Reviewerly/exHarmony huggingface dataset:
Enhancing SHADE and L-SHADE Algorithms Using Ordered Mutation

Differential Evolution (DE) algorithm is an efficient population-based metaheuristic algorithm which has shown satisfactory performance in solving complex real-world optimization problems. A Success-History Based Parameter Adaptation for Differential Evolution (SHADE) is a well-established variant of DE algorithm which employs a historical performance of the successful control parameters. L-SHADE algorithm extends SHADE with a linear population size reduction strategy. The SHADE and L-SHADE algorithms employ current-to-pbest/1 strategy for evolution, whilst the order of candidate solutions is not considered in their schemes. This paper proposes an ordering strategy for SHADE and L-SHADE algorithms which has shown a satisfactory influence on the performance of both algorithms. In the first direction, we propose current-to-3order/1 strategy for SHADE algorithm, which is based on ordering three candidate solutions. In the second direction, L-SHADE algorithm is improved based on ordering two candidate solutions, called current-to-pbest-2order/1. The proposed strategies can improve the performance of SHADE and L-SHADE algorithms without adding any extra significant computational cost. The proposed strategy is evaluated on CEC-2017 benchmark functions and with dimensions 30, 50, and 100. Our experimental results clearly verify the effectiveness of the proposed strategies.
Classification of Malayalam-English Mix-Code Comments using Current State of Art

Automatic classification of YouTube comments is a challenge especially when the comments are multilingual, as the messages are often rife with slang, symbols and abbreviations of the respective vernacular. In this work, we have evaluated top-performing classification models for classifying comments which are a mix of English and Malayalam. The statistical analysis of results indicates that XLM was the top-performing model with an accuracy of 67.31 %. Multi-layer Perceptron (MLP) with Term Frequency vectorizer produced the best results out of all the Deep Learning models with an accuracy of 65.98%. Random Forest with Term Frequency vectorizer was the top-performing model out of all the traditional classification models with an accuracy of 63.59%.
Optimal chiller loading for energy conservation using a hybrid whale optimization algorithm based on population membrane systems

(2021). Optimal chiller loading for energy conservation using a hybrid whale optimization algorithm based on population membrane systems. International Journal of Modelling and Simulation. Ahead of Print.
Device aware VOD Services with Bicubic Interpolation Algorithm on Cloud

Video streaming services are responsible for most of the global internet transit. Globally over 882 million users use video streaming services on various devices. Many service providers deliver their video contents through mobile phones nowadays because of its capability to handle media contents. While handling huge data, the limitations of hardware and software resources, are creating bottlenecks during the content delivery. Service providers following multiple ways to analyze the usage pattern of the client machines. While coming to the analysis client bandwidth and device specification plays a vital role among other parameters. Switching the video quality based on the user network will affect the user experience, but it is not possible to provide the fully compressed video for the users to compensate for the bandwidth fluctuation. The deliverable video contents should meet the minimum standards to satisfy the user. In our methodology the device specifications and interpolation algorithms are taken for analysis. The novel framework was tested with Google Compute Engine (GAE), it delivers smooth streaming services to the handheld devices within our parametric limits.
Quantifying the contributions technicians make to research

Abstract Technicians play a highly significant, but often invisible, role in research. Despite growing recognition of the importance of technicians, through initiatives such as the Technician’s Commitment, their contribution is inconsistently recognized across research groups, between disciplines, and within institutions. This article presents a simple framework, based on existing standards that can be easily integrated into common research workflows to capture technician contributions. By utilizing ORCID identifiers, Digital Object Identifiers, and the CRediT contributor role taxonomy, technician contribution can be quantified, recognized, and valuable additional data can be created for the planning and management of future research. Presented in the context of the Australasian higher education system, the framework is illustrated with a Science, Technology, Engineering and Mathematics focus but is applicable across all disciplines.
Prognostic approach to Class III malocclusion through case‐based reasoning

This investigation evaluates the evidence of case-based reasoning (CBR) in providing additional information on the prediction of future Class III craniofacial growth.The craniofacial characteristics of 104 untreated Class III subjects (7-17 years of age), monitored with two lateral cephalograms obtained during the growth process, were evaluated.Data were compared with the skeletal characteristics of subjects who showed a high degree of skeletal imbalance ('prototypes') obtained from a large data set of 1263 Class III cross-sectional subjects (7-17 years of age).The degree of similarity of longitudinal subjects with the most unbalanced prototypes allowed the identification of subjects who would develop a subsequent unfavourable skeletal growth (accuracy: 81%). The angle between the palatal plane and the sella-nasion line (PP-SN angle) and the Wits appraisal were two additional craniofacial features involved in the early prediction of the adverse progression of the Class III skeletal imbalance.Case-based reasoning methodology, which uses a personalized inference method, may bring additional information to approximate the skeletal progression of Class III malocclusion.
Accelerated WGAN update strategy with loss change rate balancing

