Reviewerly/exHarmony huggingface dataset:
Before, Between, and After: Enriching Robot Communication Surrounding Collaborative Creative Activities

Research in creative robotics continues to expand across all creative domains, including art, music and language. Creative robots are primarily designed to be task specific, with limited research into the implications of their design outside their core task. In the case of a musical robot, this includes when a human sees and interacts with the robot before and after the performance, as well as in between pieces. These non-musical interaction tasks such as the presence of a robot during musical equipment set up, play a key role in the human perception of the robot however have received only limited attention. In this paper, we describe a new audio system using emotional musical prosody, designed to match the creative process of a musical robot for use before, between and after musical performances. Our generation system relies on the creation of a custom dataset for musical prosody. This system is designed foremost to operate in real time and allow rapid generation and dialogue exchange between human and robot. For this reason, the system combines symbolic deep learning through a Conditional Convolution Variational Auto-encoder, with an emotion-tagged audio sampler. We then compare this to a SOTA text-to-speech system in our robotic platform, Shimon the marimba player.We conducted a between-groups study with 100 participants watching a musician interact for 30 s with Shimon. We were able to increase user ratings for the key creativity metrics; novelty and coherence, while maintaining ratings for expressivity across each implementation. Our results also indicated that by communicating in a form that relates to the robot’s core functionality, we can raise likeability and perceived intelligence, while not altering animacy or anthropomorphism. These findings indicate the variation that can occur in the perception of a robot based on interactions surrounding a performance, such as initial meetings and spaces between pieces, in addition to the core creative algorithms.
Principles of effective functioning of training system of future teachers of natural science and mathematics for STEM technologies usage

The issue of the training of future teachers of natural science and mathematics to using STEM technologies was discussed on the basis of the system of scientific analysis. The article describes several severities of the process of training future teachers of natural science and mathematics and organization of making use of STEM technologies based on project priorities and application-oriented study methods, connected with modern technologies and also supplying them by social connection in a professional environment. The assembly of the principles of the effectiveness of the training system referred to above shall be defined and described in the following paragraphs.
Descriptive and Predictive Analysis of Aggregating Functions in Serverless Clouds: the Case of Video Streaming

Serverless clouds allocate multiple tasks (e.g., microservices) from multiple users on a shared pool of computing resources. This enables serverless cloud providers to reduce their resource usage by transparently aggregate similar tasks of a certain context (e.g., video processing) that share the whole or part of their computation. To this end, it is crucial to know the amount of time-saving achieved by aggregating the tasks. Lack of such knowledge can lead to uninformed merging and scheduling decisions that, in turn, can cause deadline violation of either the merged tasks or other following tasks. Accordingly, in this paper, we study the problem of estimating execution-time saving resulted from merging tasks with the example in the context of video processing. To learn the execution-time saving in different forms of merging, we first establish a set of benchmarking videos and examine a wide variety of video processing tasks-with and without merging in place. We observed that although merging can save up to 44% in the execution-time, the number of possible merging cases is intractable. Hence, in the second part, we leverage the benchmarking results and develop a method based on Gradient Boosting Decision Tree (GBDT) to estimate the time-saving for any given task merging case. Experimental results show that the method can estimate the time-saving with the error rate of 0.04, measured based on Root Mean Square Error (RMSE).
CatBoost Machine Learning Based Feature Selection for Age and Gender Recognition in Short Speech Utterances

Lately, with the rapid growth of various technologies, identifying the information of gender and age give short speech utterances has become a necessity for many applications in daily life like human-robot interaction, targeted marketing, identifying suspects in criminal cases, etc.Despite the comprehensive studies carried out to extract descriptive features, the recognition accuracy is still not satisfactory.In this study, an automatic system is proposed to classify age and gender in short speech utterances without depending on the text.Firstly, two groups of features are extracted from each utterance frame, followed by measuring 10 statistical functionals for each extracted feature dimension.After that, the extracted features dimensions are normalized using the Quantile technique.Then, the CatBoost machine is utilized as an important features detection to select the most discriminatory features for speaker age and gender recognition tasks.For classification purposes, the selected feature dimensions are fed into the Support Vector Machine (SVM).Experiments are conducted on the aGender data-set for measuring the suggested system's performance.The unweighted accuracies (UA) of the proposed system for gender, age, in addition to gender & age is 89.62%, 72.29%, and 71.96%, respectively.The achieved results outperform recent results on the same data-set.
A GGIW-PHD Filter for Multiple Non-Ellipsoidal Extended Targets Tracking With Varying Number of Sub-Objects

