Reviewerly/exHarmony huggingface dataset:
An Efficient and Lightweight Model for Automatic Modulation Classification: A Hybrid Feature Extraction Network Combined with Attention Mechanism

This paper proposes a hybrid feature extraction convolutional neural network combined with a channel attention mechanism (HFECNET-CA) for automatic modulation recognition (AMR). Firstly, we designed a hybrid feature extraction backbone network. Three different forms of convolution kernels were used to extract features from the original I/Q sequence on three branches, respectively, learn the spatiotemporal features of the original signal from different “perspectives” through the convolution kernels with different shapes, and perform channel fusion on the output feature maps of the three branches to obtain a multi-domain mixed feature map. Then, the deep features of the signal are extracted by connecting multiple convolution layers in the time domain. Secondly, a plug-and-play channel attention module is constructed, which can be embedded into any feature extraction layer to give higher weight to the more valuable channels in the output feature map to achieve the purpose of feature correction for the output feature map. The experimental results on the RadiomL2016.10A dataset show that the designed HFECNET-CA has higher recognition accuracy and fewer trainable parameters compared to other networks. Under 20 SNRs, the average recognition accuracy reached 63.92%, and the highest recognition accuracy reached 93.64%.
Distributed Subgraph Query Processing Using Filtering Scores on Spark

As various services have been generating large-scale graphs to represent multiple relationships between objects, studies have been conducted to obtain subgraphs with particular patterns. In this paper, we propose a distributed query processing method to efficiently search a subgraph for a large graph on Spark. To reduce unnecessary processing costs, the search order is determined by filtering scores using the probability distribution. The partitioned queries are searched in parallel in the distributed graph of each slave node according to the search order, and the local search results obtained from each slave node are combined and returned. The query is partitioned in triplets based on the determined search order. The performance of the proposed method is compared with the performance of existing methods to demonstrate its superiority.
An Adaptive Dimension Weighting Spherical Evolution to Solve Continuous Optimization Problems

The spherical evolution algorithm (SE) is a unique algorithm proposed in recent years and widely applied to new energy optimization problems with notable achievements. However, the existing improvements based on SE are deemed insufficient due to the challenges arising from the multiple choices of operators and the utilization of a spherical search method. In this paper, we introduce an enhancement method that incorporates weights in individuals’ dimensions that are affected by individual fitness during the iteration process, aiming to improve SE by adaptively balancing the tradeoff between exploitation and exploration during convergence. This is achieved by reducing the randomness of dimension selection and enhancing the retention of historical information in the iterative process of the algorithm. This new SE improvement algorithm is named DWSE. To evaluate the effectiveness of DWSE, in this study, we apply it to the CEC2017 standard test set, the CEC2013 large-scale global optimization test set, and 22 real-world problems from CEC2011. The experimental results substantiate the effectiveness of DWSE in achieving improvement.
Software design analysis and technical debt management based on design rule theory

In this paper we reflect on our decade-long journey of creating, evolving, and evaluating a number of software design concepts and technical debt management technologies. These include: a novel maintainability metric, a new model for representing design information, a suite of design anti-patterns, and a formalized model of design debt. All of these concepts are rooted in options theory, and they all share the objective of helping a software project team quantify and visualize major design principles, and address the very real maintainability challenges faced by their organizations in practice. The evolution of our research has been propelled by our continuous interactions with industrial collaborators. For each concept, technology, and supporting tool, we embarked on an ambitious program of empirical validation—in “the lab”, with industry partners, and with open source projects. We reflect on the successes of this research and on areas where significant challenges remain. In particular, we observe that improved software design education, both for students and professional developers, is the prerequisite for our research and technology to be widely adopted. During this journey, we also observed a number of gaps: between what we offer in research and what practitioners need, between management and development, and between debt detection and debt reduction. Addressing these challenges motivates our research moving forward.
Avatar taxonomy: a new technological tool to enhance the consumer-brand relationships