Optimizing the discriminator in Generative Adversarial Networks (GANs) to completion in the inner training loop is computationally prohibitive, and on finite datasets would result in overfitting. To address this, a common update strategy is to alternate between k optimization steps for the discriminator D and one optimization step for the generator G. This strategy is repeated in various GAN algorithms where k is selected empirically. In this paper, we show that this update strategy is not optimal in terms of accuracy and convergence speed, and propose a new update strategy for networks with Wasserstein GAN (WGAN) group related loss functions (e.g. WGAN, WGAN-GP, Deblur GAN, and Super resolution GAN). The proposed update strategy is based on a loss change ratio comparison of G and D. We demonstrate that the proposed strategy improves both convergence speed and accuracy.
Interactive Learning of Mobile Robots Kinematics Using ARCore

Recent years have witnessed several educational innovations to provide effective and engaging classroom instruction with the integration of immersive interactions based on augmented reality and virtual reality (AR/VR). This paper outlines the development of an ARCore-based application (app) that can impart interactive experiences for hands-on learning in engineering laboratories. The ARCore technology enables a smartphone to sense its environment and detect horizontal and vertical surfaces, thus allowing the smartphone to estimate any position in its workspace. In this mobile app, with touch-based interaction and AR feedback, the user can interact with a wheeled mobile robot and reinforce the concepts of kinematics for a differential drive mobile robot. The user experience is evaluated and system performance is validated through a user study with participants. The assessment shows that the proposed AR interface for interacting with the experimental setup is intuitive, easy to use, exciting, and recommendable.
A Comparative Study of NoSQL Databases

The need and trend of data record analysis has seen an enormous rise in the past. More and more organizations are realizing the need for a schematic decision making procedure which makes them rely on past data to make future predictions. In this run, the data analysis techniques have also developed along with the advancement of data formats available and now trends are more towards NoSQL (Not Only SQL) type of data stores than the relational ones. This paper explores the types of NoSQL which offer high availability, performance, and eventual concurrency applications but losing the ACID properties of the traditional databases. The authors discuss various data stores in brief and also compare these data stores based on different aspects.
Effect of Quantum Noise on Deterministic Remote Preparation of an Arbitrary Two-Qubit State With a Relay Node

We propose a scheme for deterministic remote preparation of an arbitrary two-qubit state via a relay node. Afterwards, we investigate how the scheme is influenced by the noise type, the initial state to be remotely prepared, and the decoherence rate. We find that the fidelity is symmetrical with the line (λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b-f</sub> =λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p-f</sub> =0.5) in bit-flip and phase-flip channels. In addition, the fidelity decreases with the increase of decoherence rate in the amplitude-damping and phase-damping channels. We also compare it with other RSP schemes. Since only Bell pairs work as the quantum channels, it makes our RSP scheme more available and feasible.
Leveraging Auxiliary Tasks for Height and Weight Estimation by Multi Task Learning

Height and weight, two of the most important biological characteristics of human body, play crucial roles in physical condition estimation. Height and weight estimation with single face image via deep convolutional neural network suffers from poor performance due to lack of labeled data. To address this issue, inspired by the relevance of gender, age, height and weight, we propose an auxiliary-task learning framework, employing multiple relevant tasks to improve the performance of primary tasks. Specifically, gender prediction and age estimation are utilized as auxiliary tasks to assist primary tasks (i.e., height and weight estimation) learning via deep residual auxiliary block. Experiments are conducted on the public VIP-attributes datasets and our private VIPL-MumoFace- WH datasets. Our method outperforms the baseline methods of hard parameter sharing in multi-task learning, demonstrating the effectiveness of auxiliary-task learning framework for height and weight estimation.
Predictive ability of motion sickness susceptibility questionnaire for motion sickness individual difference in Chinese young males.

