Reviewerly/exHarmony huggingface dataset:
An “Outside the Box” Solution for Imbalanced Data Classification

A common problem of the real-world data sets is the class imbalance, which can significantly affect the classification abilities of classifiers.Numerous methods have been proposed to cope with this problem; however, even state-of-the-art methods offer a limited improvement (if any) for data sets with critically under-represented minority classes.For such problematic cases, an ''outside the box'' solution is required.Therefore, we propose a novel technique, called enrichment, which uses the information (observations) from the external data set(s).We present three approaches to implement the enrichment technique: (1) selecting observations randomly, (2) iteratively choosing observations that improve the classification result, (3) adding observations that help the classifier to determine the border between classes better.We then thoroughly analyze developed solutions on ten real-world data sets to experimentally validate their usefulness.On average, our best approach improves the classification quality by 27%, and in the best case, by outstanding 66%.We also compare our technique with the state-of-the-art methods.We find that our technique surpasses the existing methods performing, on average, 21% better.The advantage is especially noticeable for the smallest data sets, for which existing methods failed, while our solutions achieved the best results.Additionally, the enrichment technique applies to both the multi-class and binary classification tasks.It can also be combined with other techniques dealing with the class imbalance problem.
Time Series Forecasting of Bitcoin Price Based on Autoregressive Integrated Moving Average and Machine Learning Approaches

Bitcoin as the current leader in cryptocurrencies is a new asset class receiving significant attention in the financial and investment community and presents an interesting time series prediction problem. In this paper, some forecasting models based on classical like ARIMA and machine learning approaches including Kriging, Artificial Neural Network (ANN), Bayesian method, Support Vector Machine (SVM) and Random Forest (RF) are proposed and analyzed for modelling and forecasting the Bitcoin price. While some of the proposed models are univariate, the other models are multivariate and as a result, the maximum, minimum and the opening daily price of Bitcoin are also used in these models. The proposed models are applied on the Bitcoin price from December 18, 2019 to March 1, 2020 and their performances are compared in terms of the performance measures of RMSE and MAPE by Diebold-Mariano statistical test. Based on RMSE and MAPE measures, the results show that SVM provides the best performance among all the models. In addition, ARIMA and Bayesian approaches outperform other univariate models where they provide smaller values for RMSE and MAPE.
Survey of Knowledge Graph Approaches and Applications

With the advent of the era of big data, knowledge engineering has received extensive attention. How to extract useful knowledge from massive data is the key to big data analysis. Knowledge graph technology is an important part of artificial intelligence, which provides a method to extract structured knowledge from massive texts and images, and has broad application prospects. The knowledge base with semantic processing capability and open interconnection ability can be used to generate application value in intelligent information services such as intelligent search, intelligent question answering and personalized recommendation. Although knowledge graph has been applied to various systems, the basic theory and application technology still need further research. On the basis of comprehensively expounding the definition and architecture of knowledge graph, this paper reviews the key technologies of knowledge graph construction, including the research progress of four core technologies such as knowledge extraction technology, knowledge representation technology, knowledge fusion technology and knowledge reasoning technology, as well as some typical applications. Finally, the future development direction and challenges of the knowledge graph are prospected.
Fast Single Image Haze Removal Method for Inhomogeneous Environment Using Variable Scattering Coefficient

The images capture in a bad environment usually loses its fidelity and contrast. As the light rays travel towards its destination they get scattered several times due to the tiny particles of fog and pollutants in the environment, therefore the energy gets lost due to multiple scattering till it arrives its destination, and this degrades the images. So the images taken in bad weather appear in bad quality. Therefore, single image haze removal is quite a bit tough task. Significant research has been done in the haze removal algorithm but in all the techniques, the coefficient of scattering is taken as a constant according to the homogeneous atmosphere but in real time this does not happen. Therefore, this paper introduces a simple and efficient method so that the scattering coefficient becomes variable according to the inhomogeneous environment. Then, this research aims to remove the haze with the help of a fast and effective algorithm i.e., Prior Color Fading, according to the inhomogeneous environmental properties. Thereby, to filter the depth map, the authors used a weighted guided image filtering which removes the drawbacks of guided image filter. Afterwards the scattering coefficient is made variable according to the inhomogeneous atmosphere and then the Simple Color Balance Algorithm is applied so that the readability property of images can be increased. The proposed method tested on various general outdoor images and synthetic hazy images and analyzed on various parameters Mean Square Error (MSE), Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Mean Structural Similarity (MSSIM) and the Universal Objective Quality Index (UQI). Experimental results for the proposed method show that the proposed approach provides better results as compared to the state-of-the-art haze removal algorithms.
Does Determination of Initial Cluster Centroids Improve the Performance of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>K</mml:mi></mml:math>-Means Clustering Algorithm? Comparison of Three Hybrid Methods by Genetic Algorithm, Minimum Spanning Tree, and Hierarchical Clustering in an Applied Study

