privacy may also eventually become an outmoded concept .
Unsymmetric Image Encryption Using Lower-Upper Decomposition and Structured Phase Mask in the Fractional Fourier Domain

Background: An asymmetric cryptosystem using Structured Phase Mask (SPM) and Random Phase Mask (RPM) in fractional Fourier transform (FrFT) using lower-Upper decomposition with partial pivoting is proposed to provide extra security to the system. The usage of structured phase mask offers additional parameter in encryption. In the encoded process the phase-truncation (PT) part is replaced by the LUDP that is by the decomposition part. Objective: Introducing an asymmetric cryptosystem with LUDP is to avert quick identification of encrypted image in the FrFT domain. Method: Input image is firstly convoluted with SPM, in FrFT and in LUDP then the obtained result is convoluted with RPM in inverse FrFT and in LUDP. Finally, the encoded image is attained. Results: The strength and legitimacy of the proposed scheme have been verified by using numerical analysis on MATLAB R2018a (9.4.0.813654). For checking the viability of the proposed scheme mathematical simulations have been carried out which determines the performance and better quality of an image based on key sensitivity, occlusion attack, noise attacks and histograms. Conclusion: A novel asymmetric cryptosystem is proposed using two phase masks one is SPM and another is RPM. LUDP is proposed in which the encoded procedure is different from the decoded procedure. Security is enhanced by increasing the number of keys and the scheme is also robust against attacks. Statistical simulations are also carried out for inspection the strength and viability of the algorithm.
Research on Financial Aid Model of Colleges and Universities Based on Artificial Neural Network

Abstract There are some problems in the recognition of financial aid objects in Colleges and Universities, such as being interfered by human factors and weak in quantification. The existing relevant research is mainly about policies, systems and measures, while the application of artificial intelligence in precise financial aid in colleges and universities needs further research. And the new generation of AI technology in deep learning can automatically extract data characteristics, and it can be used in the accurate identification of poor students, with the advantages of having no need for human intervention. This model designs and optimizes the precise identification model of university funding objects based on artificial intelligence technology, and achieves a satisfactory recognition rate, which explores a new technical way to improve the equity of education funding and its efficiency exploration.
Authoring Interactions for Tangible Augmented Reality

Tangible Augmented Reality (TAR) has been explored for creating design representation for usability testing of industrial products. Currently, the process of authoring complex interactions for TAR entails scripting or low-level programming. A review of existing AR authoring tools showed that visual programming and authoring in the augmented view can potentially improve the efficiency of authoring interactions for TAR. This paper presents a review of the abilities and features provided by current AR authoring tools in order to identify requirements and opportunities necessary for non-programmers to author interactions for TAR.
An Application of Face Recognition Technology in University Classroom Teaching

In view of the problems of the classroom attendance system in the current college, such as time-consuming, error-reporting, and so on, through the face recognition technology, the face data of college students are trained, the model is established, and a college classroom intelligent attendance system is designed according to the classroom attendance function requirements. The resulting model is applied to the attendance system. Based on the Unity development environment, the model and attendance system were programmed and implemented, and applied in the classroom. The practice has proved that the system saves more time and makes more effort, and is more accurate and effective.
An Empirical Study on the Robustness of NAS based Architectures.

Most existing methods for Neural Architecture Search (NAS) focus on achieving state-of-the-art (SOTA) performance on standard datasets and do not explicitly search for adversarially robust models. In this work, we study the adversarial robustness of existing NAS architectures, comparing it with state-of-the-art handcrafted architectures, and provide reasons for why it is essential. We draw some key conclusions on the capacity of current NAS methods to tackle adversarial attacks through experiments on datasets of different sizes.
Immersive Virtual Reality App to Promote Healthy Eating in Children

An immersive virtual reality application was developed as a serious game to support children on their decisions about food, the system was composed by the Gear VR (Viewer and controller), and a phone which contains the mobile application developed in Unity, providing the immersive environment, the platform was based on a path where the player has to go through it choosing between different sort of meals and also between different physical activities, the effect of balanced diet plus adequate physical activity seems reflected on the avatar previously chosen by the user, the app was tested on 12 children between 8 and 10 years old during one week, children participated on the study after receiving an informed consent, analyzing the tendencies of food choice on children before and after, a notorious positive effect could be seen on the users according to the avatar analyzed at the end of the round, besides a System usability ore was also applied to evaluate the degree of usability of the app, reaching a ore of 88.33% which rates the app as very usable, this results were even better than what was planned at the beginning of the experience.
A sparse FFT approach for ODE with random coefficients

Abstract The paper presents a general strategy to solve ordinary differential equations (ODE), where some coefficient depend on the spatial variable and on additional random variables. The approach is based on the application of a recently developed dimension-incremental sparse fast Fourier transform. Since such algorithms require periodic signals, we discuss periodization strategies and associated necessary deperiodization modifications within the occurring solution steps. The computed approximate solutions of the ODE depend on the spatial variable and on the random variables as well. Certainly, one of the crucial challenges of the high-dimensional approximation process is to rate the influence of each variable on the solution as well as the determination of the relations and couplings within the set of variables. The suggested approach meets these challenges in a full automatic manner with reasonable computational costs, i.e., in contrast to already existing approaches, one does not need to seriously restrict the used set of ansatz functions in advance.
RETRACTED: A practical and efficient multi-assessment system for vocational teaching based on machine learning