Motion sickness has great individual differences in susceptibility. The present study tried to investigate the predictive ability of motion sickness susceptibility questionnaire (MSSQ) for descriminating susceptible (SUS) and insusceptible (INSUS) subjects in two cohorts of young male volunteers who received real ship motion (n = 115) or laboratory vertical oscillation motion test (n = 77). Predictive ability of the MSSQ and its Nausea and Vomiting sub-scales (including child and adult part) was examined via analyzing area under the curve (AUC) of receiver operating characteristic (ROC) with motion sickness susceptibility judged by Graybiel score or nausea latency. MSSQ and its sub-scores positively correlated with the Graybiel score in both tests. The Nausea child -scores showed no predictive ability for the INSUS subjects, leading to lower AUC of the Nausea sub-total than the Vomiting sub-total and the MSSQ total scores for INSUS prediction in both the ship motion (0.587 vs. 0.647 and 0.644) and the vertical oscillation test (0.696 vs. 0.761 and 0.788) when seasickness susceptibility was defined by Graybiel score. Vomiting child scores seemed to be better than Nausea child scores for INSUS prediction in both tests. Lower AUC of the Nausea sub-total score than the MSSQ total score (0.691 vs. 0.758) was observed due to invalidity of Nausea child score when susceptibility was defined by nausea latency in the vertical oscillation test. The predictive ability for SUS did not differ among MSSQ sub-scores in both tests. Relative to the ship motion test, the vertical oscillation test showed higher predictive ability of the MSSQ total score for the Graybiel score-defined SUS subjects (0.840 vs. 0.686) as a result of higher AUC in both the Vomiting (0.840 vs. 0.690) and the Nausea sub-total scores (0.806 vs. 0.651). These results suggested that MSSQ had significant but limited predictive ability for seasickness susceptibility which varied with motion pattern, slightly differed among sub-scales, and could be decreased when assessing insusceptible subjects among Chinese young males.
Visual Model Fit Estimation in Scatterplots: Influence of Amount and Decentering of Noise

Scatterplots with a model enable visual estimation of model-data fit. In Experiment 1 (N = 62) we quantified the influence of noise-level on subjective misfit and found a negatively accelerated relationship. Experiment 2 showed that decentering of noise only mildly reduced fit ratings. The results have consequences for model-evaluation.
Building an Instructional Design Model for Immersive Virtual Reality Learning Environments

The recent increase in affordability of immersive virtual reality learning environments (IVRLEs) grows the interests of university, school, and industry training contexts. Due to their unique affordances, these environments have the potential to lead to a paradigm shift in learning experience design. The absence of learning design models, however, represents a significant challenge to the widespread and effective utilisation of this technology platform. Several researchers have proposed design models for virtual reality learning environments (VRLEs). The majority of these models don't explicitly consider the unique characteristics of Immersive environments. This research describes an instructional design model for IVRLEs, which draws upon Dalgarno and Lee's affordance model for VRLE and Tacgin's IVRLE development stages for teaching concepts and procedures. The specific learning strategies and affordances of IVRLEs are matched with the features of current IVR systems and technologies.
Design and implementation of CBIR system for academic/educational purposes

This paper reveals some of the most common methods for image analysis and data extraction used in Content Based Image Retrieval (CBIR) Systems and their usage for education. It gives information about implementation of those techniques in widespread applications used daily by million users. Based on these examples, a custom CBIR system has been developed for academic and educational purposes. It is used to illustrate how images could be represented by numbers and how they are processed and analyzed to achieve image recognition and classification.
Adaptive filter based on Monte Carlo method to improve the non-linear target tracking in the radar system

This article deals with the problem of degraded tracking performance of a high non-linear target in a radar system, well known by the divergence phenomenon. In our study, we aim to improve the target state estimation to imitate the tracking scenario as well as avoid the last cited undesirable phenomenon, generated during the non-linear measurements filtering, once using extended KALMAN filter. To overcome this issue, we have implemented a new approach based on the adaptive Monte Carlo (AMC) algorithm to replace the traditional method as is known by the extended KALMAN filter (EKF). The obtained experimental results showed a challenging remediation. Where, the AMC converges towards the accurate state estimation. Thus, more efficient than extended KALMAN filter. The experimental results prove that the designed system meets the objectives set for AMC referring to an experimental database obtained by a radar system, using MATLAB software development framework.
Cross Modal Person Re-identification with Visual-Textual Queries

Classical person re-identification approaches assume that a person of interest has appeared across different cameras and can be queried by one of the existing images. However, in real-world surveillance scenarios, frequently no visual information will be available about the queried person. In such scenarios, a natural language description of the person by a witness will provide the only source of information for retrieval. In this work, person re-identification using both vision and language information is addressed under all possible gallery and query scenarios. A two stream deep convolutional neural network framework supervised by identity based cross entropy loss is presented. Canonical Correlation Analysis is performed to enhance the correlation between the two modalities in a joint latent embedding space. To investigate the benefits of the proposed approach, a new testing protocol under a multi modal ReID setting is proposed for the test split of the CUHK-PEDES and CUHK-SYSU benchmarks. The experimental results verify that the learnt visual representations are more robust and perform 20% better during retrieval as compared to a single modality system.
Unsupervised Anomaly Detection on Temporal Multiway Data

