Reviewerly/exHarmony huggingface dataset:
Enhancing differential evolution with a target vector replacement strategy

Individuals find it difficult to avoid stagnation in the iterative process of the differential evolution (DE) algorithm, and the stagnant individuals have restricted improvement in the population, which will have a negative effect on its performance. This research put forward a target vector replacement strategy (TVRS) for reducing the impact of stagnant individuals on the DE algorithm's performance. For stagnant target vectors, while executing mutation operation and crossover operation, TVRS selects non-stagnant individuals in the population to replace the stagnant target vectors with a specific probability. Because improving stagnant individuals are difficult, TVRS provides more opportunities for improvement to those who are not stagnant, while the opportunities for stagnant individuals to be improved are decreased. For assessing the efficiency of TVRS, TVRS was implemented to six DE algorithms and compared to their original algorithms. Judging from the performance outcomes of TVRS in the CEC 2014 benchmark test set, TVRS can greatly increase the DE algorithm's performance. This study will show the evolutionary opportunity that is obtained by an individual should be related to the individual state. Adjusting the individual evolutionary opportunity based on the individual state is helpful for improving the stagnation problem of the differential evolution algorithm. This study will provide a new way for improving the stagnation problem of the differential evolution algorithm and other evolutionary algorithms.
Pixel-wise triplet learning for enhancing boundary discrimination in medical image segmentation

In current state-of-the-art medical image segmentation methods, boundary details are typically enhanced by employing complex structures, which impose an additional computational burden for inference and cannot be embedded in the latest segmentation architectures as general-purpose boundary enhancers. This paper proposes a simple and flexible method, namely, pixel-wise triplet learning, for effectively improving boundary discrimination without imposing an additional computational burden. The method uses pixel-level triplet loss to enable segmentation models to learn more discriminative feature representations at boundaries, and it can be easily incorporated into the latest segmentation networks as a generic boundary booster and used for binary and multiclass medical segmentation tasks. Extensive experiments on nine medical datasets that cover five mainstream medical imaging modalities showed that the method is simple yet effective in achieving accurate boundary discrimination and high segmentation performance. • A pixel-wise triplet learning approach to enhance boundary discrimination. • An adaptive triplets selection algorithm for fast convergence. • Validity and efficiency of the method is shown on nine medical data sets.
Learning Color Representations for Low-Light Image Enhancement

Color conveys important information about the visible world. However, under low-light conditions, both pixel intensity, as well as true color distribution, can be significantly shifted. Moreover, most of such distortions are non-recoverable due to inverse problems. In the present study, we utilized recent advancements in learning-based methods for low-light image enhancement. However, while most "deep learning" methods aim to restore high-level and object-oriented visual information, we hypothesized that learning-based methods can also be used for restoring color-based information. To address this question, we propose a novel color representation learning method for low-light image enhancement. More specifically, we used a channel-aware residual network and a differentiable intensity histogram to capture color features. Experimental results using synthetic and natural datasets suggest that the proposed learning scheme achieves state-of-the-art performance. We conclude from our study that inter-channel dependency and color distribution matching are crucial factors for learning color representations under low-light conditions.
Association rule mining based fuzzy manta ray foraging optimization algorithm for frequent itemset generation from social media

Abstract Nowadays, the concept of data mining is employed widely and created a great deal of attention due to its fast arrival. Numerous approaches to frequent itemsets and association rule mining (ARM) are exemplified in recent years, but still, the performances based on scalability and processing time are considered as a major drawback that results in obtaining the solutions with very poor quality. To overcome such shortcomings, this article proposes three significant phases, namely, the data pre‐processing phase, data pre‐processing, frequent itemset mining, and ARM. In data pre‐processing phase, the collected twitter datasets are pre‐processed to eliminate redundant data and convert them into an appropriate format for further mining. In the frequent itemset mining phase, an Apriori algorithm is employed for the exact mining of frequent itemsets. The ARM phase utilizes the fuzzy manta ray foraging (FMRF) optimization algorithm that involves the generation of association rules from the huge itemsets thereby achieving minimum confidence and minimum support value. Here, the recent tweets regarding Covid‐19, trump2020, joebiden, draintheswamp, and Godzilla are the datasets collected from the Twitter web link. The experimental analysis and the comparative performances are performed for various simulation measures and the results reveal that the proposed approach provides effective performances when compared with various other existing approaches.
Machine learning to detect invalid text responses: Validation and comparison to existing detection methods