Random selection of initial centroids (centers) for clusters is a fundamental defect in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>K</mml:mi></mml:math>-means clustering algorithm as the algorithm’s performance depends on initial centroids and may end up in local optimizations. Various hybrid methods have been introduced to resolve this defect in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>K</mml:mi></mml:math>-means clustering algorithm. As regards, there are no comparative studies comparing these methods in various aspects, the present paper compared three hybrid methods with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mi>K</mml:mi></mml:math>-means clustering algorithm using concepts of genetic algorithm, minimum spanning tree, and hierarchical clustering method. Although these three hybrid methods have received more attention in previous researches, fewer studies have compared their results. Hence, seven quantitative datasets with different characteristics in terms of sample size, number of features, and number of different classes are utilized in present study. Eleven indices of external and internal evaluating index were also considered for comparing the methods. Data indicated that the hybrid methods resulted in higher convergence rate in obtaining the final solution than the ordinary <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mi>K</mml:mi></mml:math>-means method. Furthermore, the hybrid method with hierarchical clustering algorithm converges to the optimal solution with less iteration than the other two hybrid methods. However, hybrid methods with minimal spanning trees and genetic algorithms may not always or often be more effective than the ordinary <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M6"><mml:mi>K</mml:mi></mml:math>-means method. Therefore, despite the computational complexity, these three hybrid methods have not led to much improvement in the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M7"><mml:mi>K</mml:mi></mml:math>-means method. However, a simulation study is required to compare the methods and complete the conclusion.
Motion Planning of Self-driving Vehicles for Motion Sickness Minimisation

Self-driving vehicles are expected to push towards the evolution of mobility environment in the near future. However, motion sickness has been proven to be one of the main limitations to the successful introduction of fully automated vehicles. Recently, research into motion sickness on autonomous vehicles has just started to get attention. In particular, recent researches have focused on providing solutions to mitigate motion sickness with ideas from the field of human factors and ergonomics, interior design and automotive engineering. However, limited or no representative work has been done in motion planning in terms of motion sickness minimisation. In this respect,this paper presents the application of motion planning in order to minimize motion sickness in self-driving vehicles. By formulating an optimal control problem, the optimum velocity profile is sought for a predefined road path from a specific starting point to a final one. Specific and given boundaries and constrains are applied in order to minimize the motion sickness without compromising the journey time. F or the representation of the motion sickness as a cost function to our optimal control problem, the illness rating is used. Different case studies are investigated by changing the cost functions of our problem as well as varying the lateral manoeuvrability of the vehicle. According to the results, the road with flexible lateral manoeuvrability is found to provide lower sickness as well as shorter journey time when both motion sickness and journey time are consider in the cost function.
Sustainability Efficiency Challenges of Modern IT Architectures – A Quality Model for Serverless Energy Footprint

Cloud technology is transforming IT architecture. Serverless is a new paradigm driven by cloud technology that offers chances for scalable and reliable IT services. All bigger cloud service providers (CSP) offer a set of serverless services to build entire business processes on it. The services are mostly based on efficient containerization of the workload. However, the service offers are driven by parameters like scaling, availability, ease of use and costs. So far, no cloud provider has been providing information about service efficiency, in particular energy efficiency. However, breaking down complex services into micro, nano or pico services as serverless comes with an energy overhead. This energy footprint of serverless architectures demands a specific quality model to refine the ISO 25010 for the serverless domain. The model presented in this article is provided as a self-service to the Volkswagen Group IT to enable all architects and software engineers to evaluate the trade-offs with respect to sustainability aspects.
Effectiveness of Android-Based Mathematics Learning Media Application on Student Learning Achievement