Purpose Avatars, which are used as a technology and marketing tactic, can embody consumer-facing employees and mimic their real-life roles on companies' websites, thereby playing a key role in enhancing the relationships between consumers and brands in the online environment. Academics and practitioners have increasingly acknowledged the significance of the consumer-brand relationship in both traditional and online contexts. However, the impersonal nature of the online environment is considered to be a hindrance to the development of these relationships. Despite the importance of this technology, little attention has been paid to the investigation of the avatar concept from a marketing perspective. This paper explores the nature of the avatar concept, including its main characteristics, dimensions, and conditions as well as the attitudinal and behavioural consequences of avatar users. Design/methodology/approach Adopting the qualitative design, a taxonomy was developed from interviews. In total, 42 interviews were conducted with current university students. 30 participants participated in the exploratory interviews. A total of 12 interviews were conducted during the in-depth stage based on findings in the preceding research. Findings Based on the qualitative data analysis, a taxonomy was developed. The idea of the taxonomy is summarized in that different dimensions of the avatar are considered the main base (first phase) of the taxonomy. There are consequential three parts: the attitudinal consequences related to the website; the attitudinal consequences related to the brand; the behaviours towards the brand. These behaviours represent the final phase of the taxonomy. Originality/value By developing a taxonomy of using avatars on brands' websites, the authors advance the understanding consumer-brands relationships. Using avatars' verbal interactions helps in shaping consumers' cognitive, affective, attitudinal and behavioural responses and add vital empirical evidence to the increasing body of research and practices involving avatar usage in the interactive marketing area.
A Signal-Denoising Method for Electromagnetic Leakage from USB Keyboards

USB keyboards are commonly used as computer input devices and inevitably generate electromagnetic (EM) leakage signals during their operation, which carry input information. However, due to the weak energy of a keyboard’s EM signal and the small amount of effective information, the received leakage signal is often characterized by a low signal-to-noise ratio (SNR). This low SNR affects the subsequent detection and restoration of the information. In order to solve this problem, this paper proposes a denoising method for USB keyboard EM leakage signals and designs a self-attentive denoising adversarial network (SADAN) based on generative adversarial networks (GANs). The denoiser continuously enhances the denoising ability during the generative adversarial process, and the self-attention mechanism enables it to better learn the dependencies of the keyboard EM leak signal sequences, modelling the long-range relationships between the sequence sample points and reducing the impact of the number of network layers on the relationship acquisition. The method achieves noise suppression in the keyboard leakage signal, improving its SNR while preserving the effective information in the leakage signal and finally obtaining a denoised leakage signal that can be effectively restored to the information.
ABT-SVDD: A method for uncertainty handling in domain adaptation using belief function theory

Domain adaptation involves adapting a model trained on one domain to work effectively on another, which can have different statistical properties, such as distributions, correlations, and relationships between features. These heterogeneities can lead to uncertainty, impacting the model’s performance. Despite many studies that have been done on domain adaptation, most have ignored the adverse impact of uncertain and noisy data on adaptation and classification. To address this issue, the proposed method, Adaptive Belief-based Twin Support Vector Data Description (ABT-SVDD), extends the one-class support vector data description (SVDD) to an adaptive twin classifier and integrates it with a belief-based sample weighting approach. Also, it utilizes a combination of Hermite polynomial and Gaussian kernels to enhance the computational power of the linear objective function of the SVDD classifier while improving the generalization capability. The effectiveness of ABT-SVDD has been compared to the state-of-the-art methods on several tasks taken from two benchmark datasets. The experimental results demonstrate that ABT-SVDD significantly improves classification accuracy on various tasks with varying amounts of labeled data in the target domain. Specifically, in normal situations, ABT-SVDD outperforms competing methods by 6.33% to 9.08%, while in noisy situations, it achieves a more significant improvement of 9.87% compared to the competing methods. Besides, the Wilcoxon statistical test demonstrates the superiority of ABT-SVDD over state-of-the-art ones in terms of classification accuracy and computational time.
Unveiling hierarchical relationships for social image representation learning

Amidst the rapid propagation of images across social media platforms, the representation learning of social images has emerged as a prime research pursuit. A salient characteristic of social images lies in their multimodal properties, encompassing the visual content and textual descriptions present within individual images and the social relationships among different images. Notably, the content information within individual images and the structural information among different images exhibit a hierarchical nature. However, existing endeavors generally employ a flat framework, leading to suboptimal utilization of hierarchical relationships. Furthermore, the heterogeneity inherent in data content and structural information presents additional challenges in social image representation. In light of these challenges, we propose a novel Hierarchical Heterogeneous Graph Neural Network model for Social Image Representation learning, dubbed HHGSI. Our motivation lies in exploring and exploiting the hierarchical relationship between diverse modalities through designing the hierarchical heterogeneous network framework. HHGSI consists of an Intra-node Multimodal Graph Encoder and an Inter-node Heterogeneous Graph Neural Network to simultaneously capture fine-grained correlation within the image and heterogeneous relationships among the images. Moreover, a task-independent optimization objective is designed to make the model suitable for numerous network-oriented and multimodal tasks. Our proposal is extensively evaluated over four real-world datasets, and experimental results demonstrate the superiority of our proposal. Our code is publicly available in https://github.com/multimodal-code/HHGSI.
ConKgPrompt: Contrastive Sample Method Based on Knowledge-Guided Prompt Learning for Text Classification

