Reviewerly/exHarmony huggingface dataset:
Emojis as heuristic cues: The multifaceted role of emojis in online service interactions

Abstract Recent technological advances have allowed businesses to adopt emojis when interacting with consumers. To gain in‐depth theoretical and managerial insight into this trend, five pre‐registered studies (1 field observation and four controlled experiments) indicate that emojis in digital communication work as heuristic cues that might have a differential effect depending on elaboration likelihood and outcome valence. Drawing on the Heuristic Information Processing and elaboration likelihood model, this research reveals that emojis can systematically influence consumers' elaboration. Findings indicate that low elaboration in positive encounters results in a positive heuristic cue boost (emojis improve customer evaluation). In turn, high elaboration on negative service outcomes makes the heuristic content relevant, such that positive (vs. negative) emojis will bring attention to and reinforce the interaction's positive (vs. negative) aspects. This research contributes to emerging studies on the role of emojis in digital communication.
Recognition of Fiber Optic Vibration Signals Based on Laplace Wavelet Transform and Deep Learning

Abstract Convolutional neural networks possess the capability of feature learning and nonlinear mapping, which has significant advantages in classifying and recognizing optical fiber vibration signals. In order to further enhance the recognition rate of vibration signals, this paper combines wavelet transform with convolutional neural networks and designs a convolutional layer based on parameterized wavelets. In this layer, the initial signal is convolved with parameterized Laplace wavelet dictionaries to complete the wavelet transform. Such customized filters make more sense than filters with randomly initialized parameters for traditional CNNs. Simultaneously, we introduce the channel attention mechanism to enhance the features of the filtered signals. Subsequently, standard CNNs are employed to extract and process signal features, ultimately utilizing a multi‐layer perceptron for recognition and classification. The experimental results show that the network model possesses better recognition refinement. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
Ambiguity Tests, Polysemy, and Copredication

A family of familiar linguistic tests purport to help identify when a term is ambiguous. These tests are philosophically important: a familiar philosophical strategy is to claim that some phenomenon is disunified and its accompanying term is ambiguous. The tests have been used to evaluate disunification proposals about causation, pain, and knowledge, among others. These ambiguity tests, however, have come under fire. It has been alleged that the tests fail for polysemy: a common type of ambiguity, and one that is at issue in philosophically interesting cases. Furthermore, the objection that the tests fail for polysemy is often taken to be an undeniable bit of linguistic data. We argue that this is mistaken. The objection implicitly relies on controversial assumptions about how to account for copredicational sentences, in which a single argument is ascribed prima facie incompatible properties. Furthermore, on several viable theories of copredication, the objection fails. However, our discussion also reveals that even if ambiguity tests are preserved, they may be significantly harder to execute than previously thought.
Formulation of Energy Bars by using Sunflower Seeds

The study discusses the popularity and nutritional benefits of snack foods, with a focus on granola bars as a versatile and nutrient-dense option. The importance of energy bars for athletes and individuals with busy lifestyles is highlighted. The study aims to develop a high-protein, high-energy nutritional bar using plant sources. Oats are emphasized for their rich nutrient profile, including carbohydrates, fiber, proteins and various bioactive compounds. Dates and almonds contribute additional nutrients and health benefits. Sunflower seeds are recognized for their therapeutic benefits, while honey is for its antibacterial and nutritional properties. The energy bars, including a basic version and two variations, were analyzed for various nutritional parameters. The moisture content exhibited slight increments, measuring from 6.83±0.01, to 7.03±0.01, while the ash content showed minimal differences at 0.05±0.01. Energy content increased from 380±0.032 to 410±0.026, and protein content ranged from 9.17±0.01 to 9.19±0.01. Fat composition showed marginal variations while the fiber content was measured at 6.67±0.01, 6.77±0.01 and 6.78±0.01 grams. Ascorbic acid concentrations increased from 0.7±0.015 to 0.81±0.0026, while thiamine and riboflavin concentrations showed slight fluctuations. Sensory evaluation revealed that T2 (30% honey) scored highest in various categories, including color (8.5±0.527), taste (8.6±0.516), texture (8.2±0.788), flavor (8.5±0.707), appearance (8.5±0.527) and overall acceptability (8.6±0.516). T2 emerged as the most preferred, followed by T1 (25% honey) and T3 (35% honey).
A Study on the Visual Recognition Patterns of Japanese Beginner Learners Regarding Hangul Characters: Focusing on Consonant Letters