When the extension state of the non-ellipsoidal extended target (NET) changes, the performance of traditional multiple target tracking algorithms based on the constant number of sub-objects will decrease.To solve this problem, this paper proposes a gamma Gaussian inverse Wishart probability hypothesis density filter for non-ellipsoidal extended targets with varying number of sub-objects, called VN-NET-GGIW-PHD filter.In the proposed filter, each NET is considered as a combination of multiple spatially close sub-objects, and the label management is introduced to realize the association between the NET and corresponding sub-objects.Then, by target spawning and combination, the number of sub-objects for approximating the extension state of each NET can be adjusted automatically.Furthermore, to obtain the partition of the measurement set, an approach based on the clustering by fast search and find of density peaks (CFSFDP) algorithm and expectation maximization (EM) algorithm is proposed.Simulation results show that the proposed filter can adaptively adjust the number of sub-objects and has better performance when the extension state of the NET changes. INDEX TERMS Multi-target tracking
Precise Probability Estimation of Symbols in VVC CABAC Entropy Encoder

The new version of the Context-based Adaptive Binary Arithmetic Coding (CABAC) that is used in the latest Versatile Video Coding (VVC) technology is the state-of-the-art in the field of entropy compression of video data. This paper gives an in-depth analysis of possible, further extensions of the CABAC technique for even more accurate estimation of the probabilities of coded data symbols. The paper considers four author’s examples of such extensions. With respect to these examples, the paper shows the practical limitations of further increasing the efficiency of the newest version of CABAC used in VVC. Results show that for the considered methods of improvements the limit of gain in compression is up to 0.2073% on average, when compressing the VVC video data.
Based on Multimedia and Network Social Environment——A Study of Pragmatic Acquisition in College English Teaching

With the progress of the times, the number of college students is growing exponentially. But the students who enter the university are various and have their own advantages and disadvantages. Among them, the polarization of students' English level is particularly obvious and serious. Therefore, in view of the contradiction between "not enough to learn" and "unable to learn", this paper makes a preliminary exploration on the pragmatic acquisition mode of College English teaching based on multimedia and network social environment. This paper mainly discusses and analyzes the concept of pragmatic acquisition mode and about how to implement it in practical teaching. In addition, this paper is only for reference in the English teaching industry.
Novel Automated K-means++ Algorithm for Financial Data Sets

The K-means algorithm has been extensively investigated in the field of text clustering because of its linear time complexity and adaptation to sparse matrix data. However, it has two main problems, namely, the determination of the number of clusters and the location of the initial cluster centres. In this study, we propose an improved K-means++ algorithm based on the Davies-Bouldin index (DBI) and the largest sum of distance called the SDK-means++ algorithm. Firstly, we use the term frequency-inverse document frequency to represent the data set. Secondly, we measure the distance between objects by cosine similarity. Thirdly, the initial cluster centres are selected by comparing the distance to existing initial cluster centres and the maximum density. Fourthly, clustering results are obtained using the K-means++ method. Lastly, DBI is used to obtain optimal clustering results automatically. Experimental results on real bank transaction volume data sets show that the SDK-means++ algorithm is more effective and efficient than two other algorithms in organising large financial text data sets. The F-measure value of the proposed algorithm is 0.97. The running time of the SDK-means++ algorithm is reduced by 42.9% and 22.4% compared with that for K-means and K-means++ algorithms, respectively.
Efficient Approximate Minimum Entropy Coupling of Multiple Probability Distributions

Given a collection of probability distributions $p_{1},\ldots,p_{m}$, the minimum entropy coupling is the coupling $X_{1},\ldots,X_{m}$ ($X_{i}\sim p_{i}$) with the smallest entropy $H(X_{1},\ldots,X_{m})$. While this problem is known to be NP-hard, we present an efficient algorithm for computing a coupling with entropy within 2 bits from the optimal value. More precisely, we construct a coupling with entropy within 2 bits from the entropy of the greatest lower bound of $p_{1},\ldots,p_{m}$ with respect to majorization. This construction is also valid when the collection of distributions is infinite, and when the supports of the distributions are infinite. Potential applications of our results include random number generation, entropic causal inference, and functional representation of random variables.
A Novel Borda Count based Feature Ranking and Feature Fusion Strategy to Attain Effective Climatic Features for Rice Yield Prediction

