Bribing to Escape Poverty in Africa

Abstract: This study assesses the nexus between bribery and poverty using the Afrobarometer survey. Our data cover 38 countries consisting of three survey rounds and a sample of 151,345 individuals. The empirical strategy is based on multi-level mixed-effects ordered logit regression. The results reveal that while poverty has a positive effect on the spread of bribery, inflation can mitigate the impact. The impact is stronger for people living without basic necessities such as food, water, and medical care. In other words, the results indicate that the impact of poverty on bribery becomes negative when inflation increases. Policy implications are discussed.


Supporting Safety Analysis of Image-processing DNNs through Clustering-based Approaches

Abstract: The adoption of deep neural networks (DNNs) in safety-critical contexts is often prevented by the lack of effective means to explain their results, especially when they are erroneous. In our previous work, we proposed a white-box approach (HUDD) and a black-box approach (SAFE) to automatically characterize DNN failures. They both identify clusters of similar images from a potentially large set of images leading to DNN failures. However, the analysis pipelines for HUDD and SAFE were instantiated in specific ways according to common practices, deferring the analysis of other pipelines to future work. In this paper, we report on an empirical evaluation of 99 different pipelines for root cause analysis of DNN failures. They combine transfer learning, autoencoders, heatmaps of neuron relevance, dimensionality reduction techniques, and different clustering algorithms. Our results show that the best pipeline combines transfer learning, DBSCAN, and UMAP. It leads to clusters almost exclusively capturing images of the same failure scenario, thus facilitating root cause analysis. Further, it generates distinct clusters for each root cause of failure, thus enabling engineers to detect all the unsafe scenarios. Interestingly, these results hold even for failure scenarios that are only observed in a small percentage of the failing images.


(R)evolutions of Thought: Artificial Intelligence and Education Futures

Abstract: As part of the 125th issue of Teachers College Record, this commentary provides an overview of technological innovation with a focus on the emergence of machine learning and artificial intelligence (AI). The presence and use of AI is a pressing contemporary topic in education, raising questions about the information and perspective(s) AI might privilege, as well as the evolving ethical concerns related to the blurred and increasingly indistinguishable boundaries of human and nonhuman entities and practices.


Talk2Data : A Natural Language Interface for Exploratory Visual Analysis via Question Decomposition

Abstract: Through a natural language interface (NLI) for exploratory visual analysis, users can directly “ask” analytical questions about the given tabular data. This process greatly improves user experience and lowers the technical barriers of data analysis. Existing techniques focus on generating a visualization from a concrete question. However, complex questions, requiring multiple data queries and visualizations to answer, are frequently asked in data exploration and analysis, which cannot be easily solved with the existing techniques. To address this issue, in this paper, we introduce Talk2Data, a natural language interface for exploratory visual analysis that supports answering complex questions. It leverages an advanced deep-learning model to resolve complex questions into a series of simple questions that could gradually elaborate on the users’ requirements. To present answers, we design a set of annotated and captioned visualizations to represent the answers in a form that supports interpretation and narration. We conducted an ablation study and a controlled user study to evaluate the Talk2Data’s effectiveness and usefulness.


A Bridged Cybersecurity Curriculum with Embedded Stackable Credentials

Abstract: Abstract Supported by a federal grant, The University of Texas Rio Grande Valley (UTRGV) streamlined the Bachelor of Science in Cyber Security (BSCS) and Master of Science in Informatics (MSIN) whereby each degree is broken down into embedded stackable credentials, with a fast-track 4+1 option for students to complete both degrees in 5 years. This paper provides a blueprint of the bridged undergraduate and graduate curriculum integrated to provide embedded stackable credentials with fast-track 4+1 option bridging the two degrees. Most of the major-core of BSCS is divided into three embedded stackable credentials, namely, Cyber Security Basics Certificate, Cyber Security Systems Certificate, and Cyber Security Advanced Certificate. After completing the three credentials, a student needs only 9 hours to complete the major-core for the BSCS degree. Similarly, most of the MSIN is divided into two embedded stackable credentials, namely, Graduate Certificate in Cyber Security and Graduate Certificate in Informatics. After completing the two graduate credentials, a student needs only 6 hours of thesis for the thesis route, or 6 hours of designated electives for the coursework option to earn an MSIN degree. A BSCS student with an overall 3.5 GPA or higher and having a GPA of 3.25 or higher in cyber security benchmark courses, is allowed to opt for the fast-track 4+1 option which allows a student to be simultaneously admitted into the Graduate School to take 12 hours of graduate courses from MSIN degree applicable towards completing BSCS, leaving one year's worth of graduate work to earn an MSIN degree. As part of the grant, the curriculum of BSCS degree is aligned with the national standard of National Initiative for Cybersecurity Education - Workforce Framework for Cybersecurity (NICE-WFC) for the students to have an edge in the job market. The three undergraduate certificates and the two graduate certificates are referred to as embedded stackable credentials since each credential is embedded in its respective degree and serves as a stack towards earning the degree. This approach is advantageous for students since one or more credentials may be earned without completing the respective degree which allows a student to have a few credentials targeted for the job market in case the student needs to take hiatus from education and work for a while. Furthermore, a professional already working in the cyber security or informatics field can earn one or more credentials without committing to complete a degree.


A Literature Review on Some Trends in Artificial Neural Networks for Modeling and Simulation with Time Series

Abstract: This paper reviews the application of artificial neural network (ANN) models to time series prediction tasks. We begin by briefly introducing some basic concepts and terms related to time series analysis, and by outlining some of the most popular ANN architectures considered in the literature for time series forecasting purposes: feedforward neural networks, radial basis function networks, recurrent neural networks, and self-organizing maps. We analyze the strengths and weaknesses of these architectures in the context of time series modeling. We then summarize some recent time series ANN modeling applications found in the literature, focusing mainly on the previously outlined architectures. In our opinion, these summarized techniques constitute a representative sample of the research and development efforts made in this field. We aim to provide the general reader with a good perspective on how ANNs have been employed for time series modeling and forecasting tasks. Finally, we comment on possible new research directions in this area.


Fast Recurrent Neural Network with Bi-LSTM for Handwritten Tamil text segmentation in NLP

Abstract: Tamil text segmentation is a long-standing test in language comprehension that entails separating a record into adjacent pieces based on its semantic design. Each segment is important in its own way. The segments are organised according to the purpose of the content examination as text groups, sentences, phrases, words, characters or any other data unit. That process has been portioned using rapid tangled neural organisation in this research, which presents content segmentation methods based on deep learning in natural language processing (NLP). This study proposes a bidirectional long short-term memory (Bi-LSTM) neural network prototype in which fast recurrent neural network (FRNN) are used to learn Tamil text group embedding and phrases are fragmented using text-oriented data. As a result, this prototype is capable of handling variable measured setting data and gives a vast new dataset for naturally segmenting text in Tamil. In addition, we develop a segmentation prototype and show how well it sums up to unnoticeable regular content using this dataset as a base. With Bi-LSTM, the segmentation precision of FRNN is superior to that of other segmentation approaches; however, it is still inferior to that of certain other techniques. Every content is scaled to the required size in the proposed framework, which is immediately accessible for the preparation. This means, each word in a scaled Tamil text is employed to prepare neural organisation as fragmented content. The results reveal that the proposed framework produces high rates of segmentation for manually authored material that are nearly equivalent to segmentation-based plans.


GA-BP Optimization Using Hybrid Machine Learning Algorithm for Thermopile Temperature Compensation

Abstract: Thermoelectric pile, which uses non-contact infrared temperature measurement principle, is widely used in various precision temperature measuring instruments. This paper analyzes environmental temperature's influence on thermoelectric piles' measurement accuracy and proposes a environment temperature compensation based on GA-BP (Genetic Algorithm-Back Propagation) neural network. The GA algorithm makes up for the slow iterative speed and easy to fall into local optimization of BP algorithm. The experimental simulation results show that environment temperature compensation based on GA-BP can accurately correct the measurement error caused by environmental temperature and other factors.


Reading participation and assessment of spreadsheet skills across multiple cohorts when using an interactive textbook

Abstract: This evidence-based practice complete paper centers on the topics of assessment and learning technology for first-year engineering students.Spreadsheets are convenient for completing many calculations that engineering students and practicing engineers encounter.Spreadsheet programs are easily accessible and have been available for decades, including Microsoft Excel and Google Sheets; numerous formulas, functions, and other tasks are common across platforms.However, learning spreadsheet skills is usually limited to demonstration by an expert, such as numerous videos on YouTube.Here, an interactive textbook provided students the opportunity to acquire spreadsheet skills by performing spreadsheet actions.Various components of the interactive textbook apply learning theories including cognitive load, scaffolding, and deliberate practice.First, reading participation measured clicks from using interactive components, including multistep animations, multiple-choice questions, and matching exercises.Next, students had unlimited attempts to correctly answer over 120 auto-graded, randomized problems.In addition to basic spreadsheet skills and functions, advanced topics included solver, error, statistics, and matrix operations.In total, data generated by five cohorts (> 400 students) were studied.Median reading participation was very high -over 96% of 290 clicks per student -for all five cohorts.In addition, animation view rates were as high as 118%, indicating repetitive use.Animation view rates were higher for more advanced topics, such as double interpolation, compared to the basic skills and formulas.A median completion over 94% on auto-graded problems was observed for each of the five cohorts.By examining fraction correct on specific topics, real-time misconceptions and struggle can be noted by instructors, which leads to opportunities to provide interventions and facilitate learning.