Objectives The purpose of this study is to examine the visual recognition of Hangul consonants among Japanese beginner learners using data from a visual recognition survey. It aims to identify the consonants that learners find visually challenging to distinguish and to provide educational implications for the instruction of Hangul characters at the introductory level.&#x0D; Methods An online survey using Google Forms was conducted among 129 Japanese beginner learners studying Korean as an elective liberal arts course at universities in Japan. This survey involved two rounds of visual recog-nition tests on 19 consonant characters. The tests were employing a form constancy testing method where partic-ipants were required to identify Hangul characters that matched the form of the given examples.&#x0D; Results In comparison to the first survey, the average score of the second survey increased by 0.39 points, but the difference between the average scores of the first and second surveys was not statistically significant. It was also confirmed that the order of learning Hangul characters and their frequency of appearance did not have a sig-nificant effect on the visual recognition of character forms. In the error analysis of Japanese beginner learners, er-rors due to native language, target language, and curriculum were found, and some errors due to native language and target language were interpreted as errors due to the influence of font styles.&#x0D; Conclusions Japanese beginner learners encounter difficulties in the visual recognition of Hangul characters dur-ing the Korean language learning process. Exposure to these characters in textbooks is insufficient for accurate recognition of their forms, necessitating continuous character form education. It is particularly noted that the de-tailed features of Hangul characters and variations in font styles are significant factors for Japanese learners. This approach is anticipated to serve as foundational data for enhancing understanding of Hangul character learning and for developing effective teaching methods.
A bibliometric analysis of metaverse technologies in healthcare services

Abstract A bibliometric analysis was conducted to examine healthcare metaverse research using 3721 Web of Science publications covering the period 1994–2022. Using bibliometric and bibliographic link network analyses, this study aimed to uncover emerging trends, country and university contributions, author collaborations, and common metaverse themes. Contrary to expectations, inconsistent correlations between publication volume and citation impact emerged. This pioneering research evaluated empirical bibliometric laws, identified influential entities, and provided a structured thematic categorization for metaverse studies. Serving as the first comprehensive investigation of the healthcare metaverse, this study lays a solid foundation for subsequent research efforts and guides further scholarly research.
Enhancement of Seismic Image Based on Novel Approach with Custom U-Net Architecture

Abstract This paper introduces an innovative approach for enhancing the vertical resolution of seismic images, augmenting conventional signal processing with deep learning algorithms and modeling techniques. The algorithm outlined in this study is specifically designed to be applicable across diverse datasets, operating independently of well data. It effectively addresses noise reduction, distinguishing geological features from disturbances and by improving the signal-to-noise ratio, and implements enhancements to the frequency spectrum through a nonlinear approach. The research endeavors to create an unconventional methodology, focusing on vertical resolution improvement. Notably, the effectiveness of this approach is demonstrated using real seismic data collected from the South Caspian Basin. Detailed results are presented, underscoring the potential to significantly enhance the accuracy and reliability of seismic exploration procedures in the realm of geophysics.
ANALISIS SENTIMEN VAKSIN COVID-19 PADA TWITTER MENGGUNAKAN RECURRENT NEURAL NETWORK (RNN) DENGAN ALGORITMA LONG SHORT-TERM MEMORY (LSTM)

