Reviewerly/exHarmony huggingface dataset:
Weighted structure tensor total variation for image denoising

Authors:
raw_author_name: Xiuhan Sheng
raw_affiliation_string: Guangdong University of Technology, School of Mathematics and Statistics, Guangzhou, China
raw_author_name: Lijuan Yang
raw_affiliation_string: Guangdong University of Technology, School of Mathematics and Statistics, Guangzhou, China

Abstract: For image denoising problems, the structure tensor total variation (STV)-based models show good performances when compared with other competing regularization approaches. However, the STV regularizer does not couple the local information of the image and may not maintain the image details. Therefore, we employ the anisotropic weighted matrix introduced in the anisotropic total variation (ATV) model to improve the STV model. By applying the weighted matrix to the discrete gradient of the patch-based Jacobian operator in STV, our proposed weighted STV (WSTV) model can effectively capture local information from images and maintain their details during the denoising process. The optimization problem in the model is solved by a fast first-order gradient projection algorithm with a complexity result of O(1/i2). For images with different Gaussian noise levels, the experimental results demonstrate that the WSTV model can effectively improve the quality of restored images compared to other TV and STV-based models.
Solar Radiation Forecast Utilizing an Artificial Neural Network Model with Three Distinct Learning Algorithms

Authors:
raw_author_name: M. Bhuvaneswari
raw_affiliation_string: Sri Krishna College of Engineering and Technology
raw_author_name: S. Rajesh
raw_affiliation_string: R.M.K. Engineering College, Department of Mechanical Enginee

Abstract: &lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Solar energy, which has always been at the forefront, has discovered numerous uses in a variety of fields. One of the key targets of scientists and producers in the twenty-first decade is sustainable solar energy collecting. The maximization of solar energy is totally dependent on the radiation absorbed by the photovoltaic panels. Radiation is observed using numerous equipment and calculated using diverse methods. If the device is to be totally reliant on solar energy, it must be calculated far ahead. It is difficult to work because solar radiation is affected by various factors, including region as well as seasonality. In forecast scenarios, Artificial Neural Networks (ANN) is a popular approach among scientists. Therefore, this research provides a technique for estimating solar radiation that makes use of back-propagation algorithms. The data of 17 stations in Tamil Nadu, India, were acquired for analysis and split into three clusters: training, validation, and testing. This research is focused on nine input variables and one outcome variable. Solar radiation is estimated via feed-forward back-propagation in this case. The presented approach is ascertained for training techniques such as Levenberg Marquardt (LM), Bayesian-Regularization (BR), as well as Scaled Conjugate Gradient (SCG). In all three scenarios, the resulting statistical error values and regression values prove the adequacy of the presented approach. The Root Mean Square Error (RMSE) for the BR approach is found to be the lowest, with a value of 0.0013. Also, from all statistical error and regression values, the training approach BR produces the best value than LM and SCG training approaches.&lt;/div&gt;&lt;/div&gt;
Event Data Recorder Trigger Probability in the Crash Investigation Sampling System Database

Authors:
raw_author_name: Richard A. Watson
raw_affiliation_string: Biodynamic Research Corporation, Engineering