Geometric Aspects of Mixed Quantum States Inside the Bloch Sphere

Abstract: When studying the geometry of quantum states, it is acknowledged that mixed states can be distinguished by infinitely many metrics. Unfortunately, this freedom causes metric-dependent interpretations of physically significant geometric quantities such as the complexity and volume of quantum states. In this paper, we present an insightful discussion on the differences between the Bures and the Sjöqvist metrics inside a Bloch sphere. First, we begin with a formal comparative analysis between the two metrics by critically discussing three alternative interpretations for each metric. Second, we explicitly illustrate the distinct behaviors of the geodesic paths on each one of the two metric manifolds. Third, we compare the finite distances between an initial state and the final mixed state when calculated with the two metrics. Interestingly, in analogy with what happens when studying the topological aspects of real Euclidean spaces equipped with distinct metric functions (for instance, the usual Euclidean metric and the taxicab metric), we observe that the relative ranking based on the concept of a finite distance between mixed quantum states is not preserved when comparing distances determined with the Bures and the Sjöqvist metrics. Finally, we conclude with a brief discussion on the consequences of this violation of a metric-based relative ranking on the concept of the complexity and volume of mixed quantum states.


Terrestrial Laser Scanning for Fast Spatially Resolved Cleanliness Assessment of Heliostat Fields

Abstract: This paper presents a novel method for the assessment of cleanliness levels (i.e. specular relative reflectance) in the solar field by use of laser scanning. The detected backscattered laser intensity caused by soil and dust, on the otherwise specular mirror surfaces, can be used for determining the cleanliness of mirrors in the solar field with an excellent spatial resolution and acquisition speed. The backscatter behavior of soiled mirrors with distance and incident angle is analyzed and used for transferring the raw measurements values to a reference distance and incident angle. The experimental results of the novel laser scanning measurement technique as well as new insights on soiling behavior are presented. First, the calibration measurements with artificially soiled mirror samples are analyzed, followed by an extensive measurement campaign in a heliostat field. A correlation for the backscatter power and the cleanliness is found, and the first spatially resolved cleanliness measurement for a heliostat field is shown, successfully validating the measurement principle.


Effects of Different Leadership Styles on Cognitive Engagement in Online Collaborative Learning

Abstract: The collaborative learning approach as a universal teaching strategy is widely used in online learning. It is proven that the group leader has an important impact on group collaborative knowledge construction in online collaborative learning (OCL). However, limited research is available on how leadership styles influence a group universal teaching strategy is widely used in online learning this study, the authors adopted lag sequential analysis, epistemic network analysis, and social network analysis to explore the influence of divergent and convergent leadership styles on cognitive engagement in OCL groups. Compared with convergent leadership, divergent leadership strengthened online collaborative cognitive engagement through significant organizer and manager roles, triggered high-quality cognitive behavior transformation within the group, and promoted the balanced development of learners' cognitive structure.


Learning from Machine Learning and Teaching with Machine Teaching: Using Lessons from Data Science to Enhance Collegiate Classrooms

Abstract: Engineering in 2013 from the University of Colorado at Boulder.My pedagogical research interests are on improving the quality of collegiate classroom environments through the use of nontraditional techniques and active participation by instructors.These include the


A Primer on Generative Artificial Intelligence

Abstract: Many educators and professionals in different industries may need to become more familiar with the basic concepts of artificial intelligence (AI) and generative artificial intelligence (Gen-AI). Therefore, this paper aims to introduce some of the basic concepts of AI and Gen-AI. The approach of this explanatory paper is first to introduce some of the underlying concepts, such as artificial intelligence, machine learning, deep learning, artificial neural networks, and large language models (LLMs), that would allow the reader to better understand generative AI. The paper also discusses some of the applications and implications of generative AI on businesses and education, followed by the current challenges associated with generative AI.


Abstract: Salient Object Detection (SOD) is gradually applied in natural scene images. However, due to the apparent differences between optical remote sensing images and natural scene images, directly applying the SOD of natural scene images to optical remote sensing images has limited performance in global context information. Therefore, salient object detection in optical remote sensing images (ORSI-SOD) is challenging. Optical remote sensing images usually have large-scale variations. However, the vast majority of networks are based on Convolutional Neural Network (CNN) backbone networks such as VGG and ResNet, which can only extract local features. To address this problem, we designed a new model that employs a transformer-based backbone network capable of extracting global information and remote dependencies. A new framework is proposed for this question, named Global and Multiscale Aggregate Network for Saliency Object Detection in Optical Remote Sensing Images (GMANet). In this framework, the Pyramid Vision Transformer (PVT) is an encoder to catch remote dependencies. A Multiscale Attention Module (MAM) is introduced for extracting multiscale information. Meanwhile, a Global Guiled Brach (GGB) is used to learn the global context information and obtain the complete structure. Four MAMs are densely connected to this GGB. The Aggregate Refinement Module (ARM) is used to enrich the details of edge and low-level features. The ARM fuses global context information and encoder multilevel features to complement the details while the structure is complete. Extensive experiments on two public datasets show that our proposed framework GMANet outperforms 28 state-of-the-art methods on six evaluation metrics, especially E-measure and F-measure. It is because we apply a coarse-to-fine strategy to merge global context information and multiscale information.


Modeling Low- and High-Frequency Noise in Transmon Qubits with Resource-Efficient Measurement

Abstract: Transmon qubits experience open-system effects that manifest as noise at a broad range of frequencies. We present a model of these effects using the Redfield master equation with a hybrid bath consisting of low- and high-frequency components. We use two-level fluctuators to simulate 1/f-like noise behavior, which is a dominant source of decoherence for superconducting qubits. By measuring quantum state fidelity under free evolution with and without dynamical decoupling (DD), we can fit the low- and high-frequency noise parameters in our model. We train and test our model using experiments on quantum devices available through IBM quantum experience. Our model accurately predicts the fidelity decay of random initial states, including the effect of DD pulse sequences. We compare our model with two simpler models and confirm the importance of including both high frequency and 1/f noise in order to accurately predict transmon behavior.3 MoreReceived 16 March 2023Revised 26 October 2023Accepted 16 January 2024DOI:https://doi.org/10.1103/PRXQuantum.5.010320Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasOpen quantum systems & decoherenceQuantum computationQuantum controlQuantum Information, Science & Technology


Financial Anti-Fraud Based on Dual-Channel Graph Attention Network

Abstract: This article addresses the pervasive issue of fraud in financial transactions by introducing the Graph Attention Network (GAN) into graph neural networks. The article integrates Node Attention Networks and Semantic Attention Networks to construct a Dual-Head Attention Network module, enabling a comprehensive analysis of complex relationships in user transaction data. This approach adeptly handles non-linear features and intricate data interaction relationships. The article incorporates a Gradient-Boosting Decision Tree (GBDT) to enhance fraud identification to create the GBDT–Dual-channel Graph Attention Network (GBDT-DGAN). In a bid to ensure user privacy, this article introduces blockchain technology, culminating in the development of a financial anti-fraud model that fuses blockchain with the GBDT-DGAN algorithm. Experimental verification demonstrates the model’s accuracy, reaching 93.82%, a notable improvement of at least 5.76% compared to baseline algorithms such as Convolutional Neural Networks. The recall and F1 values stand at 89.5% and 81.66%, respectively. Additionally, the model exhibits superior network data transmission security, maintaining a packet loss rate below 7%. Consequently, the proposed model significantly outperforms traditional approaches in financial fraud detection accuracy and ensures excellent network data transmission security, offering an efficient and secure solution for fraud detection in the financial domain.


Lip-Reading Advancements: A 3D Convolutional Neural Network/Long Short-Term Memory Fusion for Precise Word Recognition

Abstract: Lip reading, the art of deciphering spoken words from the visual cues of lip movements, has garnered significant interest for its potential applications in diverse fields, including assistive technologies, human–computer interaction, and security systems. With the rapid advancements in technology and the increasing emphasis on non-verbal communication methods, the significance of lip reading has expanded beyond its traditional boundaries. These technological advancements have led to the generation of large-scale and complex datasets, necessitating the use of cutting-edge deep learning tools that are adept at handling such intricacies. In this study, we propose an innovative approach combining 3D Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to tackle the challenging task of word recognition from lip movements. Our research leverages a meticulously curated dataset, named MobLip, encompassing various speech patterns, speakers, and environmental conditions. The synergy between the spatial information extracted by 3D CNNs and the temporal dynamics captured by LSTMs yields impressive results, achieving an accuracy rate of up to 87.5%, showcasing robustness to lighting variations and speaker diversity. Comparative experiments demonstrate our model’s superiority over existing lip-reading approaches, underlining its potential for real-world deployment. Furthermore, we discuss ethical considerations and propose avenues for future research, such as multimodal integration with audio data and expanded language support. In conclusion, our 3D CNN-LSTM architecture presents a promising solution to the complex problem of word recognition from lip movements, contributing to the advancement of communication technology and opening doors to innovative applications in an increasingly visual world.