An attempt has been made in the agricultural field to predict the effect of climatic variability based on rice crop production and climatic features of three coastal regions of Odisha, a state of India. The novelty of this work is Borda Count based fusion strategy on the ranked features obtained from various ranking methodologies. The proposed prediction model works in three phases; in the first phase, three feature ranking approaches such as; Random Forest, Support Vector Regression-Recursive Feature Elimination (SVR-RFE) and F-Test are applied individually on the two datasets of three coastal areas and features are ranked as per their algorithm. In the second phase; Borda Count as a fusion method has been implemented on those ranked features from the above phase to obtain the top five best features. The multi quadratic activation function based Extreme Learning Machine (ELM) has been used to predict the rice crop yield using those ranked features obtained from fusion-based raking strategy and the number of varying features are obtained which gives prediction accuracy above 99% in the third phase of experimentation. Finally, the statistical paired T-test has been used to evaluate and validate the significance of the proposed fusion based ranking prediction model. This prediction model not only predicts the rice yield per hector but also able to obtain the significant or most affecting features during Rabi and Kharif seasons. From the observations made during experimentation, it has been found that; relative humidity is playing a vital role along with the minimum and maximum temperature for rice crop yield during Rabi and Kharif seasons.
Robust Face Recognition Under Partial Occlusion Based on Local Generic Features

Face recognition has drawn significant attention due to its potential use in biometric authentication, surveillance, security, robotics, and so on. It is a challenging task in the field of computer vision. Although the various state-of-the-art methods of face recognition in constrained environments have achieved satisfactory results, there are still many issues which are untouched in unconstrained environments, such as partial occlusions, large pose variations, etc. In this paper, the authors have proposed an approach which utilized the local generic feature (LGF) to recognize the face in the partial occlusion by fusing features scale invariant feature transform (SIFT) and multi-block local binary pattern (MB-LBP). It also utilizes robust kernel method for classification of the query image. They have validated the effectiveness of the proposed approach on the benchmark AR face database. The experimental outcomes illustrate that the proposed approach outperformed the state-of-art methods for robust face recognition.
Community Opinion Sentiment Analysis on Social Media Using Naive Bayes Algorithm Methods

The election of Governor is an election event for the Regional Head for the future of the region and the country. The Central Java Governor election in 2018 was held jointly on 27 June 2018, which was followed by 2 candidate pairs of the governor. Its many responses from people through twitter's social media to bring up opinions from the public. Sentiment analysis of 2 research objects of Central Java Governor 2018 candidates with a total of 400 tweets with each candidate being 200 tweets. The used of tweets are divided into 3 classes: positive class, neutral class and negative class. In this study the classification process used the Naive Bayes Classifier (NBC) method, while for data preprocessing is using Cleansing, Punctuation Removal, Stopword Removal, and Tokenisation, to determine the sentiment class with the Lexicon Based method produces the highest accuracy in the Ganjar Pranowo dataset with an accuracy of 87,9545%, Precision value is 0.891%, Recall value is 0.88% and F-Measure is 0.851% while Sudirman Said dataset has an accuracy rate of 84.322%, Precision value of 0.867%, Recall value of 0.843% and F-Measure of 0.815%. From these results, we can conclude that the Ganjar Pranowo dataset was higher compared to Sudirman Said's dataset.
Occlusion‐invariant face recognition using simultaneous segmentation

When using convolutional neural network (CNN) models to extract features of an occluded face, the occluded part will inevitably be embedded into the representation just as with other facial regions. Existing methods deal with occluded face recognition either by augmenting the training dataset with synthesized occluded faces or by segmenting occlusions first and subsequently recognize the face based on unoccluded facial regions. Instead, simultaneous occlusion segmentation and face recognition is developed to make the most of these correlated two tasks. This is inspired by the phenomenon that features corrupted by occlusion are traceable within a CNN trained to segment occluded parts in face images. Specifically, a simultaneous occlusion invariant deep network (SOIDN) is proposed that contains simultaneously operating face recognition and occlusion segmentation networks coupled with an occlusion mask adaptor module as their bridge to learn occlusion invariant features. The training of SOIDN is jointly supervised by classification and segmentation losses aiming to obtain (1) occlusion invariant features, (2) occlusion segmentation, and (3) an occlusion feature mask that weighs the reliability of features. Experiments on synthesized occluded dataset (e.g. LFW-occ) and real occluded face dataset (e.g. AR) demonstrate that SOIDN outperforms state of the art methods for face verification and identification.
The Perception of Using Technology Canva Application as a Media for English Teacher Creating Media Virtual Teaching and English Learning in Loei Thailand