The Coronavirus, also known as the Covid-19 pandemic, has reached every country worldwide, including Indonesia. Covid-19 is still prevalent and has killed many people in Indonesia. This makes it impossible to stop Covid-19 from spreading. The government's attempt to stop the Covid-19 pandemic is acquiring the vaccine. The administration of the Covid-19 vaccine has generated much discussion on social media, particularly Twitter. Tweets displaying public opinion on Twitter can be used for sentiment analysis and categorizing public opinion on the Covid-19 vaccine. 20,000 tweets were collected by Twitter crawling between January 10 and January 15, 2022. 3.290 tweets were left after pre-processing and meaningless tweets were eliminated. The data were processed using the Recurrent Neural Network method with the Long Short-Term Memory algorithm to determine its accuracy and identify topics often discussed by the public on Twitter. The LSTM method is capable of storing old information/data. A model with 70% training data, a learning rate of 0.01, 100 LSTM units, 32 batch sizes, 100 epochs, a cross-entropy loss function, and Adam optimizers was used to build the classification in this study. The accuracy value obtained from the performance evaluation of the Long Short-Term Memory model research was 80.34%.
Earthquake monitoring using deep learning with a case study of the Kahramanmaras Turkey earthquake aftershock sequence

Abstract. Seismic phase picking and magnitude estimation are fundamental aspects of earthquake monitoring and seismic event analysis. Accurate phase picking allows for precise characterization of seismic wave arrivals, contributing to a better understanding of earthquake events. Likewise, accurate magnitude estimation provides crucial information about an earthquake's size and potential impact. Together, these components enhance our ability to monitor seismic activity effectively. In this study, we explore the application of deep-learning techniques for earthquake detection and magnitude estimation using continuous seismic recordings. Our approach introduces DynaPicker, which leverages dynamic convolutional neural networks to detect seismic body-wave phases in continuous seismic data. We demonstrate the effectiveness of DynaPicker using various open-source seismic datasets, including both window-format and continuous recordings. We evaluate its performance in seismic phase identification and arrival-time picking, as well as its robustness in classifying seismic phases using low-magnitude seismic data in the presence of noise. Furthermore, we integrate the phase arrival-time information into a previously published deep-learning model for magnitude estimation. We apply this workflow to continuous recordings of aftershock sequences following the Turkey earthquake. The results of this case study showcase the reliability of our approach in earthquake detection, phase picking, and magnitude estimation, contributing valuable insights to seismic event analysis.
All-optical image denoising using a diffractive visual processor

Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images - implemented at the speed of light propagation within a thin diffractive visual processor that axially spans <250 × λ, where λ is the wavelength of light. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30-40%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays.
A circuit area optimization of MK-3 S-box

Abstract In MILCOM 2015, Kelly et al. proposed the authentication encryption algorithm MK-3, which applied the 16-bit S-box. This paper aims to implement the 16-bit S-box with less circuit area. First, we classified the irreducible polynomials over $$\mathbb {F}_{2^n}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>n</mml:mi> </mml:msup> </mml:msub> </mml:math> into three kinds. Then we compared the logic gates required for multiplication over the finite field constructed by the three types of irreducible polynomials. According to the comparison result, we constructed the composite fields, $$\mathbb {F}_{(2^4)^2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>4</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:msub> </mml:math> and $$\mathbb {F}_{(2^8)^2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>8</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:msub> </mml:math> . Based on the isomorphism of finite fields, the operations over $$\mathbb {F}_{2^{16}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>16</mml:mn> </mml:msup> </mml:msub> </mml:math> can be conducted over $$\mathbb {F}_{(2^8)^2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>8</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:msub> </mml:math> . Similarly, elements over $$\mathbb {F}_{2^8}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>8</mml:mn> </mml:msup> </mml:msub> </mml:math> can be mapped to the corresponding elements over $$\mathbb {F}_{(2^4)^2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>4</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:msub> </mml:math> . Next, the SAT solver was used to optimize the operations over smaller field $$\mathbb {F}_{2^4}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>4</mml:mn> </mml:msup> </mml:msub> </mml:math> . At last, the architecture of the optimized MK-3 S-box was worked out. Compared with the implementation proposed by the original designer, the circuit area of the MK-3 S-box in this paper is reduced by at least 55.9%.
A multi-classification detection model for imbalanced data in NIDS based on reconstruction and feature matching