Text classification aims to classify text according to pre-defined categories. Despite the success of existing methods based on the fine-tuning paradigm, there is a significant gap between fine-tuning and pre-training. Currently, prompt learning methods can bring state of the art (SOTA) performance to pre-trained language models (PLMs) in text classification and transform a classification problem into a masked language modeling problem. The crucial step of prompt learning is to construct a map between original labels and the label extension words. However, most mapping construction methods consider only labels themselves; relying solely on a label is not sufficient to achieve accurate prediction of mask tokens, especially in classification tasks where semantic features and label words are highly interrelated. Therefore, the accurate prediction of mask tokens requires one to consider additional factors beyond just label words. To this end, we propose a contrastive sample method based on knowledge-guided prompt learning framework (ConKgPrompt) for text classification. Specifically, this framework utilizes external knowledge bases (KBs) to expand the label vocabulary of verbalizers at multiple granularities. In the contrastive sample module, we incorporate supervised contrastive learning to make representations more expressive. Our approach was validated on four benchmark datasets, and extensive experimental results and analysis demonstrated the effectiveness of each module of the ConKgPrompt method.
58‐2: Structurally Anisotropic Backing Sheets for Rollable‐Foldable Display Modules

While perforated‐slot backing plates are universally used to ruggedize today's foldable OLED display modules, we investigated an alternative approach incorporating freestanding ribs connected by a low modulus spring material. We explore manufacturing options and offer a closed‐form structural analysis of both types to inform applications of the alternative approach.
Operations-informed incident response playbooks

Cyber security incident response playbooks are critical for establishing an effective incident response capability within organizations. We identify a significant conceptual gap in the current research and practice of cyber security playbook design: the lack of ability to communicate the operational impact of an incident and of incident response on an organization. In this paper, we present a mechanism to address the gap by introducing the operational context into an incident response playbook. This conceptual contribution calls for a shift from playbooks that consist only of process models to playbooks that consist of process models closely linked with a model of operations. We describe a novel approach to embed a model of operations into the incident response playbook and link it with the playbook's incident response activities. This allows to reflect, in an accurate and systematic way, the interdependencies and mutual influences of incident response activities on operations and vice versa. The approach includes the use of a new metric for evaluating the change in operations in coordination with critical thresholds, supporting decision-making during cyber security incident response. We demonstrate the application of the proposed approach to playbook design in the context of a ransomware attack incident response, using a newly developed open-source tool.
82‐4: Color characterization of virtual reality devices using professional‐and consumer‐grade instruments

Color characteristics of an OLED‐based virtual reality headset were measured using a professional‐grade telescopic spectroradiometer and a consumer‐grade contact‐type colorimeter. The average color error was 1.02 ΔE00 and could be further reduced to 0.57 ΔE00 after customized calibration.
31‐2: An Asynchronous Single‐Ended Touch Sensing Method for Y‐OCTA Using Adaptive TX Duty Control method

In this paper, it is introduced a new touch sensing method for the flexible OLED (Y‐OCTA) eliminating the chronic constraints on selecting touch sensing frequencies. In the prior art, the touch sensing frequencies have been limited to the display scan frequency(H‐sync) or its harmonics to avoid the interference effects between Touch and Display operations, which results in some weaknesses in external noise immunity for the Touch sensor. This paper proposes a method to overcome the constraints such that any sensing frequencies would be available, while preserving Display Noise immunity for Touch IC and display flicker‐free for Display as well. As a result, the proposed IC showed SNR enhancement of 17 dB without display flicker in the asynchronous sensing mode for the worst test image patterns having the strongest Display Noise. The proposed IC was fabricated on a 90‐nm CMOS process and its silicon area is 21.2mm 2 .
Enhancing Grammatical Skills through Recounting the YouTube Video to Improve Speaking Ability

Delivering ideas to others involves speaking in a significant way. Information exchange amongst language users is facilitated by this. However, many EFL students had difficulties when they tried to practice speaking. During the delivery, they stutter and become stuck. Ten freshmen attend the therapy to address these faults. It modifies YouTube content to enhance students' speaking abilities. To understand how to create sentences, the session began by using lexicogrammar. The instructor requested that the student study and retell a grammar video from YouTube in order to improve understanding. The students watched a vocabulary film on YouTube and then learnt and repeated the topic to increase their vocabulary. They then studied and repeated the information from a YouTube video that described the image. The teacher invited the students to describe photos using both written and spoken language in order to observe the students' growth in their vocabulary, grammar, and speaking abilities. The instructor provided feedback in the most recent class. The information gathered during the procedure was qualitatively examined. The study's findings led to an improvement in the students' speaking abilities by helping them with their grammar, vocabulary, and pronunciation. This kind of discovery could be used by teachers to teach the pertinent subjects. Additionally, it motivates additional researchers to study relevant topics.
Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots

Coal Mine Mobile Robots (CMRRs) are generally large in size and inertia, while narrow laneway space and bumpy terrain pose great challenges to CMRR’s planning and control. Aiming at the trajectory planning and tracking problems of CMRR, a new trajectory class MINCO is derived in detail based on the properties of differential flat systems. A trajectory planning method based on MINCO trajectory and safety corridor constraints constructed with underground environmental constraints is further proposed. A trajectory tracking method based on model predictive control (MPC) is further proposed. The prediction model of MPC is constructed by a kinematics model and transformed into a standard quadratic programming problem according to the cost function of a trajectory tracking target. Finally, large quantities of field tests were carried out for the proposed approaches. The results show that the proposed planning algorithm based on MINCO trajectory can achieve good avoidance effects within 10 planning attempts in different obstacle scenarios, and the trajectory is smoother compared to the Fast-Planner algorithm, with shorter trajectory length and less planning time. The tracking error of MPC is always less than 0.05 m in different underground scenarios, having a more adaptable trajectory tracking effect than PID.
Are Reality, Simulation, and Augmented Reality Interchangeable?

Students often ask why they should learn or where they would use this knowledge when learning. Real-life experiences make learning more meaningful for the students. Thus, learning environments where the students could acquire real-life experiences are important. However, due to the student profile, crowded classes, inadequate course hours, technological advances, natural disasters, etc., conventional instruction methods could not meet student requirements and they could not practice. This negatively affects learning achievements and psychomotor skills of the students. Effective real-life educational experiences are required to improve learning achievements and psychomotor skills of the students. Thus, the present study aimed to investigate learning achievement and psychomotor skills levels of college students in the ICT course and substitution of augmented reality applications and simulations with real-life experiences. The study data were collected from 63 college students. Descriptive statistics, two-way ANOVA, and Wilcoxon Signed Rank Test analysis were employed to answer the research questions. The findings demonstrated that augmented reality and simulation-assisted learning environments were as effective as real-life learning environments in the improvement of the learning achievements and psychomotor skills of the students in the ICT course. Thus, it could be suggested that augmented reality or simulation applications could be employed in learning environments that lack real-life experiences.
SUBTLEX-CY: A new word frequency database for Welsh

We present SUBTLEX-CY, a new word frequency database created from a 32-million-word corpus of Welsh television subtitles. An experiment comprising a lexical decision task examined SUBTLEX-CY frequency estimates against words with inconsistent frequencies in a much smaller Welsh corpus that is often used by researchers, the Cronfa Electroneg o'r Gymraeg (CEG), and three other Welsh word frequency databases. Words were selected that were classified as low frequency (LF) in SUBTLEX-CY and high frequency (HF) in CEG and compared with words that were classified as medium frequency (MF) in both SUBTLEX-CY and CEG. Reaction time analyses showed that HF words in CEG were responded to more slowly compared to MF words, suggesting that SUBTLEX-CY corpus provides a more reliable estimate of Welsh word frequencies. The new Welsh word frequency database that also includes part-of-speech, contextual diversity, and other lexical information is freely available for research purposes on the Open Science Framework repository at https://osf.io/9gkqm/.
Signature Specimen: The Alma King Rhodochrosite At the Denver Museum of Nature &amp; Science

Click to increase image sizeClick to decrease image size ACKNOWLEDGMENTSWe are indebted to Editor-in-Chief Marie Huizing for the invitation to write this article, to Bryan Lees and Jack Murphy for reviewing the manuscript, to William Besse for drafting the location maps, and to Dave Baysinger, Jack Murphy, Dave Schrader, Laura Uglean-Jackson, and Rick Wicker for providing photographs.Additional informationNotes on contributorsCarl A. FrancisDr. Carl A. Francis is the curator at the Maine Mineral & Gem Museum and an executive editor of Rocks & Minerals.James W. HagadornDr. James W. Hagadorn is the Tim and Kathryn Ryan Curator of Geology at the Denver Museum of Nature & Science.
動画を対象とした顕著性マップ推定のための視線解析

Saliency maps for predicting areas of human fixations from images have been actively studied. In order to improve the accuracy of the video saliency model, we compared the saliency map predicted by video saliency models with the saliency map measured by experiment. We found that the area of fixations is biased in the direction where the target is moving and the target cannot be gazed at when the motion of the target changes. Moreover, we confirmed that the findings are unavailable for one of the state-of-the-art video saliency models, STSANet.