Automated Classification of User Needs for Beginner User Experience Designers: A Kano Model and Text Analysis Approach Using Deep Learning

Abstract: This study introduces a novel tool for classifying user needs in user experience (UX) design, specifically tailored for beginners, with potential applications in education. The tool employs the Kano model, text analysis, and deep learning to classify user needs efficiently into four categories. The data for the study were collected through interviews and web crawling, yielding 19 user needs from Generation Z users (born between 1995 and 2009) of LEGO toys (Billund, Denmark). These needs were then categorized into must-be, one-dimensional, attractive, and indifferent needs through a Kano-based questionnaire survey. A dataset of over 3000 online comments was created through preprocessing and annotating, which was used to train and evaluate seven deep learning models. The most effective model, the Recurrent Convolutional Neural Network (RCNN), was employed to develop a graphical text classification tool that accurately outputs the corresponding category and probability of user input text according to the Kano model. A usability test compared the tool’s performance to the traditional affinity diagram method. The tool outperformed the affinity diagram method in six dimensions and outperformed three qualities of the User Experience Questionnaire (UEQ), indicating a superior UX. The tool also demonstrated a lower perceived workload, as measured using the NASA Task Load Index (NASA-TLX), and received a positive Net Promoter Score (NPS) of 23 from the participants. These findings underscore the potential of this tool as a valuable educational resource in UX design courses. It offers students a more efficient and engaging and less burdensome learning experience while seamlessly integrating artificial intelligence into UX design education. This study provides UX design beginners with a practical and intuitive tool, facilitating a deeper understanding of user needs and innovative design strategies.


Needs Analysis and Requirements Delivery: Guiding the Development Approach with an Intuitive Framework

Abstract: Abstract The needs assessment and requirements engineering domains involve tasks that help us fully understand a project's motivation. The functions of this domain include exploring the problem or opportunity and recommending a possible solution and development approach to the problem. From a project perspective, this is a critical stage as we can understand the value a viable solution brings to the customer and identify an appropriate development approach. A business investing resources to develop a solution must be pragmatic about the product development approach. Our research paper presents a framework to aid decision-making within this context. It involves considering attributes such as delivery cadence, risk, degree of innovation, and requirements certainty. Knowledge of the impact of these attributes on project outcomes will help educate engineers to become successful entrepreneurs and product managers.


Exploring Virtual Reality as a Design Observation Training Tool for Engineering Students

Abstract: Abstract Observation is an essential tool in human-centered engineering design. Observations allow engineering designers to collect rich data on design problem environments and stakeholder behaviors in ways that are not always possible with other methods, as well as allow for the identification of discrepancies in other data (e.g., verbal reports from stakeholders) (Blomberg & Burrel, 2009). Developing design observation skills presents challenges, however, as observation often involves uncontrolled environments and the management of significant levels of uncertainty, which engineering students have been shown to struggle with (Jordan, 2015). Practical observation experience may also require extra effort for students to reach observation locations outside the classroom. In addition, students may be confronted with high-pressure or sensitive observation environments like medical facilities in co-curricular and extracurricular design projects and internships, which are likely to be especially difficult to prepare for in a classroom. Virtual reality (VR) has been promoted as a promising design training tool for its ability to provide controlled, yet immersive access to scenarios that are otherwise difficult to replicate in a classroom (Özgen et al., 2021). There is mixed empirical evidence on the benefits and drawbacks of VR-based training simulations in higher education (Merchant et al., 2013), however, and no studies have characterized the use of VR for engineering design observation training. To explore the effectiveness of VR for supporting engineering design observation training, this study explored students' perceptions of a prototype VR-based design observation training tool. Four undergraduate engineering students were each interviewed twice: once after the VR training experience and again after in-person design observation practice within a clinical setting during a co-curricular design program. Approximately six hours of data were collected and analyzed inductively to characterize student perceptions of advantages, disadvantages, and effectiveness of VR as a training tool compared to conventional classroom training (i.e., lecture and in-classroom practice). We found that student participants reported VR-based design observation training to be a more engaging format than other classroom-based training formats, such as lectures or two-dimensional videos. Participants also reported that VR-based training did not fully prepare them for responding to the social queues and interactions present in in-person design observation experiences, however. Preliminary recommendations for VR-based observation training experiences and related future research are also discussed.


What makes a solar engineer?

Abstract: The international response to climate change has driven growing demand for engineers trained to design solar energy systems and integrate them into electricity infrastructure.There is a need for some guidance as to what training is necessary for future engineers to meet this demand.This study gathers data from literature, industry perspectives and current educational practice to help establish suitable learning objectives for training undergraduate engineers to be prepared for solar project development.In general, results suggest that students be trained to understand the complete workflow of concepts related to design of photovoltaic systems including solar positioning, the solar resource and irradiance data sources, design of a photovoltaic system from both a solar resource and an electrical perspective and performing calculations to model or support validation of photovoltaic systems.Professionals also highlighted the importance of discussing various types of typical financing structures for solar energy systems, though these were less common in existing courses.The paper summarizes these outcomes with a proposed set of learning objectives that encompasses these highlight areas and that could form the backbone of an undergraduate course on solar energy engineering.


Exploring Magic Interactions for Collaboration in Virtual Reality Learning Factory

Abstract: Abstract A hands-on curriculum that blends theory and practical skills is essential to teach manufacturing. An integral part of such a curriculum is a learning factory, which allows engineering students to experience the entire manufacturing cycle of a product in a realistic factory environment. In addition to learning the required technical skills, students can practice their collaborative skills and communication via teamwork in a learning factory. With virtual reality (VR), environments can be made using game engines that simulate their real-world equivalents, providing realistic experiences. Compared to traditional remote learning, VR-based learning together with online remote learning is experiential, allows for natural interaction, and is only limited by the capabilities of the hardware running the virtual environments. The cost of VR devices has dramatically reduced with standalone VR devices such as Meta Quest 2, making these devices a compelling option for specialized educational simulations. A VR Learning Factory should support synchronous collaboration of multiple learners in the same environment. This is a critical advantage of using VR, since collaboration is an essential skill for engineers. To maximize this benefit, it is imperative to develop an appropriate VR interaction mode, because it can greatly influence the effectiveness of collaborations. In this research, we explore multi-user interaction within the context of the VR Learning Factory and compare two modes of virtual user interaction that we call natural and magic. Magic interactions include three additional tools: object container, holographic representation, and multi-object selection. We conduct an analysis of the two modes of VR interaction in a craft production task and show increased performance of using magic interactions.


Speech Melody Research as the Interdisciplinary Foundation of the Petrograd Institute of the Living Word

Abstract: The assumption of similarity between artistic speech melody and music was deeply rooted in Russian Symbolism and based on the culturally established analogy between poetry/lyrical prosody and music. This connection was the basis for a wide range of performative practices focused on performed word such as the experiments of director Vsevolod Meyerhold and composer Mikhail Gnesin in Petrograd theater studios in 1900–1910s, and the collective declamation of Vasilii Serezhnikov and Vsevolod Vsevolodskii-Gerngross. However, after the October revolution, this analogy not only inspired new artistic paths, but also new approaches in humanities. This article explores the correlation between a practice-based strategy and advanced theory that characterized the structure and curricula of the Petrograd Institute of the Living Word (Institut zhivogo slova; 1918–1924). Its specific institutional features affected the development of disciplines in the fields of linguistics, poetics, and literary studies. The earlier period of its work (1918–1921) was defined by the search for common ground, which could unite representatives of different disciplines. The study of the melody of speech, which this article is focused on, became one of the key joint research projects of the Institute’s team. It is the perspective of the Institute of the Living Word’s research projects and performance-related art practices that is used for analysis of the Russian Formalist approaches in the 1910s–20s, specifically articles and books of philologist Boris Eikhenbaum on the melody and composition of verse intonation.


Brain-driven facial image reconstruction via StyleGAN inversion with improved identity consistency

Abstract: The reconstruction of visual stimuli from fMRI data represents a major technological and scientific challenge at the forefront of contemporary neuroscience research. Deep learning techniques have played a critical role in advancing decoding models for visual stimulus reconstruction from fMRI data. Particularly, the use of advanced GANs has resulted in significant improvements in the quality of image generation, providing a powerful tool for addressing the challenges of this complex task. However, none of these studies have taken into account the inherent characteristics of the stimulus contents themselves; This, in turn, leads to unsatisfactory outcomes, as demonstrated by the inconsistent identity between reconstructed faces and ground truth in the decoding of facial images. In order to tackle this challenge, we introduce a new framework aimed at enhancing the accuracy of reconstructing facial images from fMRI data. Our key innovation involves extracting and disentangling multi-level visual information from brain signals in the latent space and optimizing high-level features for facial identity control using identity loss. Specifically, our framework uses StyleGAN inversion to extract hierarchical latent codes from images, which are then bridged to fMRI data through transformation blocks. Additionally, we introduce a multi-stage refinement method to enhance the accuracy of reconstructed faces, which involves progressively updating fMRI latent codes with custom loss functions designed for both feature- and image-wise optimization. Our experimental results demonstrate that our proposed framework effectively achieves two critical objectives: (1) accurate facial image reconstruction from fMRI data and (2) preservation of identity characteristics with a high level of consistency.