Abstract With the exponential growth of various data interactions on network systems, network intrusions are also increasing. The emergence of edge computing technology brings a new solution to network security. However, due to the difficulty of processing massive and unbalanced data at the edge, higher accuracy requirements are necessary for deployed detection models. This paper proposes a multi-classification model for network intrusion detection based on reconstruction and feature matching. This model can be deployed on small-scale edge nodes, effectively identifying various attack behaviors through the utilization of reconstruction errors and adaptive scaling. Furthermore, we proposed a model transfer method based on feature matching to enhance the training and detection efficiency of multi-classification models under different data distribution conditions. The proposed model has been evaluated on the CICIDS2017 dataset in terms of accuracy, recall, precision and F1 score. The model demonstrates high accuracy for normal flows in the network, majority class attacks, and minority class attacks, achieving an overall multi-class accuracy of 99.81%, outperforming similar models. Furthermore, this model demonstrates faster convergence and training speed after feature matching, exhibiting better robustness and outstanding performance at the edge.
Recommendation systems-based software requirements elicitation process—a systematic literature review

Abstract Requirements elicitation is one of the fundamental sub-processes of requirements engineering which is used to find the needs of stakeholders. There are several activities in this sub-process, i.e., identification of stakeholders and their requirements, software requirements prioritization, and analysis. Recommendation systems have been intertwined with the requirements elicitation process to predict the stakeholders’ requirements based on their preferences for functional and non-functional requirements. A number of systematic literature reviews (SLRs) have been carried out in the area of requirements elicitation. These SLRs do not support the applications of the recommendation systems during the requirements elicitation process. To deal with this issue, we present an SLR on recommendation systems-based software requirements elicitation processes, from 2009 to 2022, undertaking four research questions: (a) What are the different activities of the software requirements elicitation methods? (b) What are the applications of recommendation systems in the identification of the software requirements? (c) How the recommendation systems can facilitate the identification of stakeholders in the requirements elicitation process? (d) What are the ways to automate the selection of requirements elicitation techniques? The aim of this study is to identify the research gaps in the area of recommendation systems-based requirements elicitation processes and suggest future research directions.
A Novel Hybrid Deep Learning Model for Photovoltaic Power Forecasting Based on Feature Extraction and <scp>BiLSTM</scp>

In this paper, a hybrid photovoltaic power forecasting model is proposed based on bidirectional long‐short‐term memory network. Firstly, the photovoltaic power and meteorological data are decomposed by ensemble empirical mode decomposition. Secondly, key features in the meteorological subsequences are extracted by kernel principal component analysis method to eliminate the correlation and redundancy in the original meteorological sequence and reduce the input dimension of the model, then the coupling time characteristics between meteorological subsequences and photovoltaic power subsequence are further explored. Thirdly, a bidirectional long‐short‐term memory network optimized by improved particle swarm optimization based on inertia weight of anti‐sine function is proposed, and then the eventual photovoltaic power forecasting results are obtained by superimposing the predicted values of each prediction component. The experiment is carried out with the operation data of the Inner Mongolia power grid, and the results show that the proposed forecasting model exhibits higher prediction accuracy. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Detection of activities in bathrooms through deep learning and environmental data graphics images

Automatic detection activities in indoor spaces has been and is a matter of great interest. Thus, in the field of health surveillance, one of the spaces frequently studied is the bathroom of homes and specifically the behaviour of users in the said space, since certain pathologies can sometimes be deduced from it. That is why, the objective of this study is to know if it is possible to automatically classify the main activities that occur within the bathroom, using an innovative methodology with respect to the methods used to date, based on environmental parameters and the application of machine learning algorithms, thus allowing privacy to be preserved, which is a notable improvement in relation to other methods. For this, the methodology followed is based on the novel application of a pre-trained convolutional network for classifying graphs resulting from the monitoring of the environmental parameters of a bathroom. The results obtained allow us to conclude that, in addition to being able to check whether environmental data are adequate for health, it is possible to detect a high rate of true positives (around 80%) in some of the most frequent and important activities, thus facilitating its automation in a very simple and economical way.