A crucial step in analysing text data is the detection and removal of invalid texts (e.g., texts with meaningless or irrelevant content). To date, research topics that rely heavily on analysis of text data, such as autobiographical memory, have lacked methods of detecting invalid texts that are both effective and practical. Although researchers have suggested many data quality indicators that might identify invalid responses (e.g., response time, character/word count), few of these methods have been empirically validated with text responses. In the current study, we propose and implement a supervised machine learning approach that can mimic the accuracy of human coding, but without the need to hand-code entire text datasets. Our approach (a) trains, validates, and tests on a subset of texts manually labelled as valid or invalid, (b) calculates performance metrics to help select the best model, and (c) predicts whether unlabelled texts are valid or invalid based on the text alone. Model validation and evaluation using autobiographical memory texts indicated that machine learning accurately detected invalid texts with performance near human coding, significantly outperforming existing data quality indicators. Our openly available code and instructions enable new methods of improving data quality for researchers using text as data.
A Hierarchical Motion Planning Framework for Autonomous Driving in Structured Highway Environments

This paper presents an efficient hierarchical motion planning framework with a long planning horizon for autonomous driving in structured environments. A 3D motion planning with time information is a non-convex problem because there exists more than one local minimum point and various constraints such as roads and obstacles. Accordingly, to deal with high computational complexity and the problem of falling into a local minimum in an enormous solution space, a decoupled method is utilized, that consists of two steps: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Long-term</i> planning and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">short-term</i> planning. First, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">long-term</i> planner provides reasonable far-sighted behavior through two processes. In the first process, a rough path that includes a driving strategy is generated in the 2D spatial space. Then, the jump point search algorithm is utilized with time information on the path to reduce the computational burden of A*, giving an acceptable quality of solution at the same time. In this step, a safe, comfortable, and dynamically feasible trajectory is generated. Next, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">short-term</i> planner optimizes a short-sighted trajectory using particle swarm optimization. In this method, a steering angle set is encoded as a particle, resulting in a safe, comfortable, and kinodynamically feasible trajectory. The proposed algorithm is implemented and evaluated in a range of vehicle-in-the-loop simulation scenarios, which include various virtual static and dynamic obstacles generated by Intelligent Driver Model. In the evaluation results, the proposed method reduced the computation time by up to 0.696 s with increasing the step cost by up to about 3%. The proposed algorithm is executed every 100 ms for a planning horizon of 10 seconds, and the average computation time is 31 ms with the worst-case computation time of 94 ms.
A novel <scp>COVID</scp>‐19 sentiment analysis in Turkish based on the combination of convolutional neural network and bidirectional long‐short term memory on Twitter

The whole world has been experiencing the COVID-19 pandemic since December 2019. During the pandemic, a new life has been started by necessity where people have extensively used social media to express their feelings, and find information. Twitter was used as the source of what people have shared regarding the COVID-19 pandemic. Sentiment analysis deals with the extraction of the sentiment of a given text. Most of the related works deal with sentiment analysis in English, while studies for Turkish sentiment analysis lack in the research field. To this end, a novel sentiment analysis model based on the combination of convolutional neural network and bidirectional long short-term memory was proposed in this study. The proposed deep neural network model was trained on the constructed Twitter dataset, which consists of 15k Turkish tweets regarding the COVID-19 pandemic, to classify a given tweet into three sentiment classes, namely, (i) positive , (ii) negative , and (iii) neutral . A set of experiments were conducted for the evaluation of the proposed model. According to the experimental result, the proposed model obtained an accuracy as high as 97.895% , which outperformed the state-of-the-art baseline models for sentiment analysis of tweets in Turkish.
A novel unsupervised multiple feature hashing for image retrieval and indexing (MFHIRI)

Recently, techniques that can automatically figure out the incisive information from gigantic visual databases are urging popularity. The existing multi-feature hashing method has achieved good results by fusing multiple features, but in processing these multi-features, fusing multi-features into one feature will cause the feature dimension to be very high, increasing the amount of calculation. On the one hand, it is not easy to discover the internal ties between different features. This paper proposes a novel unsupervised multiple feature hashing for image retrieval and indexing (MFHIRI) method to learn multiple views in a composite manner. The proposed scheme learns the binary codes of various information sources in a composite manner, and our scheme relies on weighted multiple information sources and improved KNN concept. In particular, here we adopt an adaptive weighing scheme to preserve the similarity and consistency among binary codes. Precisely, we follow the graph modeling theory to construct improved KNN concept, which further helps preserve different statistical properties of individual sources. The important aspect of improved KNN scheme is that we can find the neighbors of a data point by searching its neighbors' neighbors. During optimization, the sub-problems are solved in parallel which efficiently lowers down the computation cost. The proposed approach shows consistent performance over state-of-the-art (three single-view and eight multi-view approaches) on three broadly followed datasets viz. CIFAR-10, NUS-WIDE and Caltech-256.
High-Speed Post-Quantum Cryptoprocessor Based on RISC-V Architecture for IoT