Temporal anomaly detection looks for irregularities over space-time. Unsupervised temporal models employed thus far typically work on sequences of feature vectors, and much less on temporal multiway data. We focus our investigation on twoway data, in which a data matrix is observed at each time step. Leveraging recent advances in matrix-native recurrent neural networks, we investigated strategies for data arrangement and unsupervised training for temporal multiway anomaly detection. These include compressing-decompressing, encoding-predicting, and temporal data differencing. We conducted a comprehensive suite of experiments to evaluate model behaviors under various settings on synthetic data, moving digits, and ECG recordings. We found interesting phenomena not previously reported. These include the capacity of the compact matrix LSTM to compress noisy data near perfectly, making the strategy of compressingdecompressing data ill-suited for anomaly detection under the noise. Also, long sequence of vectors can be addressed directly by matrix models that allow very long context and multiple step prediction. Overall, the encoding-predicting strategy works very well for the matrix LSTMs in the conducted experiments, thanks to its compactness and better fit to the data dynamics.
Transfer Learning to improve Arabic handwriting text Recognition

In recent years, the leveraging of deep learning approaches allows a great progress in text recognition task. But they usually need a considerable amount of training examples to learn a new model. Therefore, lack of data can be an issue when developing a new recognition model, especially for handwriting Arabic text recognition where the lack of databases is stilling an interested problem. In this context, the main contributions of this paper is based on transfer learning the parameters learned with a bigger mixed-fonts printed Arabic text database to handwriting one. Experiments shows the good improvement provide with this technique.
Abstractive Summarization of Meeting Conversations

This paper contains the research work of encapsulating meetings by summarizing it which is within the style of dialogues. Throughout this process, the report will contain extractive information as an abstract of the text which is provided to the end-user. Abstractive and extractive methods are the foremost eminent techniques in text summarization. Extractive summarization is the most common summarization technique as it captures sentences based on either their frequency or using ranks associated with each of them and hence is unable to come up with grammatically correct sentences. The abstractive summarization may generate new vocabulary words apart from acquiring some extractive information from the text corpus. Here, we use the abstractive method of summarizing to train our model for dialogue systems.
Towards an Extended Team Model for Agile Development of Complex Products

In order to handle the increasing complexity in product development projects, agility has established as a promising solution approach. A large number of agile methods from software development have aroused great interests among industrial users. However, deviating conditions in the development of complex mechanical engineering products prevent simple adoption of these agile methods. Therefore, this paper aims to present a robust transdisciplinary understanding of agility for a complex product development. Using a systematic literature review, the paper derives central agile factors along different structural dimensions of an extended team model. In a final empirical evaluation, the derived agile factors are subjected to a practical assessment among development project managers. For further research, the results should allow the translation of the practically confirmed factors into measurable indicators of agility in product development.
Action Representation for Intelligent Agents Using Memory Nets

Memory Nets (Eggert et al.: Memory Nets: Knowledge Representation for Intelligent Agent Operations in Real World, 2019) are a knowledge representation schema targeted at autonomous Intelligent Agents (IAs) operating in real world. Memory Nets are targeted at leveraging the large body of openly available semantic information, and incrementally accumulating additional knowledge from situated interaction. Here we extend the Memory Net concepts by action representation. In the first part of this paper, we recap the basic domain independent features of Memory Nets and the relation to measurements and actuator capabilities as available by autonomous entities. In the second part we show how the action representation can be created using the concepts of Memory Nets and relate actions that are executable by an IA with tools, objects and the actor itself. Further, we show how action specific information can be extracted and inferred from the created graph. The combination of the two main parts provide an important step towards a knowledge base framework for researching how to create IAs that continuously expand their knowledge about the world.
Density operator formulation for magnetic systems: Physical and mathematical aspects

In this work, the density operator diagonal representation in the coherent states basis, known as the Glauber–Sudarshan-P representation, is used to study harmonic oscillator quantum systems and models of spinless electrons moving in a two-dimensional noncommutative space, subject to a magnetic field background coupled with a harmonic oscillator. Relevant statistical properties such as the Q-Husimi distribution and the Wehrl entropy are investigated.