Abstract This study aims to implement android-based learning media in mathematics basic concepts courses on student achievement, and the effectiveness of android-based learning media in mathematics basic concepts courses on student achievement at PGMI Nurul Jadid University. This research uses a quantitative research approach. The data collection is done through documentation and tests. The determination of the sample of this research was carried out by random sampling technique which included the fourth-semester PGMI Study Program students. While the data analysis in this study uses paired sample t-test analysis techniques. Based on the results of data analysis, it can be concluded that t-count = -18,378 with significance = 0,000 &lt; 0.01. Thus, it can be stated that H1 is accepted and H0 is rejected, so it can be concluded that the android-based learning media on the subject of mathematics basic concepts are convincingly effective in improving student achievement.
Scope of Sentiment Analysis on News Articles Regarding Stock Market and GDP in Struggling Economic Condition

The present paper studies the scope of sentiment analysis on news articles on impact of economic downfall of the primary international stock markets, comparing it with that of previous terrorist attacks or epidemic (9/11,.For properly embedding our investigation in the theoretical outline, first we present an overview of existing studies focusing on the consequence of terrorism on economic markets.The practical part is mainly in terms of measuring whether the return of the New York Stock Market's main index due to the attacks vary statistically regarding the deviations of the four months before and after the attack.We study the internet data, contrasting the direct impact of news spread through news media affected the index of the New York stock market comparing with gold and crude oil price.The result analysis discusses interpretative hypotheses involving a behavior evolution of the stock markets as a result of the terrorist attacks or epidemic which find out the scope of more future works with sentiment analysis on news data in stock market.
Education Systems and Technology in 1990, 2020, and Beyond

In Restructuring Education Through Technology, I incorporated systems thinking to identify seven types of relationships in educational systems: teacher-student, student-content, student-context, teacher-content, teacher-context, content-context, and education system-environment relationships (Frick 1991). I now revisit these education system relations and discuss potential futures of education. The World Wide Web did not exist when I wrote the original treatise, nor did wireless smartphones and tablets, Google’s search engine, YouTube, Facebook, or Wikipedia. However, one important education system relationship should not change: the affective bonding between teachers and their students.
Learning Automata with Side-Effects

Automata learning has been successfully applied in the verification of hardware and software. The size of the automaton model learned is a bottleneck for scalability, and hence optimizations that enable learning of compact representations are important. This paper exploits monads, both as a mathematical structure and a programming construct, to design and prove correct a wide class of such optimizations. Monads enable the development of a new learning algorithm and correctness proofs, building upon a general framework for automata learning based on category theory. The new algorithm is parametric on a monad, which provides a rich algebraic structure to capture non-determinism and other side-effects. We show that this allows us to uniformly capture existing algorithms, develop new ones, and add optimizations.
Development of a Privacy-By-Design Speech Assistant Providing Nutrient Information for German Seniors

Elderly people could take benefit of speech assistants since they provide the most natural way to interact with assistive technology. Nevertheless, current speech assistants are mainly based on cloud-systems which are non-functional without a stable internet connection or introduce severe privacy-concerns. In this work we provide an overview of state-of-the-art available components for developing a privacy-by-design, open source speech assistant for seniors, as well as a fully functional implementation. We chose the health related task of getting nutrition information of packaged foodstuff for which we rely on an open data database. Our prototype was used in a workshop with two German seniors to gather further insights into the special requirements of elderly people, which can be taken into account for future speech assistants.
A proposed hybrid cryptography algorithm based on GOST and salsa (20)

Security concepts are frequently used interchangeably. These concepts are interrelated and share similar objectives for the protection of privacy, credibility, and access to information; however, there are some slight differences between them. Such variations lie mostly in the subject matter approach, the approaches used, and the focus fields. With the intention of protecting data in contradiction of unauthorized or unintentional disclosure, cryptography is used during transit (electronic or physical) and when data is stored. In the course of the past few years, some block ciphers and stream ciphers have been proposed. These block ciphers take encryption method that uses Substitution-Permutation and Feistel network structure while stream ciphers choose a onetime method. GOST encryption is based on the confidentiality of the secret key. However, it leads to the same ciphertext being generated when the encryption program is used with the same key for the plain text. Reproduction of messages can thus easily be identified by an opponent that is a weak link in any communication. In this paper, proposed a hybrid encryption method based on GOST block cipher and Salsa stream cipher to provide proper security with as high hardness randomly enhances the five standard tests and modifies key schedule as secure operations. The downside of the GOST algorithm is a simple key schedule so that in certain circumstances be the weak point of the method of cryptanalysis as related-key cryptanalysis. However, this resolved by the proposed method by passing the keys of GOST to Salsa stream to have the right combination and more robustness security. Its need for 2256 probable keys to breaking keys that, because of its uncomfortable procedure in this situation, is to be not used brute force attack. Correspondingly, five standard tests successfully surpassed the randomness of a proposed method.
RGSR: A two-step lossy JPG image super-resolution based on noise reduction