Public-key plays a significant role in today’s communication over the network. However, current state-of-the-art public-key encryption (PKE) schemes are too complex to be efficiently employed in resource-constrained devices. Moreover, they are vulnerable to quantum attacks and soon will not have the required security. In the last decade, lattice-based cryptography has been a progenitor platform of the post-quantum cryptography (PQC) due to its lower complexity, which makes it more suitable for Internet of Things applications. In this article, we propose an efficient implementation of the binary learning with errors over ring (Ring-BinLWE) on the reduced instruction set computer-five (RISC-V) platform. Our field-programmable gate array (FPGA) implementations improve the speed of scheme operations by more than 51% in terms of CPU cycles compared to previous work. The proposed hardware module has low complexity and only imposes around 6%–9% overhead to the original core. Moreover, it has constant-time operations and is resistant to timing attacks. Besides, a more reliable fault-resilient variant of the architecture with 1% area overhead is proposed. According to the application-specific integrated circuits (ASICs) implementations, the proposed architecture achieves at least 32%, 79%, and 50% lower power, energy, and area consumption compared to the previous work, respectively.
A fuzzy partition-based method to classify social messages assessing their emotional relevance

With the surge of the large volume of data availability, Machine Learning and mainly Deep Learning techniques are the leading solutions in classification and predictive tasks, targeted at data-efficient learning. These models learn by training on many diversified samples in a process that is computationally expensive or time-consuming. Moreover, in many real-world scenarios, the amount of available data for training is unsuitable, because it is unlabeled or covers only portions of the whole reference domain cases. This paper proposes an alternative approach for document classification that leverages the distribution of the data projected in the multi-dimensional feature space to assess the weight of features in the final classification. The approach does not rely on traditional iterative methods for classification but builds a relevance measure to assess the relevance/importance of the features describing the domain of interest. The idea is to harness this metric to select relevant features and then express the values calculated by these metrics in natural language by exploiting fuzzy variables and linguistic labels to make human comprehension more immediate. The approach has been employed for emotion extraction from social media messages. The novelty of this approach is twofold: first, the well-known TF-IDF measure was reinterpreted as a relevance measure of emotions discovered in text content. Then, the discovered emotion relevance was described by fuzzy linguistic labels, defined on an ad-hoc-designed fuzzy partition, to express the data classification in natural language, more suitable to human understanding.
The effects of MALL training on preservice and in-service EFL teachers’ perceptions and use of mobile technology

Abstract Owing to the anytime-anywhere-anyhow nature of mobile learning, together with the ubiquity of affordably priced mobile phones, learning has become a mobigital practice, as termed by Şad and Göktaş (2014). Consequently, language teaching/learning is gradually shifting from computer-assisted language learning to mobile-assisted language learning (MALL). In response, the current study examined the impact of MALL training on preservice and in-service EFL teachers’ perceptions and use of mobile technology (MT). For this purpose, two groups of preservice ( N = 33) and in-service ( N = 31) EFL teachers were randomly selected and exposed to MALL training. The pretest-posttest experimental mixed-methods design was used as a framework for collecting and analyzing both quantitative and qualitative data (using closed- and open-ended-question surveys). Quantitative results revealed that both preservice and in-service teachers had similar perceptions of MT before and after training. The only exception is that, after training, in-service teachers were more interested in MT than preservice teachers. However, both groups demonstrated an overall (and subfactor) improvement in their perceptions after MT training, except for their perceived ease of use. In-service teachers’ use also improved after training and, due to the yielded positive correlation, their perceptions were a significant predictor of use. Qualitative findings showed that in-service teachers used MT more in listening and speaking (for synchronous communication) than in reading and writing, selecting social media and translation apps as the least useful ones. Moreover, they regarded technical and digital literacy problems as the ones most challenging to the use of MT.
Hybridization Gamified: A Mobile App for Learning About Hybridization