An Abnormal External Link Detection Algorithm Based on Multi-Modal Fusion

Abstract: Website link detection is an important means to ensure the security of the external chain. In the past, it was mainly realized through blacklisting and feature engineering-based machine learning, which has the problems of slow detection speed and weak model generalization ability. The development of neural networks has brought a new solution to the security detection of the external chain of the website. To address the performance bottleneck caused by the variable content length of web pages, this article introduces an innovative approach: a website external link security detection algorithm based on multi-modal fusion. It extracts text, dynamic script, and image features separately, and constructs a deep fusion model that combines these multi-modal features. Compared with the previous research results, the proposed method is superior to the traditional single-mode method, and can quickly and accurately identify malicious web pages. The accuracy and F1 value are improved by 2.7% and 0.026.


Building bonds: an examination of relational bonding in continuous content contribution behaviors on metaverse-based non-fungible token platforms

Abstract: Purpose The proliferation of non-fungible token (NFT)-based crypto-art platforms has transformed how creators manage, own and earn money through the creation, assets and identity of their digital works. Despite this, no studies have examined the drivers of continuous content contribution behavior (CCCB) toward NFTs. Hence, this study draws on the theory of relational bonds to examine how various relational bonds affect feelings of psychological ownership, which, in turn, affects CCCB on metaverse platforms. Design/methodology/approach Using structural equation modeling and importance-performance matrix analysis, an online survey of 434 content creators from prominent NFT platforms empirically validated the research hypotheses. Findings Financial, structural, and social bonds positively affect psychological ownership, which in turn encourages CCCBs. The results of the importance-performance matrix analysis reveal that male content creators prioritized virtual reputation and social enhancement, whereas female content creators prioritized personalization and monetary gains. Originality/value We examine Web 3.0 and the NFT creators’ network that characterizes the governance practices of the metaverse. Consequently, the findings facilitate a better understanding of creator economy and meta-verse commerce.


Quantum steering and coherence evolution of two atoms under noisy environments

Abstract: By considering two kinds of non-Markovian (NM) environments, the dynamical behavior of quantum steering, Bell nonlocality and coherence of a system of two atoms initially immersed in a vacuum has been studied. In this context, the cases of a cavity little off-resonance with the atomic transition frequency and non-perfect photonic band-gap (NPPBG) has been considered. The control and preservation of the quantumness measures is discussed in terms of the physical parameters. Furthermore, the situations for which the atoms state is steerable with the presence of Bell nonlocality and coherence has been illustrated. Finally, the monotonic dependence among the quantumness measures is displayed with respect to the model parameters.


On a discrete symmetric optimal associated kernel for estimating count data distributions

Abstract: The nonparametric estimator using discrete kernels is one competing alternative to the frequency estimator. We investigate a discrete symmetric “optimal” kernel. Its properties are established and studied by simulations. An application on real count data is given.


Y-GAN: Learning dual data representations for anomaly detection in images

Abstract: We propose a novel reconstruction-based model for anomaly detection in image data, called’Y-GAN’. The model consists of a Y-shaped auto-encoder and represents images in two separate latent spaces. The first captures meaningful image semantics, which are key for representing (normal) training data, whereas the second encodes low-level residual image characteristics. To ensure the dual representations encode mutually exclusive information, a disentanglement procedure is designed around a latent (proxy) classifier. Additionally, a novel representation-consistency mechanism is proposed to prevent information leakage between the latent spaces. The model is trained in a one-class learning setting using only normal training data. Due to the separation of semantically-relevant and residual information, Y-GAN is able to derive informative data representations that allow for efficacious anomaly detection across a diverse set of anomaly detection tasks. The model is evaluated in comprehensive experiments with several recent anomaly detection models using four popular image datasets, i.e., MNIST, FMNIST, CIFAR10, and PlantVillage. Experimental results show that Y-GAN outperforms all tested models by a considerable margin and yields state-of-the-art results. The source code for the model is made publicly available at https://github.com/MIvanovska/Y-GAN.


Self-Distillation and Pinyin Character Prediction for Chinese Spelling Correction Based on Multimodality

Abstract: Chinese spelling correction (CSC) constitutes a pivotal and enduring goal in natural language processing, serving as a foundational element for various language-related tasks by detecting and rectifying spelling errors in textual content. Numerous methods for Chinese spelling correction leverage multimodal information, including character, character sound, and character shape, to establish connections between incorrect and correct characters. Research indicates that a majority of spelling errors stem from pinyin similarity, with character similarity accounting for half of the errors. Consequently, effectively modeling character pinyin and character relationships emerges as a key challenge in the CSC task. In this study, we propose enhancing the CSC task by introducing the pinyin character prediction task. We employ an adaptive weighting method in the pinyin character prediction task to address predictions in a more granular manner, achieving a balance between the two prediction tasks. The proposed model, SPMSpell, utilizes ChineseBERT as an encoder to capture multimodal feature information simultaneously. It incorporates three parallel decoders for character prediction, pinyin prediction, and self-distillation modules. To mitigate potential overfitting concerning pinyin, a self-distillation method is introduced to prioritize character information in predictions. Extensive experiments conducted on three SIGHAN benchmark tests showcase that the model introduced in this paper attains a superior level of performance. This substantiates the correctness and superiority of the adaptive weighted pinyin character prediction task and underscores the effectiveness of the self-distillation module.


GPONet: A Two-Stream Gated Progressive Optimization Network for Salient Object Detection

Abstract: The salient object detection task is to locate and detect salient regions in images, which is widely applied in various fields. In this paper, we propose a gated progressive optimization network (GPONet) for salient object detection. Firstly, to extract salient regions more accurately, we design a multi-level feature fusion module with a gating mechanism named gate fusion network (GFN). GFN focuses on the semantic information of high-level features as well as the detailed information of low-level features, enabling purposeful delivery of high-level features to low-level features. The gate fusion unit (GFU) is also able to maintain valid information and suppress redundant information in the fusion process. Secondly, while some existing methods have shown that the additional edge supervision can facilitate salient object detection, edge pixels are often much less common than non-edge pixels, leading to the challenge of class imbalance. To overcome this issue, we introduce detail labels that provide additional internal details as a supplementary supervisory signal. Combining these labels with proposed Detail Perception Loss enables our network to learn edge information of salient objects more effectively. To complement each other and guide information exchange between the two branches, we propose a cross guide module (CGM) to control the information flow transfer between them. Finally, we develop a simple and efficient attention fusion strategy to merge the prediction maps of the two branches to generate the final salient prediction map. Extensive experimental results validate that our method reaches optimal or comparable performance on several mainstream datasets. The code of GPONet is available from https://github.com/antonie-z/GPONet


The Future of Competitive Advantage in Oman: Integrating Green Product Innovation, AI, and Intellectual Capital in Business Strategies

Abstract: This study delves into the dynamics of green product innovation, artificial intelligence (AI) adaption, and intellectual capital, investigating their impact on the competitiveness of firms in Oman. It emphasizes the crucial role of government intervention and R&D investments in this process. Based on the responses of 214 top managers in Oman, the research employs structural equation modeling to analyze the intricate relationships between these factors. The findings underscore a significant positive correlation between green innovation, AI implementation, and intellectual capital, with government involvement and R&D investments as vital moderators. This study provides a novel perspective on the synergy of technology, innovation, and intellectual capital in developing economies. It offers essential insights for business leaders, policymakers, and scholars, highlighting the necessity of integrating advanced technologies and sustainable practices in business strategies to achieve competitive advantage. The research adds to the existing body of knowledge on innovation and competitiveness. It offers practical implications for enhancing firm performance in Oman and similar emerging markets.


DeepCover: Advancing RNN test coverage and online error prediction using state machine extraction

Abstract: Recurrent neural networks (RNNs) have emerged as powerful tools for processing sequential data in various fields, including natural language processing and speech recognition. However, the lack of explainability in RNN models has limited their interpretability, posing challenges in understanding their internal workings. To address this issue, this paper proposes a methodology for extracting a state machine (SM) from an RNN-based model to provide insights into its internal function. The proposed SM extraction algorithm was assessed using four newly proposed metrics: Purity, Richness, Goodness, and Scale. The proposed methodology along with its assessment metrics contribute to increasing explainability in RNN models by providing a clear representation of their internal decision making process through the extracted SM. In addition to improving the explainability of RNNs, the extracted SM can be used to advance testing and and monitoring of the primary RNN-based model. To enhance RNN testing, we introduce six model coverage criteria based on the extracted SM, serving as metrics for evaluating the effectiveness of test suites designed to analyze the primary model. We also propose a tree-based model to predict the error probability of the primary model for each input based on the extracted SM. We evaluated our proposed online error prediction approach using the MNIST dataset and Mini Speech Commands dataset, achieving an area under the curve (AUC) exceeding 80% for the receiver operating characteristic (ROC) chart.


Confidence Intervals for Parameters of Unobserved Events

Abstract: Consider a finite sample from an unknown distribution over a countable alphabet. Unobserved events are alphabet symbols which do not appear in the sample. Estimating the probabilities of unobserved events is a basic problem in statistics and related fields, which was extensively studied in the context of point estimation. In this work we introduce a novel interval estimation scheme for unobserved events. Our proposed framework applies selective inference, as we construct confidence intervals (CIs) for the desired set of parameters. Interestingly, we show that obtained CIs are dimension-free, as they do not grow with the alphabet size. Further, we show that these CIs are (almost) tight, in the sense that they cannot be further improved without violating the prescribed coverage rate. We demonstrate the performance of our proposed scheme in synthetic and real-world experiments, showing a significant improvement over the alternatives. Finally, we apply our proposed scheme to large alphabet modeling. We introduce a novel simultaneous CI scheme for large alphabet distributions which outperforms currently known methods while maintaining the prescribed coverage rate.