Abstract: &lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The objective of this study was to quantify the field performance of passenger vehicle event data recorders (EDRs) in recording data into non-volatile memory at the 8 km/h delta-v (Δ&lt;i&gt;v&lt;/i&gt;) trigger threshold specified by Title 49, Part 563 of the Code of Federal Regulations (Part 563). Part 563 applies to passenger vehicles manufactured on or after September 1, 2012. The trigger threshold is distinct from the threshold required to deploy an airbag. Events meeting the trigger threshold will cause data to be preserved on the EDR even if airbags are not deployed. This is the first study to quantify EDR trigger threshold performance. This data is valuable in the evaluation of sub-airbag deployment crashes. The study was accomplished via analysis of EDR and reconstructed Δ&lt;i&gt;v&lt;/i&gt; data from 3,960 cases in the Crash Investigation Sampling System (CISS) database maintained by the National Highway Traffic Safety Administration (NHTSA). The binary presence or non-presence of an event on the EDRs of vehicles exposed to a collision was compared to the CISS reconstructed Δ&lt;i&gt;v&lt;/i&gt; for each vehicle. Logistic regression models were developed to predict the probability of an event present on the EDR at the trigger threshold. We found that vehicles manufactured by Toyota had lower Δ&lt;i&gt;v&lt;/i&gt; thresholds compared to other manufacturers. For Toyota manufactured vehicles, the probability of an EDR event at 8 km/h ranged from 87% to 99%. EDR event probabilities for non-Toyota vehicles in vehicle-to-vehicle collisions ranged from 58% to 93% for Part 563 compliant vehicles and 33% to 83% for pre-Part 563 vehicles. The persistence of EDR events in memory was analyzed using the number of ignition cycles present between events and imaging of the EDR, finding average duration of 3,595 ignition cycles for pre-existing EDR events unrelated to the CISS case.&lt;/div&gt;&lt;/div&gt;
Computational Fluid Dynamics Analysis of a Coaxial Unmanned Aerial Vehicle

Authors:
raw_author_name: G. Dinesh Kumar
raw_affiliation_string: Hindustan Institute of Technology and Science

Abstract: &lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The focus of this project is on the preliminary design of an unmanned aerial vehicle (UAV) utilizing a coaxial rotor setup, taking into account its flight dynamics. Additionally, a comprehensive aerodynamic analysis is conducted using computational fluid dynamics (CFD). The idealogy for our design came from the Rukma Vimana, a flying craft mentioned in the ancient Hindu Scriptures. The design is optimized with the coaxial copter setup, a different approach that has yet to be widely explored in the UAV aspect. Coaxial rotors are installed in pairs, with one rotor mounted above the other on concentric shafts. Both rotors have the same axis of rotation, but they rotate in opposite directions (contra-rotate). CFD simulations are conducted to see how the fluid medium flows over the unconventional design of the UAV. Three different in terms of three designs heights are considered, ie, a 30 cm height model, a 35 cm height model, and a 40 cm height model. The inclination of the analysis done is parallel to gravity; therefore, we have tested for vertical flow since it is a vertical take-off and landing (VTOL) configuration. Variations in the nose cones are also carried, such as the blunt nose cone and the round nose cone. Overall, with three different heights and two variations in the nose cone, we had a total of six simulations to analyze in CFD from which 30cm performed better out of which we chose the blunt nose cone with a height of 30cm is assumed to be the best configuration as it was feasible to fabricate.&lt;/div&gt;&lt;/div&gt;
Social VR design features and experiential outcomes: narrative review and relationship map for dyadic agent conversations

Authors:
raw_author_name: Brendan Rooney
raw_affiliation_string: UCD School of Psychology, University College Dublin, John Henry Newman Building, UCD, Stillorgan Rd, Belfield, Dublin 4, D04 V1W8, Ireland
raw_author_name: Maximilian A. Friehs
raw_affiliation_string: UCD School of Psychology, University College Dublin, John Henry Newman Building, UCD, Stillorgan Rd, Belfield, Dublin 4, D04 V1W8, Ireland
raw_author_name: John Francis Leader
raw_affiliation_string: UCD School of Psychology, University College Dublin, John Henry Newman Building, UCD, Stillorgan Rd, Belfield, Dublin 4, D04 V1W8, Ireland

Abstract: Abstract The application of virtual reality to the study of conversation and social interaction is a relatively new field of study. While the affordances of VR in the domain compared to traditional methods are promising, the current state of the field is plagued by a lack of methodological standards and shared understanding of how design features of the immersive experience impact participants. In order to address this, this paper develops a relationship map between design features and experiential outcomes, along with expectations for how those features interact with each other. Based on the results of a narrative review drawing from diverse fields, this relationship map focuses on dyadic conversations with agents. The experiential outcomes chosen include presence &amp; engagement, psychological discomfort, and simulator sickness. The relevant design features contained in the framework include scenario agency, visual fidelity, agent automation, environmental context, and audio features. We conclude by discussing the findings of the review and framework, such as the multimodal nature of social VR being highlighted, and the importance of environmental context, and lastly provide recommendations for future research in social VR.
The lonely traveller’s mind: exploring the influence of loneliness on revisit intention