Online Learning requires teachers to think creatively and innovatively to attract students. Learning media becomes the main factor in helping teachers make it happen. This study aims to find out the perception of English teachers in the use of canva application as a virtual learning media creation in Loei Thailand.This research applies mixed method design that starts with collecting quantitative data. After analyzing quantitative data, qualitative data is then collected and analyzed. 20 teachers were asked to fill out a questionnaire to find out their perspective on the use of Canva.&#x0D; This research has proven that canva application can be used to create learning media. The result in this research was the function and benefit of canva divided into two, namely as sumplemen and subtitusi. The supplement function of canva is (1) Canva fasilitated teacher to make learning media, (2) Canva Application Improve online teaching process. The substitution function of canva is (1) Canva Application Improves learning media effectively, (2) Learning media of Canva is easy to distribute to students (3) Canva application can help the technology requirement. That is, the function of Canva obtained in this study.
Human Motion Tracking with Less Constraint of Initial Posture from a Single RGB-D Sensor

High-quality and complete human motion 4D reconstruction is of great significance for immersive VR and even human operation. However, it has inevitable self-scanning constraints, and tracking under monocular settings also has strict restrictions. In this paper, we propose a human motion capture system combined with human priors and performance capture that only uses a single RGB-D sensor. To break the self-scanning constraint, we generated a complete mesh only using the front view input to initialize the geometric capture. In order to construct a correct warping field, most previous methods initialize their systems in a strict way. To maintain high fidelity while increasing the easiness of the system, we updated the model while capturing motion. Additionally, we blended in human priors in order to improve the reliability of model warping. Extensive experiments demonstrated that our method can be used more comfortably while maintaining credible geometric warping and remaining free of self-scanning constraints.
Separation of geochemical anomalies from the background by using concentration-number fractal methods in the Veshnavah Area (South of Qom)

In this study, the concentration-number fractal method was used for regional exploration studies, and determining the anomalies of copper, lead and zinc elements. For this purpose, 800 samples of stream sediments were selected from the rivers in the area (i.e. from Kahak and Aran geological maps, 1: 100,000 sheets) and then the anomalies of these elements were mapped. The results show that strong copper anomalies are observed in the northern, central, southern and western parts of the area and the highest lead anomalies are located in the western part of the area. Strong anomalies of the zinc element are located in the central, southern and western parts of the region. These anomalies coincide with the lithological units of andesitic- basalt lava, volcanic breccia, tuffs, dacites, small scale masses of quartz -diorite, and small-scale masses of quartz-monzonite. The obtained map from combining anomalies and faults map reveals that the anomalies are mostly concentrated in fault zones and fault intersection points in the area and faults play a fundamental role in ore mineralization.
Decentralized, Self-Sovereign, Consortium: The Future of Digital Identity in Canada

This article introduces how SecureKey Technologies Inc. (SecureKey) worked with various network participants and innovation partners alongside government, corporate, and consumer-focused collaborators, in a consortium approach to create a mutually beneficial network of self-sovereign identity (SSI) principles with blockchain in Canada. These principles are based on giving users ownership and control over all of their digital identity attributes as an alternative approach to the current status quo of centralized digital identity, which focuses on discrete identities are made within individual online properties. Blockchain is used as the foundation for its strong security protocols to prevent information from being identified, accessed, or misused and uphold SSI principles. This article will consider the current status quo of digital identity known as centralized digital identity and comparisons to the case study’s emphasis on the alternative thinking of SSI with principles with blockchain, which prioritizes a decentralized, self-sovereign, consortium approach as opposed to discrete identities within individual online properties. Each of these principles will be explained in detail before highlighting the practical implications, lessons learned for future applications, and how both the Canadian and global identity landscapes should proceed for wider acceptance of SSI with blockchain. The case study detailed – that of Verified.Me – will demonstrate how blockchain developers can actively work to help partners transition from current identity silos to instead collaborate across varied industries and create a cohesive, secure service and digital identity network that benefits users through SSI principles and the benefits of blockchain. We also offer recommendations for how both the Canadian and global identity landscapes should proceed for wider acceptance of SSI with blockchain, the benefits of doing so, and anticipated barriers affecting the adoption of future decentralized identity initiatives.
Playing Quantum Nonlocal Games with Six Noisy Qubits on the Cloud

Abstract Nonlocal games are extensions of Bell inequalities, aimed at demonstrating quantum advantage. These games are well suited for noisy quantum computers because they only require the preparation of a shallow circuit, followed by the measurement of non‐commuting observable. Here, the minimal implementation of the nonlocal game proposed in Science 362, 308 (2018) is considered. This game is tested by preparing a 6‐qubit cluster state using quantum computers on the cloud by IBM, IonQ, and Honeywell. The present implementation includes several levels of optimization, such as circuit identities and error mitigation, and allows us to cross the classical threshold and demonstrate quantum advantage in one quantum computer. The quantum nature of the cluster state is further studied by a Bell inequality that allows us to observe quantum advantage in less accurate quantum computers, at the expense of probing a larger number of circuits.