Global panel data on World Governance and State Fragility from 2006 to 2022

Abstract: This global panel dataset contains information collected from two different sources (The Fund for Peace and World Bank), on the governance and stability levels of 178 countries between 2006 and 2022. The dataset includes information on 1) Cohesion (security apparatus (C1), factionalized elites (C2), and group grievance (C3)), 2) Economic (economy (E1), economic inequality (E2), and human flight and brain drain (E3)), 3) Political (state legitimacy (P1), public services (P2), and human rights (P3)) indicators, 4) Social and cross-cutting (demographic pressures (S1), refugees and internally displaced persons (S2), and external intervention (X1)), and 5) Governance (voice and accountability (G1), political stability and absence of violence/terrorism (G2), government effectiveness (G3), regulatory quality (G4), rule of law (G5), and control of corruption (G6)). Data analysis was carried out using SPSS version 29 software to ensure a complete description of the data (labels, type and measure of variables, and uniformity of decimals), as well as the imputation possibility of missing data, which will allow future researchers to study both cross-sectional and longitudinal relationships between the five types of indicators and the eighteen indicators reported.


The relevance of media coverage and sentiment for small and large scale bitcoin investors

Abstract: We provide a novel perspective on the Bitcoin market, investigating determinants of investor positions and their response to public information proxied by sentiment indicators. We distinguish between investors by size and observe their respective behaviour concerning incoming information. We find that price dynamics and media coverage lead to different decisions depending on the Bitcoin portfolio size. Retail investors react strongly to incoming public information and media narratives, with their decisions strongly influenced by sentiment and media attention. Conversely, the response of large-scale investors to such information is much weaker because they arguably have different, non-public information and divergent investment objectives.


Intelligent Customer Service System Optimization Based on Artificial Intelligence

Abstract: To elevate the intelligence of customer service dialogue systems, this article proposes an intelligent customer service system comprising chat dialogue subsystems, task-oriented multi-turn dialogue subsystems, single-turn dialogue subsystems, and an integration model. Firstly, to enhance diversity of responses and improve user experience, particularly in casual chat scenarios, this article presents a Seq2Seq-based approach for multi-answer responses, allowing for more expressive emotional expression in responses. Secondly, to address situations where customers cannot articulate their needs in a single sentence during multi-turn dialogues, this article designs a task-oriented multi-turn dialogue module. It employs intent recognition and slot filling to maintain contextual information throughout the conversation, aiding customers in problem resolution. Lastly, to overcome the current limitation of intelligent customer service models providing relatively one-dimensional answers in specific domains.


Regularized Discrete Optimal Transport for Class-Imbalanced Classifications

Abstract: Imbalanced class data are commonly observed in pattern analysis, machine learning, and various real-world applications. Conventional approaches often resort to resampling techniques in order to address the imbalance, which inevitably alter the original data distribution. This paper proposes a novel classification method that leverages optimal transport for handling imbalanced data. Specifically, we establish a transport plan between training and testing data without modifying the original data distribution, drawing upon the principles of optimal transport theory. Additionally, we introduce a non-convex interclass regularization term to establish connections between testing samples and training samples with the same class labels. This regularization term forms the basis of a regularized discrete optimal transport model, which is employed to address imbalanced classification scenarios. Subsequently, in line with the concept of maximum minimization, a maximum minimization algorithm is introduced for regularized discrete optimal transport. Subsequent experiments on 17 Keel datasets with varying levels of imbalance demonstrate the superior performance of the proposed approach compared to 11 other widely used techniques for class-imbalanced classification. Additionally, the application of the proposed approach to water quality evaluation confirms its effectiveness.


Overview and empirical analysis of wealth decentralization in blockchain networks

Abstract: The decentralization paradigm has made blockchain one of the most disruptive technologies today. When evaluating the level of decentralization, the key metric for most public blockchain networks is the degree of decentralization of the resources responsible for determining who generates the blocks. In turn, it facilitates a greater understanding of both security and scalability on a blockchain. This work provides an overview of the current state-of-the-art on wealth decentralization, which has not yet received the attention it deserves. We collect data, calculate various wealth decentralization metrics, and compare our results with research on the same methodology. As the amount of data for various blockchains increases rapidly, it is helpful to have techniques to aggregate data for statistical analysis. We introduce and provide conservative estimates of decentralized group metrics based on the reduced data and compare them with full-data measurements. Our research considers both the Layer 1 blockchains of Bitcoin and Ethereum, along with Layer 2 blockchains such as Arbitrum, Optimism, and Polygon.


Projective Synchronization of Inertial Quaternion-Valued Neural Networks via Non-reduced Order Approach

Abstract: Abstract In this article, the issue on projective synchronization of delayed inertial quaternion-valued neural networks (IQVNNs) is investigated. Different from most existing literature, we adopt the non-reduced order approach to deal with IQVNNs described by second order differential equations. By introducing a novel Lyapunov functional, several sufficient criteria are presented in component form to ensure the projective synchronization between master–slave systems. A numerical experiment demonstrates the feasibility of control strategy as well as the correctness of theoretical results.


Innovating Sustainability

Abstract: In today's global society, carbon neutrality has become a focal point of concern. Greenhouse gas emissions and rising atmospheric temperatures are triggering various extreme weather events, sea level rise, and ecological imbalances. These changes not only affect the stability and sustainable development of human society but also pose a serious threat to the Earth's ecosystems and biodiversity. Faced with this global challenge, finding effective solutions has become urgent. This article aims to propose a comprehensive artificial intelligence design approach to address issues related to carbon neutrality. This method integrates technologies from fields such as computer vision, natural language processing, and deep learning to achieve a comprehensive understanding of environmental conservation and innovative solutions. Specifically, the authors first use a visual module to extract features from images, which helps capture important information in the images. Next, we employ the ALBEF model for cross-modal alignment, enabling better collaboration between images and textual information.


The Dynamic Connectedness Between Environmental Attention and Green Cryptocurrency

Abstract: The results indicate a dynamic pattern of interconnectedness throughout history. Based on the findings, the transmission of volatility exhibited a higher magnitude during the period of COVID-19. The issue of high transmission volatility due to limited diversification options concerns investors, green stakeholders, and policymakers alike. This article proposes various potential areas for future research. The ICEA index can potentially assist businesses operating in environmentally sensitive sectors make well-informed policy decisions. It includes sectors such as environmental green bonds, and commodities. Consideration should be given to implementing blockchain technology, as it can consume less power in this particular scenario. By employing a time-frequency paradigm, this study is able to incorporate the investment horizon, a crucial factor to be taken into account when making financial judgments. The advancement of this research could be facilitated by directing our attention toward the implications of our findings on portfolios and developing appropriate measures for their evaluation.


Feature Extraction of Dialogue Text Based on Big Data and Machine Learning

Abstract: In this article, a dialogue text feature extraction model based on big data and machine learning is constructed, which transforms the high-dimensional space of text features into the low-dimensional space that is easy to process, so that the best feature words can be selected to represent the document set. Tests show that in most cases, the classification accuracy of this model is higher than 88%, and the recall rate is higher than 85%, thus achieving the goal of higher classification accuracy with less computation. When extracting the features of dialogue texts, there is no need for preprocessing, just count the data such as lexical composition, sentence length and sentence-to-sentence relationship of the target text, and make linear analysis to obtain key indicators and weights. Based on this, the classification model can achieve good results, thus effectively reducing the workload and computation of text classification.


APPLICATION OF ARTIFICIAL INTELLIGENCE SYSTEMS FOR RECOGNITION AND EVALUATION OF MOTOR SKILLS AND TECHNIQUES IN HOCKEY

Abstract: A methodology and tools were developed for recognizing and evaluating the quality of performing techniques and skills in hockey using artificial intelligence methods.


Finding Success in Adapting Repeated Microteaching Rehearsals (RMTR) for an Online Science Methods Course

Abstract: In February/March 2020, postsecondary educators worldwide were required to suddenly shift from face-to-face to online instruction due to the COVID-19 pandemic. For teacher educators, this meant reimagining how to enact core pedagogical strategies, such as teaching rehearsals for online instruction. This chapter shares my experiences of integrating repeated microteaching rehearsals (RMTR) into my elementary science methods courses before the COVID-19 pandemic to disastrous results. Then, adapting RMTR for an online learning environment due to the rapid shift from face-to-face to online learning during the pandemic and its successful integration as an online practice teaching strategy. This chapter also shares how RMTR was and continues to be implemented in my post-pandemic science methods course and the lessons learned along the way.