Authors:
raw_author_name: Yue Liu
raw_affiliation_string: School of Tourism, Hainan University, Haikou, People’s Republic of China

Abstract: Despite the fact that feelings of loneliness offer invaluable opportunities for marketing and management, studies on its impacts and consequences in the travel context are still in their infancy. Drawing on compensatory consumption behaviour theory, and using a mixed-methods approach, this research explored the influence of loneliness (i.e. trait loneliness and state loneliness) on tourists' revisit intention. In light of the findings derived from interviews and scenario-based experiments, the research validates the positive effects of each loneliness variable on revisit intention. Moreover, the mediating effects of nostalgia on the relationships between trait loneliness and revisit intention, and between state loneliness and revisit intention, have been revealed. This study enriches the literature on tourism and consumer behaviour and offers several tailor-made suggestions for relevant practitioners.
Joint first and second order total variation decomposition for remote sensing images destriping

Authors:
raw_author_name: Ayoub Boutemedjet
raw_affiliation_string: Laboratoire des Systèmes Lasers, Ecole Militaire Polytechnique, Algiers, Algeria

Abstract: Stripe noise remains a significant source of errors and image quality degradation in remote sensing systems. A prominent approach for tackling this problem is the first-order Total Variation (TV) regularization, which has a proven efficiency in dealing with stripe noise. Unfortunately, denoised images, in this case, may suffer from texture loss and staircase artefacts around smooth areas. In this paper, a novel image decomposition scheme is proposed to tackle these drawbacks. This decomposition is based on the use of directional first- and second-order TV regularizers that are employed to separate the clear image from the stripe component while considering the directionality and smoothness of the latter. The proposed model is solved using a Chambolle-based algorithm and its performance is compared to traditional destriping methods using different noise structures and intensities. The results have shown comparable performance to the existing state-of-the-art methods with some improvements in structure preservation and noise cancellation.
Unsupervised framework for evaluating and explaining structural node embeddings of graphs

Authors:
raw_author_name: Ashkan Dehghan
raw_affiliation_string: Department of Mathematics, Toronto Metropolitan University , 350 Victoria Street , Toronto, ON M5B 2K3, Canada
raw_author_name: Kinga Siuta
raw_affiliation_string: Department of Mathematics, Toronto Metropolitan University , 350 Victoria Street , Toronto, ON M5B 2K3, Canada; SGH Warsaw School of Economics , al. Niepodległości 162 , 02-554 Warszawa, Poland
raw_author_name: Agata Skorupka
raw_affiliation_string: Department of Mathematics, Toronto Metropolitan University , 350 Victoria Street , Toronto, ON M5B 2K3, Canada; SGH Warsaw School of Economics , al. Niepodległości 162 , 02-554 Warszawa, Poland
raw_author_name: Andrei Betlen
raw_affiliation_string: Patagona Technologies , Pickering, ON, Canada
raw_author_name: David Miller
raw_affiliation_string: Patagona Technologies , Pickering, ON, Canada
raw_author_name: Bogumił Kamiński
raw_affiliation_string: SGH Warsaw School of Economics , al. Niepodległości 162 , 02-554 Warszawa, Poland
raw_author_name: Paweł Prałat
raw_affiliation_string: Department of Mathematics, Toronto Metropolitan University , 350 Victoria Street , Toronto, ON M5B 2K3, Canada