Single Image Super-Resolution (SISR) is a fundamental and important low-level computer vision (CV) task, yet its performance on real-world applications is not always satisfactory. Different from the previous SISR research, we focus on a specific but realistic SR issue: How can we obtain satisfied SR results from compressed JPG (C-JPG) images, which is a ubiquitous image format to greatly release storage space while missing fine details. the JPG SR task is deeply analyzed to discover the connotation. Then, we propose an effective two-step model structure named RGSR, involving two specifically designed components, i.e., JPG recovering and SR generation, instead of the perspective of noise elimination in traditional SR approaches. Besides, we further integrate the cycle loss to build a hybrid objective across scales for better SR generation. Experimental results on both of the standard test data sets and real images show that our approach achieves outstanding results and succeed in applying to practical C-JPG SR tasks.
Shape-reserved stereo matching with segment-based cost aggregation and dual-path refinement

Abstract Stereo matching is one of the most important topics in computer vision and aims at generating precise depth maps for various applications. The major challenge of stereo matching is to suppress inevitable errors occurring in smooth, occluded, and discontinuous regions. In this paper, the proposed stereo matching system uses segment-based superpixels and matching cost. After determination of edge and smooth regions and selection of matching cost, we suggest the segment-based adaptive support weights in cost aggregation instead of color similarity and spatial proximity only. The proposed dual-path depth refinements use the cross-based support region by referring texture features to correct the inaccurate disparities with iterative procedures to improve the depth maps for shape reserving. Specially for leftmost and rightmost regions, the segment-based refinement can greatly improve the mismatched disparity holes. The experimental results show that the proposed system can achieve higher accurate depth maps than the conventional methods.
Visual Analysis of Contact Patterns in School Environments

Information Visualisation strategies can be applied in a variety of domains.In the context of temporal networks, i.e., networks in which interactions between individuals occur throughout time, efforts have been conducted to develop visual approaches that allow finding interaction patterns, anomalies, and other behaviours not previously perceived in the data.This paper presents two case studies involving real-world education networks from a primary school and a high school.For this purpose, we used the Massive Sequence View (MSV) layout with the Community-based Node Ordering (CNO) method, two well established approaches for visual analysis of temporal networks.Our results show that the identified patterns involving students/students and students/ teachers represent important information to benefit and support decision making about school management and teaching strategies, especially those related to strategic group formation.
AutoRec: An Automated Recommender System

Realistic recommender systems are often required to adapt to ever-changing data and tasks or to explore different models systematically. To address the need, we present AutoRec 1 2, an open-source automated machine learning (AutoML) platform extended from the TensorFlow [3] ecosystem and, to our knowledge, the first framework to leverage AutoML for model search and hyperparameter tuning in deep recommendation models. AutoRec also supports a highly flexible pipeline that accommodates both sparse and dense inputs, rating prediction and click-through rate (CTR) prediction tasks, and an array of recommendation models. Lastly, AutoRec provides a simple, user-friendly API. Experiments conducted on the benchmark datasets reveal AutoRec is reliable and can identify models which resemble the best model without prior knowledge.
A Convolutional Neural Network Accelerator Architecture with Fine-Granular Mixed Precision Configurability

Convolutional neural networks (CNNs) have been widely deployed in deep learning applications, especially on power hungry GP-GPUs. Recent efforts in designing CNN accelerators are considered as a promising alternative to achieve higher energy efficiency. Unfortunately, with the growing complexity of CNN, the demanded computational and storage resources for accelerators keep increasing, hindering its wider applications in mobile devices. On the other hand, many quantization algorithms have been proposed for efficient CNN training, which brings many small or zero weights. This is a unique opportunity for accelerator designers to employ much fewer bits, e.g., 4 bits, in both arithmetic core and storage, thereby saving significant design cost. However, such a single precision strategy inevitably compromises the accuracy as some key operations may demand a higher precision. Thus, this paper proposes a low power CNN accelerator architecture that can simultaneously conduct computations with mixed precisions and assign the appropriate arithmetic cores to operation with different precision demands. This proposed architecture can achieve significant area and energy savings, without accuracy compromise. The experimental results show that the proposed architecture implemented on FPGA can reduces almost half of the weight storage and MAC area, and lower the dynamic power by 12.1% when compared with a state-of-the-art CNN accelerator design.