Trajectory optimization with hybrid probabilistic roadmap approach to achieve time efficient navigation of unmanned vehicles in unstructured environment

Abstract: Purpose This paper aims to focus on solving the path optimization problem by modifying the probabilistic roadmap (PRM) technique as it suffers from the selection of the optimal number of nodes and deploy in free space for reliable trajectory planning. Design/methodology/approach Traditional PRM is modified by developing a decision-making strategy for the selection of optimal nodes w.r.t. the complexity of the environment and deploying the optimal number of nodes outside the closed segment. Subsequently, the generated trajectory is made smoother by implementing the modified Bezier curve technique, which selects an optimal number of control points near the sharp turns for the reliable convergence of the trajectory that reduces the sum of the robot’s turning angles. Findings The proposed technique is compared with state-of-the-art techniques that show the reduction of computational load by 12.46%, the number of sharp turns by 100%, the number of collisions by 100% and increase the velocity parameter by 19.91%. Originality/value The proposed adaptive technique provides a better solution for autonomous navigation of unmanned ground vehicles, transportation, warehouse applications, etc.


Capital flight, institutional quality and real sector in sub-Saharan African countries

Abstract: Purpose This study aims to provide an empirical investigation into rising capital flight and the role of institutional quality to mitigate its effect on the real sector in sub-Saharan Africa (SSA). Design/methodology/approach The study uses the system generalized method of moments and uses data spanning from 1989 to 2020 from 26 SSA countries. Findings The findings show that capital flight has no direct impact on the real sector while institutional quality adversely impacted the agricultural and industrial sectors. The study also found that institutional quality is unable to mitigate the effect of capital flight on the industrial sector. Originality/value This study investigates if institutional quality mitigates the impact of capital flight on the real sector proxied by industrial value-added and agriculture value-added.


Linguocognitive Approach to Text Analysis

Abstract: Cognitive sciences were officially included in the nomenclature of scientific specialties in Russia in 2021. However, the issue of the subject and method of cognitive science in general and its projection on text analysis in particular remains little known to the general scientific community. Linguistic text analysis has gone through several stages of evolution. Each of them accumulates the critical aspects highlighting the necessity to search for new approaches. There are three such approaches in linguistics: structural, communicative, cognitive. The structural paradigm made it possible to solve scientific problems using the example of simple language units. However, it was experiencing serious difficulties in studying complex emergent phenomena, to which the text belongs. Some of these difficulties were leveled by including a communicative approach in the linguist’s methodological baggage. At the same time, a significant layer of factors influencing on the meaning generation of the text remained outside the scope of the research focus. As a result, the linguocognitive approach to text analysis was formed in linguistics. This made it possible to include not only linguistic structures, but also the widest range of mental structures (concepts, frames and slots, prototypes, categories, conceptual oppositions, gestalts, mental spaces and models, conceptual metaphors, precedent phenomena, semantic networks) and cognitive operations (conceptualization, categorization, inference, causal attribution, conceptual projections, blending) in the analysis. Thus, the linguocognitive approach to text analysis increases the heuristics of scientific research due to it is aimed at studying of semantic processes as mental processes.


Full Spatial Characterization of Entangled Structured Photons

Abstract: Vector modes are fully polarized modes of light with spatially varying polarization distributions, and they have found widespread use in numerous applications such as microscopy, metrology, optical trapping, nanophotonics, and communications. The entanglement of such modes has attracted significant interest, and it has been shown to have tremendous potential in expanding existing applications and enabling new ones. However, due to the complex spatially varying polarization structure of entangled vector modes (EVMs), a complete entanglement characterization of these modes remains challenging and time consuming. Here, we have used a time-tagging event camera to demonstrate the ability to completely characterize the entanglement of EVMs. Leveraging the camera's capacity to provide independent measurements for each pixel, we simultaneously characterize the entanglement of approximately $2.6\ifmmode\times\else\texttimes\fi{}{10}^{6}$ modes between a bipartite EVM through measuring only 16 observables in polarization. We reveal that EVMs can naturally generate various polarization-entangled Bell states. This achievement is an important milestone in high-dimensional entanglement characterization of structured light, and it could significantly impact the implementation of related quantum technologies. The potential applications of this technique are extensive, and it could pave the way for advancements in quantum communication, quantum imaging, and other areas where structured entangled photons play a crucial role.


Playing Extensive Games with Learning of Opponent’s Cognition

Abstract: Decision-making is a basic component of agents’ (e.g., intelligent sensors) behaviors, in which one’s cognition plays a crucial role in the process and outcome. Extensive games, a class of interactive decision-making scenarios, have been studied in diverse fields. Recently, a model of extensive games was proposed in which agent cognition of the structure of the underlying game and the quality of the game situations are encoded by artificial neural networks. This model refines the classic model of extensive games, and the corresponding equilibrium concept—cognitive perfect equilibrium (CPE)—differs from the classic subgame perfect equilibrium, since CPE takes agent cognition into consideration. However, this model neglects the consideration that game-playing processes are greatly affected by agents’ cognition of their opponents. To this end, in this work, we go one step further by proposing a framework in which agents’ cognition of their opponents is incorporated. A method is presented for evaluating opponents’ cognition about the game being played, and thus, an algorithm designed for playing such games is analyzed. The resulting equilibrium concept is defined as adversarial cognition equilibrium (ACE). By means of a running example, we demonstrate that the ACE is more realistic than the CPE, since it involves learning about opponents’ cognition. Further results are presented regarding the computational complexity, soundness, and completeness of the game-solving algorithm and the existence of the equilibrium solution. This model suggests the possibility of enhancing an agent’s strategic ability by evaluating opponents’ cognition.


Machine Learning-Based Rapid Epicentral Distance Estimation from a Single Station

Abstract: Abstract Rapid epicentral distance estimation is of great significance for earthquake early warning (EEW). To rapidly and reliably predict epicentral distance, we developed machine learning models with multiple feature inputs for epicentral distance estimation using a single station and explored the feasibility of three machine learning methods, namely, Random Forest, eXtreme Gradient Boosting, and Support Vector Machine, for epicentral distance estimation. We used strong-motion data recorded by the Japanese Kyoshin network within a range of 1° (∼112 km) from the epicenter to train machine learning models. We used 30 features extracted from the P-wave signal as inputs to the machine learning models and the epicentral distance as the prediction target of the models. For the same test data set, within 0.1–5 s after the P-wave arrival, the epicentral distance estimation results of these three machine learning models were similar. Furthermore, these three machine learning methods can obtain smaller mean absolute errors and root mean square errors, as well as larger coefficients of determination (R2), for epicentral distance estimation than traditional EEW epicentral distance estimation methods, indicating that these three machine learning models can effectively improve the accuracy of epicentral distance estimation to a certain extent. In addition, we analyzed the importance of different features as inputs to machine learning models using SHapley additive exPlanations. We found that using the top 15 important features as inputs, these three machine learning models can also achieve good results for epicentral distance estimation. Based on our results, we inferred that the machine learning models for estimating epicentral distance proposed in this study are meaningful in EEW.


Resonant Schrödinger cat states in circuit quantum electrodynamics

Abstract: We propose a fast scheme to generate Schr\"odinger cat states in a superconducting resonator using a continuously driven qubit without resorting to the dispersive regime, two-photon drives, or engineered two-photon dissipation. We provide analysis for when the qubit is on and off resonance from the drive. We extend our analysis to account for a third level in a weakly anharmonic qutrit. We also discuss the case of a strongly anharmonic qutrit. Throughout the paper, we corroborate our analytical results with numerical simulations in the presence of energy relaxation and dephasing of the qubit and resonator using realistic experimental parameters.


Multiplex-free physical reservoir computing with an adaptive oscillator

Abstract: Nonlinear oscillators can often be used as physical reservoir computers, in which the oscillator's dynamics simultaneously performs computation and stores information. Typically, the dynamic states are multiplexed in time, and then machine learning is used to unlock this stored information into a usable form. This time multiplexing is used to create virtual nodes, which are often necessary to capture enough information to perform different tasks, but this multiplexing procedure requires a relatively high sampling rate. Adaptive oscillators, which are a subset of nonlinear oscillators, have plastic states that learn and store information through their dynamics in a human readable form, without the need for machine learning. Highlighting this ability, adaptive oscillators have been used as analog frequency analyzers, robotic controllers, and energy harvesters. Here, adaptive oscillators are considered as a physical reservoir computer without the cumbersome time multiplexing procedure. With this multiplex-free physical reservoir computer architecture, the fundamental logic gates can be simultaneously calculated through dynamics without modifying the base oscillator.


Using Design Thinking to break social barriers: An experience report with former inmates

Abstract: Abstract Design Thinking techniques have been widely used in software requirements elicitation to understand the necessities of stakeholders and end‐users. However, there is a lack of evidence of their effectiveness when applied to guide the development process of a system targeting vulnerable populations. What are the implications of using Design Thinking techniques to elicit requirements in a community of former inmates—and what would be the benefits of and challenges in this deployment? In this paper, we report our experience of using Design Thinking for requirements elicitation of a mobile application customized for the former inmates of the Brazilian prison system and their families. We applied techniques such as Brainstorming, Stakeholder Mapping, Personas Creation, Rapid Ethnography, and Interviews to obtain relevant data and create several prototypes. These techniques contribute to the development of an uncommon application that aims to help the reintegration process of former inmates into society. Our results validate the initial hypothesis that such techniques, when applied to a sensitive context, assist product development that meets the end‐users' needs by creating a higher quality product. The main limitation of the research was the lack of access to low‐literacy end‐users and/or former inmates without previous experience using mobile devices.