Abstract: Abstract An embedding is a mapping from a set of nodes of a network into a real vector space. Embeddings can have various aims like capturing the underlying graph topology and structure, node-to-node relationship, or other relevant information about the graph, its subgraphs or nodes themselves. A practical challenge with using embeddings is that there are many available variants to choose from. Selecting a small set of most promising embeddings from the long list of possible options for a given task is challenging and often requires domain expertise. Embeddings can be categorized into two main types: classical embeddings and structural embeddings. Classical embeddings focus on learning both local and global proximity of nodes, while structural embeddings learn information specifically about the local structure of nodes’ neighbourhood. For classical node embeddings, there exists a framework which helps data scientists to identify (in an unsupervised way) a few embeddings that are worth further investigation. Unfortunately, no such framework exists for structural embeddings. In this article, we propose a framework for unsupervised ranking of structural graph embeddings. The proposed framework, apart from assigning an aggregate quality score for a structural embedding, additionally gives a data scientist insights into properties of this embedding. It produces information which predefined node features the embedding learns, how well it learns them, and which dimensions in the embedded space represent the predefined node features. Using this information, the user gets a level of explainability to an otherwise complex black-box embedding algorithm.
Does corruption sand the wheels of sustainable development? Evidence through green innovation

Authors:
raw_author_name: Tanveer Ahsan
raw_affiliation_string: Rennes School of Business Rennes France
raw_author_name: Ammar Ali Gull
raw_affiliation_string: International School Vietnam National University Hanoi Vietnam; Léonard de Vinci Pôle Universitaire, Research Center  Paris France

Abstract: Abstract This study investigates the impact of corruption on green innovation, as corruption may impede or foster green innovation in developing economies due to their weak governance systems. We develop a dataset of Chinese non‐financial firms listed between 2007 and 2020 and apply static and dynamic regression techniques. The results indicate a highly significant negative association between corruption and green innovation. This supports the notion that corruption culture reduces corporate legitimacy concerns ( institutional theory ), increases managerial rent‐seeking ( agency theory ), and hinders green innovation, thus impeding sustainable development and supporting the “ sand the wheels ” hypothesis. Our analysis also reveals that corruption's negative impact on green innovation is particularly significant for firms with lower media and analyst coverage, non‐state‐owned firms, and firms in heavy‐polluting industries. These results are robust to alternate proxies of green innovation and corruption as well as econometric specifications that account for endogeneity issues and industry, region, and time‐fixed effects.
A quantum secure multi-party summation protocol based on one-direction quantum walks on a circle

Authors:
raw_author_name: Jin-Tao WANG
raw_author_name: Xia LI
raw_author_name: Tian-Yu YE

Abstract: <p indent="0mm">This paper presents a novel quantum secure multi-party summation protocol based on one-direction quantum walks on a circle (ODQWC), which allows the summation computation of different integer sequences from "<italic>n</italic>" secret holders. Crucially, it ensures that this process does not infringe on the privacy of these secret holders by delivering quantum walk states in a circular pattern. Our proposed protocol employs two-particle product states rather than quantum entangled states as the initial quantum resource and eliminates the need for different secret holders to perform quantum entanglement swapping operations. Instead, it emphasizes imposing one-direction evolution operators on quantum walk states. Additionally, our protocol requires single-particle measurements. Security analysis validates that our proposed protocol can effectively counter external attacks and those initiated by participants. Under specific conditions, our proposed protocol can be adapted into a quantum secure multi-party bit summation protocol based on ODQWC.
Emojis as heuristic cues: The multifaceted role of emojis in online service interactions

Authors:
raw_author_name: Diego Costa Pinto
raw_affiliation_string: NOVA Information Management School (NOVA IMS) Universidade Nova de Lisboa Lisboa Portugal
raw_author_name: Márcia Maurer Herter
raw_affiliation_string: Advance/CSG, ISEG, Universidade de Lisboa Lisboa Portugal
raw_author_name: Anna Mattila
raw_affiliation_string: Penn State College of Health and Human Development The Pennsylvania State University University Park Pennsylvania USA

Abstract: 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

Authors:
raw_author_name: Jinshui Qi
raw_affiliation_string: Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology, Xinjiang University  Urumqi 830 017 China
raw_author_name: Jiaqing Mo
raw_affiliation_string: Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology, Xinjiang University  Urumqi 830 017 China
raw_author_name: Yasen Niu
raw_affiliation_string: Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology, Xinjiang University  Urumqi 830 017 China

Abstract: 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.
A Study on the Visual Recognition Patterns of Japanese Beginner Learners Regarding Hangul Characters: Focusing on Consonant Letters

Authors:
raw_author_name: Minsoo Kim

Abstract: 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.