Higher vocational education is a self-consistent system for higher education appropriate to the development of productivity and economy in the world. It aims at training skilled talents, which has made great contribution to the economy and industry. Generally, designing courses in high vocational education includes teaching analysis, teaching strategy, teaching practice and teaching assessment. Among the teaching steps, teaching assessment is one of the most important method to improve the quality of course teaching. However, in most high vocational education courses, traditional written exam is still the primary tools of assessments, which can not fulfill the development of high vocational education. In order to improve the quality of high vocational education, it is very urgent to design a practical and efficient system with multiple assessments. We exploit machine learning techniques to design assessment system for high vocation education. Machine learning is a very powerful tool for data analysis and it has been used for education tools in recent years. First, we improve the teaching organization for training skilled talents. Second, we propose a feature selection model based on the improved teaching organization. Third, we propose a machine learning model for teaching assessment. With the main contributions and other improvements, we design a multi-assessment system for vocational teaching based on machine learning. We implement the multi-assessment system by using Python and TensorFlow, which shows that the system can provide practical and efficient multiple assessments for vocational teaching based on training machine learning model. Compared with other assessment methods, machine learning based multi-assessment is more intelligent and automatic. Besides, it can be extended to other fields of education with slight modifications.
Information security issues in educational institutions

The information is currently constituted as an asset within the institutions, whether public or private, and with the technological advance the security of this information is put at risk, due to the set of threats that are presented on the Internet, many of the institutions have tools to prevent these risks. The present work is a systematic review of literature (SRL) on the problems that arise in information security in Higher Education also security measures used to protect it and the tools that are used to detect and battle informatic attacks. For the effect, 35 scientific articles from the last five years were investigated, analyzed and compiled, extracted from four most relevant libraries, being these: ScienceDirect, Taylor & Francis, Esmerald Group Publishing y AIS eLibrary. These scientific articles answer four questions posed within the bibliographic research that was carried out. With the results obtained, it was possible to identify the security issues, the policies and therefore the existing relationship of these factors with the organizational culture and, in that way safeguard the security of information in HEIs.
Aviation Terminal Data Security Architecture Based on Blockchain

Abstract With the advent of the 5G era, the number of UAV (Unmanned Aerial Vehicle) has grown dramatically. Accordingly, UAV need more computing and storage capabilities to complete a variety of tasks (including some collaborative tasks). To meet the requirements of UAV terminals, edge computing nodes and storage devices need to be deployed. But the problem of trust between edge computing nodes will lead to a series of data security problems. The blockchain features, which contain detrust, immutable, decentralized, can solve this problem properly. Therefore, this paper establishes an aviation terminal data security architecture based on the blockchain. Edge computing nodes and storage devices provide computing and storage resources for UAVs. As the core operating mechanism of the architecture, blockchain technology guarantees that the system has Byzantine fault tolerance performance through its consensus algorithm. The blockchain data structure under this architecture also guarantees that the data access records of all nodes will be written on the ledger and the ledger is stored by all miner nodes.
AI-Informed Analytics Cycle: Reinforcing Concepts

The implications of the decision-making process for analytics and artificial intelligence (AI) require further exploration of the process. The semantic network of concepts should facilitate the comprehension of the process, and, for the purposes of this chapter, helps us identify all pertinent elements that one has to consider for analytics, as will become clear later on. Information is not converted into knowledge in any computational tool. Humans, however, can absorb information and learn from it. This relationship between information and knowledge occurs within the human mind. AI is a field of study dedicated to the advancement of intelligent agents, which are those entities that exhibit rational behavior when making decisions through complex tasks such as planning, classification, recommendation, interpretation, prescription, and so on. Analytics explores several purposes of data motivated by queries in specific contexts. It is important thus to comprehend how queries, their purposes, and technical types can vary and evolve in certain business contexts.
Comparative Study of KNN, SVM and SR Classifiers in Recognizing Arabic Handwritten Characters Employing Feature Fusion

This paper evaluates and compares the performance of K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Sparse Representation Classifier (SRC) for recognition of isolated Arabic handwritten characters. The proposed framework converts the gray-scale character image to a binary image through Otsu thresholding, and size-normalizes the binary image for feature extraction. Next, we exploit image down-sampling and the histogram of image gradients as features for image classification and apply fusion (combination) of these features to improve the recognition accuracy. The performance of the proposed system is evaluated on Isolated Farsi/Arabic Handwritten Character Database (IFHCDB) – a large dataset containing gray scale character images. Experimental results reveal that the histogram of gradient consistently outperforms down-sampling based features, and the fusion of these two feature sets achieves the best performance. Likewise, SRC and SVM both outperform KNN, with the latter performing the best among the three. Finally, we achieved a commanding accuracy of 93.71% in character recognition with fusion of features classified by SVM, where 92.06% and 91.10% is achieved by SRC and KNN respectively.