L2 English speaking syntactic complexity: Data preprocessing issues, reliability of automated analysis, and the effects of proficiency, L1 background, and topic

Abstract: Abstract The effects of learner‐ and task‐related variables on second language (L2) writing syntactic complexity (SC) have been extensively investigated. However, previous research has rarely assessed the reliability of computational tools for analyzing the SC of L2 spoken production, and we know less about the effects of such variables on L2 speaking SC. Using data from the International Corpus Network of Asian Learners of English, this study explores data preprocessing issues for preparing L2 English speech samples for automated SC analysis, evaluates the reliability of L2 Syntactic Complexity Analyzer on preprocessed L2 English speech samples, and examines the effects of proficiency, first language (L1) background, and topic on L2 speaking SC. Our manual analysis of 30 random speech samples identified several issues that can be addressed through preprocessing to improve the accuracy of automated SC analysis. Results from multiple linear mixed‐effects models revealed significant effects of proficiency, L1 background, and topic on the mean length of clause, the number of complex AS‐units per AS‐unit, and the number of dependent clauses and complex nominals per clause in L2 learners’ spoken production. Our findings have useful implications for L2 speaking pedagogy and assessment as well as future L2 speaking SC research.


Organizational Changes in Adopting Agile Approaches: A Systematic Literature Review

Abstract: Although agile approaches to project management were originally developed for use by small software development teams, agile methods quickly began to be used by entire department processes and, in some cases, to the entire organization. Despite this quick adoption, there is a lack of studies seeking a better understanding of the changes that an organization needs to carry out for agile implementation at the organizational level. Therefore, a systematic literature review was conducted to organize and synthesize knowledge in this field. The main changes reported by these studies were consolidated into four major dimensions, that together, provided a consolidated view on the topic. In addition to contributing to the theory of agile approaches, it is also intended that this research will support the organizations that plan to undertake this journey of transformation, helping them plan the actions needed to achieve the desired results more consistently.


The Structure of Arguments from Deontic Authority and How to Successfully Attack Them

Abstract: Abstract Despite increasing interest in studying arguments from deontic authority of the general form “(1) $$\delta$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>δ</mml:mi> </mml:math> is a deontic authority in institution $$\varOmega$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Ω</mml:mi> </mml:math> ; (2) according to $$\delta$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>δ</mml:mi> </mml:math> , I should do $$\alpha$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> , C : therefore, (3) I should do $$\alpha$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> ”, the state of the art models are not capable of grasping their complexity. The existing sets of critical questions assigned to this argumentation scheme seem to conflate two problems: whether a person is subject to an authority of an institution in the first place and whether the command issued within the context of a particular institution is eventually binding. For this reason, we introduce (1) a set of Basic Critical Questions to scrutinize the former issue, and (2) a set of more detailed questions related to specific features, also referred to as “parameters”, of institutional environments (Intra-Institutional Critical Questions). We identify major elements of institutional environments in which authoritative utterances are made and the crucial parameters of arguments from deontic authority. The selected evidence from the decisions of the Polish Supreme Administrative Court helps us show how these parameters may be used to reconstruct subtypes of this argument scheme, with their associated sets of critical questions. In specific institutional contexts, such detailed schemes are capable of grasping the complexity of appeals to deontic authority and thus should be used rather than general schemes. The reconstruction of argumentation schemes with critical questions shows how particular arguments may successfully be attacked.


Unveiling the Dynamics of Extrinsic Motivations in Shaping Future Experts’ Contributions to Developer Q&A Communities

Abstract: Developer question and answering communities rely on experts to provide helpful answers. However, these communities face a shortage of experts. To cultivate more experts, the community needs to quantify and analyze the rules of the influence of extrinsic motivations on the ongoing contributions of those developers who can become experts in the future (potential experts). Currently, there is a lack of potential expert-centred research on community incentives. To address this gap, we propose a motivational impact model with self-determination theory-based hypotheses to explore the impact of five extrinsic motivations (badge, status, learning, reputation, and reciprocity) for potential experts. We develop a status-based timeline partitioning method to count information on the sustained contributions of potential experts from Stack Overflow data and propose a multifactor assessment model to examine the motivational impact model to determine the relationship between potential experts’ extrinsic motivations and sustained contributions. Our results show that (i) badge and reciprocity promote the continuous contributions of potential experts while reputation and status reduce their contributions; (ii) status significantly affects the impact of reciprocity on potential experts’ contributions; (iii) the difference in the influence of extrinsic motivations on potential experts and active developers lies in the influence of reputation, learning, and status and its moderating effect. Based on these findings, we recommend that community managers identify potential experts early and optimize reputation and status incentives to incubate more experts.


Research on fault detection and diagnosis of carbon dioxide heat pump systems in buildings based on transfer learning

Abstract: Nowadays, carbon dioxide (CO2) heat pump systems are widely used in fields such as refrigeration, heating, and hot water. However, the faulty operation of heat pump equipment may lead to higher energy consumption and even problems such as damaged components and systems that do not work properly. It is necessary to establish a fault detection and diagnosis (FDD) model for CO2 heat pumps to reduce energy waste caused by faults and restore the system to the expected performance level. Therefore, this study proposes four transfer learning methods based on feature transformations to establish an FDD model for CO2 heat pump systems. The models are also optimized by feature parameter screening, classifier tuning and training set occupancy. The results show that the accuracy of the methods based on transfer component analysis (TCA), joint distribution adaptation (JDA) and balanced distribution adaptation (BDA) increased from 97.79% to 98.89%, and the accuracy of the method based on correlation alignment (CORAL) increased from 15.67% to 74.23%. The runtime of the four methods TCA, JDA, BDA and CORAL are 0.07s,5.37s, 3.30s and 0.03s respectively. It is demonstrated that the transfer learning-based method proposed in this study can effectively diagnose common faults in CO2 heat pump systems.


German funders’ data sharing policies—A qualitative interview study

Abstract: Background Data sharing is commonly seen as beneficial for science but is not yet common practice. Research funding agencies are known to play a key role in promoting data sharing, but German funders’ data sharing policies appear to lag behind in international comparison. This study aims to answer the question of how German data sharing experts inside and outside funding agencies perceive and evaluate German funders’ data sharing policies and overall efforts to promote data sharing. Methods This study is based on sixteen guided expert interviews with representatives of German funders and German research data experts from stakeholder organisations, who shared their perceptions of German’ funders efforts to promote data sharing. By applying the method of qualitative content analysis to our interview data, we categorise and describe noteworthy aspects of the German data sharing policy landscape and illustrate our findings with interview passages. Results We present our findings in five sections to distinguish our interviewees’ perceptions on a) the status quo of German funders’ data sharing policies, b) the role of funders in promoting data sharing, c) current and potential measures by funders to promote data sharing, d) general barriers to those measures, and e) the implementation of more binding data sharing requirements. Discussion and conclusion Although funders are perceived to be important promoters and facilitators of data sharing throughout our interviews, only few German funding agencies have data sharing policies in place. Several interviewees stated that funders could do more, for example by providing incentives for data sharing or by introducing more concrete policies. Our interviews suggest the academic freedom of grantees is widely perceived as an obstacle for German funders in introducing mandatory data sharing requirements. However, some interviewees stated that stricter data sharing requirements could be justified if data sharing is a part of good scientific practice.


Identifying the most important facilitators of open research data sharing and reuse in Epidemiology: A mixed-methods study

Abstract: To understand how open research data sharing and reuse can be further improved in the field of Epidemiology, this study explores the facilitating role that infrastructural and institutional arrangements play in this research discipline. It addresses two research questions: 1) What influence do infrastructural and institutional arrangements have on open research data sharing and reuse practices in the field of Epidemiology? And 2) how could infrastructural and institutional instruments used in Epidemiology potentially be useful to other research disciplines? First, based on a systematic literature review, a conceptual framework of infrastructural and institutional instruments for open research data facilitation is developed. Second, the conceptual framework is applied in interviews with Epidemiology researchers. The interviews show that two infrastructural and institutional instruments have a very high influence on open research data sharing and reuse practices in the field of Epidemiology, namely (a) access to a powerful search engine that meets open data search needs and (b) support by data stewards and data managers. Third, infrastructural and institutional instruments with a medium, high, or very high influence were discussed in a research workshop involving data stewards and research data officers from different research fields. This workshop suggests that none of the influential instruments identified in the interviews are specific to Epidemiology. Some of our findings thus seem to apply to multiple other disciplines. This study contributes to Science by identifying field-specific facilitators and challenges for open research data in Epidemiology, while at the same time revealing that none of the identified influential infrastructural and institutional instruments were specific to this field. Practically, this implies that open data infrastructure developers, policymakers, and research funding organizations may apply certain infrastructural and institutional arrangements to multiple research disciplines to facilitate and enhance open research data sharing and reuse.