For years, hybridization in chemistry has been taught using static pictures and model kits. We decided to reimagine how students learn hybridization through the development of a mobile learning tool. The tool contains gamification features such as achievements and progressive leveling that keep students engaged, while the mobile platform allows students to study anywhere, anytime. A study conducted at the University of Arizona showed that playing the hybridization exercises increased academic performance, confidence, and engagement on the topic of hybridization. This work highlights the development and course implementation of a novel mobile hybridization learning tool.
Unsupervised Learning of Monocular Depth and Ego-Motion in Outdoor/Indoor Environments

Visual-based unsupervised learning <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> – <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> has emerged as a promising approach in estimating monocular depth and ego-motion, avoiding intensive efforts on collecting and labeling the ground truth. However, they are still restrained by the brightness constancy assumption among video sequences, especially susceptible with frequent illumination variations or nearby textureless surroundings in indoor environments. In this article, we selectively combine the complementary strength of visual and inertial measurements, i.e., videos extract static and distinct features while inertial readings depict scale-consistent and environment-agnostic movements, and propose a novel unsupervised learning framework to predict both monocular depth and ego-motion trajectory simultaneously. This challenging task is solved by learning both forward and backward inertial sequences to eliminate inevitable noises, and reweighting visual and inertial features via gated neural networks in various environments or with user-specific moving dynamics. In addition, we also employ structure cues to produce scene depths from a single image and explore structure consistency constraints to calibrate the depth estimates in indoor buildings. Experiments on the outdoor KITTI data set and our dedicated indoor prototype reveal that our approach consistently outperforms the state of the art on both depth and ego-motion estimates. To the best of our knowledge, this is the first work to fuse visual and inertial data without any supervision signals for monocular depth and ego-motion estimation, and our solution remain effective and robust even in textureless indoor scenarios.
Spatial changes of nutrients and metallic contaminants in topsoil with multi-geostatistical approaches in a large-size watershed

Appropriate assessment on concerned soil contaminants spatially is of importance for decision-makers and stakeholders to make efficient mitigation countermeasures. In this study, we applied multiple geostatistical approaches to explore soil nutrient and metallic contaminant distributions in a large river watershed in Thailand, and to compare their performances in predicting spatial distribution patterns of the concerned soil contaminants under suitable application scenarios. The total carbon, nitrogen and phosphorous in surface soils over the whole watershed were measured with their maximum concentrations up to 131.47, 9.24, 5.33 g·kg-1, respectively, while the concentrations of eight metallic elements (Cu, Zn, Pb, Cd, Hg, As, Cr, and Ni) were 933.00, 6862.50, 373.00, 6.22, 1.15, 178.53, 761.11, and 372.44 mg·kg-1, respectively. It was found that the conditional interpolation approaches such as land use stratified inverse distance weighted and land use stratified original kriging provided better boundary details than original interpolations, with substantially reduced root mean square errors (up to 28% for nutrients and 54% for specific metals) and mean relative errors (up to 38% for nutrients and specific metals respectively) in predicting the spatial patterns of soil nutrients and several land use specific metals (Cu, Zn, Cd, and Pb). The global accuracies were equivalent or higher than those of geographically weighted regression. Nonetheless, the prediction accuracy for Cr, Ni, As, and Hg could not be improved using the land use stratified interpolation because their sources and pathways were not significantly correlated with land use types in the watershed, as reflected by the results of analysis of variance with post hoc test (p ≤ 0.05) and principal component analysis.
Variable shearing holography with applications to phase imaging and metrology

We report variable shear interferometers employing liquid-crystal-based geometric-phase (GP) gratings. Conventional grating-based shear interferometers require two lateral shifts of the gratings to enable phase-shifting capabilities in x- and y- direction and two axial shifts of the gratings to adjust the shear amount in x- and y- direction, i.e., these systems need control of four axes mechanically. Here we show that the GP gratings combined with a pixelated polarization camera give instantaneous-phase shifting so that no mechanical movement is necessary to obtain phase shifts. Furthermore, we show that a single fixed shear with a rotational shear axis provides a more robust selection of shears while requiring the control of only one mechanical axis. We verify this statement using spatial domain and frequency domain criteria. We further show that the resolution of the reconstructed wavefield depends not only on the numerical aperture of the imaging system, the pixel size of the detector, or the spatial coherence of the source but also on the ability to determine the shear amount accurately. To improve this, we report a methodology to accurately detect the shear amounts using the second derivative of the autocorrelation function of the measured holograms, which further relaxes the requirements on mechanical stability.