Local business environment and firm innovation: evidence from China

Abstract: Purpose This paper aims to investigate the influence of the regional business environment on local firm innovation, considering various dimensions such as administrative, financial and legal environments. Design/methodology/approach Multiple regression analysis is employed to analyze archival data for firms listed on Chinese stock markets. Findings We find that the optimizations of the administrative and financial environments positively affect firm innovation, whereas the legal environment does not exert a similar impact. Our analysis also reveals that the business environment’s optimization significantly influences innovation in firms that are small, non-state-owned and operating in high-tech industries. Furthermore, the business environment acts as a moderating variable in the relationship between firm innovation and firm value. Research limitations/implications This study contributes to a more comprehensive understanding of institutional-level determinants of firm innovation, highlighting the nuances of the legal environment and the importance of context-specific analysis, especially in emerging markets like China. Practical implications Developing countries can significantly enhance firm innovation by improving the business environment, including the optimization of administrative and financial systems, reducing transaction costs and ensuring capital supply. Tailored legal frameworks and alternative institutional strategies may also be explored. Social implications This study explicitly emphasizes the governmental role in promoting firm innovation, shedding light on policy formulation and strategic alignment with local administrative policies. Originality/value To the best of our knowledge, this paper is the first to explore the relationship between the business environment and firm innovation using World Bank indicators in an emerging market context, providing novel insights into the unique dynamics of legal, financial and administrative sub-environments.


Understanding user perceptions toward marketing in the metaverse

Abstract: Purpose This study develops a comprehensive research model and investigates the significant factors affecting positive marketing outcomes in the Metaverse through perceived social benefits and trust. Design/methodology/approach The authors propose a new research model based on social exchange theory (SET) and examine the impact of cost and reward factors. Using 327 survey samples collected from current Metaverse users in South Korea, dual-stage analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) and an artificial neural network (ANN) were employed to test the study’s hypotheses. Findings The results showed that perceived social benefit and trust had significant mediating effects on marketing outcomes, such as loyalty to the seller, product/service attitude, and purchase intention. All antecedents, except perceived performance risk, had a crucial impact on the two mediators. The most interesting finding of this study is the positive influence of knowledge-seeking efforts on perceived social benefits. Originality/value This study is the first empirical research to examine the effectiveness of marketing in the Metaverse. It also proposes a new theoretical model based on SET to investigate users’ behavioral intentions regarding marketing in the Metaverse, and confirms its explanatory power. Moreover, the results of this study also offer suggestions to brands on how to market to consumers in the Metaverse.


Argo data anomaly detection based on transformer and Fourier transform

Abstract: Argo data is multidimensional observational data from ocean floats, which has long been plagued by data anomalies. Currently, the anomaly detection problem in Argo data still faces many challenges, as anomalies may involve complex relationships between multiple variables, leading to suboptimal performance with traditional machine learning methods. Thanks to the advancement of deep learning, it has become the predominant methodology for anomaly detection, demonstrating notable performance. The Transformer model has shown significant potential in the field of data anomaly detection, with its core Self-Attention mechanism capable of learning relationships between variables. We introduce Fast Fourier Transform (FFT) into the Transformer model, enabling the model to better capture periodic patterns and complex relationships in multivariate data, learning normal data patterns to improve the method for Argo data anomaly detection. Through experiments conducted on three public datasets and the Argo dataset, the enhanced model outperforms the original model in terms of performance. This also demonstrates the potential of FFT in multidimensional data anomaly detection, providing new insights into addressing anomaly detection challenges in real-world complex datasets.


Integrating Egocentric and Robotic Vision for Object Identification Using Siamese Networks and Superquadric Estimations in Partial Occlusion Scenarios

Abstract: This paper introduces a novel method that enables robots to identify objects based on user gaze, tracked via eye-tracking glasses. This is achieved without prior knowledge of the objects’ categories or their locations and without external markers. The method integrates a two-part system: a category-agnostic object shape and pose estimator using superquadrics and Siamese networks. The superquadrics-based component estimates the shapes and poses of all objects, while the Siamese network matches the object targeted by the user’s gaze with the robot’s viewpoint. Both components are effectively designed to function in scenarios with partial occlusions. A key feature of the system is the user’s ability to move freely around the scenario, allowing dynamic object selection via gaze from any position. The system is capable of handling significant viewpoint differences between the user and the robot and adapts easily to new objects. In tests under partial occlusion conditions, the Siamese networks demonstrated an 85.2% accuracy in aligning the user-selected object with the robot’s viewpoint. This gaze-based Human–Robot Interaction approach demonstrates its practicality and adaptability in real-world scenarios.


Mineralogy and Geochemistry of the Mupane and Shashe Gold Deposits of the Tati Greenstone Belt (NE Botswana): Implications for Mineral Deposit Models and Exploration

Abstract: Abstract The Archean Tati greenstone belt is located at the southwestern margin of the Zimbabwe craton (northeast Botswana) and hosts numerous Cu-Ni ± platinum group element (PGE) and Au ± Ag occurrences and deposits. Gold occurrences/deposits are poorly studied, and key questions pertaining to their genesis remain unclear, including the mode of occurrence(s) of gold, the relative timing of gold introduction with respect to the evolution of the greenstone belt, the number of gold mineralization events, the alteration patterns, and the relationships between each alteration pattern and gold mineralization. A detailed study that includes sulfide and gold grain chemistry using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) and electron probe microanalysis (EPMA), respectively, and X-ray diffraction (XRD) of mineralized rock samples of three Tati greenstone belt gold deposits was carried out to constrain the genesis of gold mineralization and formulate exploration guidelines in the Tati greenstone belt. Gold in the Tati greenstone belt is the result of multiple events, is mainly associated with arsenopyrite, pyrite, and sphalerite, and occurs as (1) microinclusions within sulfides, (2) intergrowth with sulfides, (3) minute particles (&lt;2–10 μm) within the silicate matrix, (4) microfractures and microvug infills, and (5) lattice-bound and sulfide-hosted refractory gold. The first gold event (as electrum) is premetamorphic and associated with sphalerite-quartz veins. The second stage is a postmetamorphic high-grade gold event that is accompanied by extensive carbonatization and propylitization of host rocks. The third stage of gold mineralization is marked by the dissolution of gold in the early formed stages 1 to 2 and subsequent reprecipitation within cracks, fractures, and vugs. Auriferous pyrite composition suggests that Au-bearing mineralizing fluids are predominantly of magmatic origin and that their physicochemical compositions changed during the mineralization process, as supported by chemically zoned Au-bearing arsenopyrite, various alteration types containing gold, and variation in gold fineness across the several gold deposits in the Tati greenstone belt. Gold deposition in the Tati greenstone belt mainly occurred through sulfidation, as indicated by the closest spatial association between gold and Fe-bearing sulfides and ferromagnesian silicates. Gold in the Tati greenstone belt is closely correlated with As, Sb, Pb, Bi, Ni, Hg, Tl, Cd, In, Mo, W, Zn, and Te and moderately to weakly associated with Sn, Se, Cr, Co, Ge, Cd, Mn, V, Ga, and Ag. The correlation between Au and fluid mobile elements, i.e., Te, Sb, Se, As, Hg, and Bi, can be used as a vectoring tool during the exploration of gold within the Tati greenstone belt, as these elements likely form halos that are much broader than the primary footprint of gold mineralization.


A Dual Fusion Pipeline to Discover Tactical Knowledge Guided by Implicit Graph Representation Learning

Abstract: Discovering tactical knowledge aims to extract tactical data derived from battlefield signal data, which is vital in information warfare. The learning and reasoning from battlefield signal information can help commanders make effective decisions. However, traditional methods are limited in capturing sequential and global representation due to their reliance on prior knowledge or feature engineering. The current models based on deep learning focus on extracting implicit behavioral characteristics from combat process data, overlooking the embedded martial knowledge within the recognition of combat intentions. In this work, we fill the above challenge by proposing a dual fusion pipeline introducing graph representation learning into sequence learning to construct tactical behavior sequence graphs expressing implicit martial knowledge, named TBGCN. Specifically, the TBGCN utilizes graph representation learning to represent prior knowledge by building a graph to induce deep learning paradigms, and sequence learning finds the hidden representation from the target’s serialized data. Then, we employ a fusion module to merge two such representations. The significance of integrating graphs with deep learning lies in using the artificial experience of implicit graph structure guiding adaptive learning, which can improve representation ability and model generalization. Extensive experimental results demonstrate that the proposed TBGCN can effectively discover tactical knowledge and significantly outperform the traditional and deep learning methods.


Latency-Constrained Neural Architecture Search Method for Efficient Model Deployment on RISC-V Devices

Abstract: The rapid development of the RISC-V instruction set architecture (ISA) has garnered significant attention in the realm of deep neural network applications. While hardware-aware neural architecture search (NAS) methods for ARM, X86, and GPUs have been extensively explored, research specifically targeting RISC-V remains limited. In light of this, we propose a latency-constrained NAS (LC-NAS) method specifically designed for RISC-V. This method enables efficient network searches without the requirement of network training. Concretely, in the training-free NAS framework, we introduce an RISC-V latency evaluation module that includes two implementations: a lookup table and a latency predictor based on a deep neural network. To obtain real latency data, we have designed a specialized data collection pipeline for RISC-V devices, which allows for precise end-to-end hardware latency measurements. We validate the effectiveness of our method in the NAS-Bench-201 search space. Experimental results demonstrate that our method can efficiently search for latency-constrained networks for RISC-V devices within seconds while maintaining high accuracy. Additionally, our method can easily integrate with existing training-free NAS approaches.


A categorical interpretation of state merging algorithms for DFA inference

Abstract: We use Category Theory to interpret the family of algorithms for inference of DFAs that work by merging states. This interpretation allows us to characterize the structure of the search space and to define criteria for the convergence of these algorithms to the correct DFA. We also prove that the well-known EDSM algorithm does not identify DFAs in the limit.