Reviewerly/exHarmony huggingface dataset:
The Effectiveness of Psychoeducation on Depression Literacy and Psychological Well-being of University Students

Today’s modern life is a challenging issue for human beings, particularly those who have mental illnesses like depression, requiring awareness-related programs for increasing their Depression Literacy (D-Lit) and Psychological Well-being (PWB). To mark the gap, we planned to investigate the effect of web-based psychoeducation on literacy regarding depression and psychological well-being. A total of 100 depressed students were included in the sampling frame, purposively selected based on some inclusion and exclusion criteria from Jagannath University. The study was accomplished by following a pretest-posttest with a control group design where data were collected using Depression, Depression Literacy (D-Lit), and Psychological Well-being questionnaires along with a personal information form. The data were analyzed by applying an independent sample t-test, paired sample t-test, and ANCOVA. Results revealed that the level of D-Lit and PWB of the depressed students significantly increased in the post-test as compared to the pre-test in the experimental group, but no significant changes in the control group, indicating psychoeducation has an effect on increasing D-Lit and PWB. This finding explored that psychoeducation can be an immediate awareness program for improving depression-related knowledge and psychological well-being in the community. The applications of these outcomes for investigation and practice are discussed.&#x0D; Jagannath University Journal of Science, Volume 10, Number I, Jun 2023, pp. 1−10
A self-adaptive parallel image stitching algorithm for unmanned aerial vehicles in edge computing environments

The overlap between edge computing and unmanned aerial systems enables Unmanned Aerial Vehicles (UAV) to quickly offload image processing tasks onto edge devices, avoiding the transmission of images over long distances. To improve the speed and efficiency of UAV image stitching in an edge computing environment, this paper proposes an adaptive UAV image parallel stitching algorithm in an edge computing environment. The algorithm incorporates both route-based parallel processing and inverted binary tree-based parallel processing, dividing the image stitching task into multiple processes and allocating them to different cores based on the CPU core count, number of flight routes, and number of images, thereby enhancing computational efficiency in edge scenarios. The experimental results indicate that, when the number of flight routes is greater than or equal to the number of CPUs, the adaptive algorithm will employ the more efficient route parallelism. Conversely, when the number of flight routes is less than the number of CPUs, the efficiency of inverted binary tree parallelism is higher. In the same experimental environment and dataset, the adaptive image stitching algorithm demonstrates an efficiency improvement of approximately 2–10 times compared to other algorithms, with no significant degradation in image quality. This demonstrates that in edge environments, the utilization of multi-threaded adaptive route and inverted binary tree-based parallel approaches can effectively harness the computing resources of edge devices, significantly improving the stitching speed of UAV images and providing technical support for rapid real-time monitoring by UAV.
Bribing to Escape Poverty in Africa

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

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

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 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

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

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

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

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

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

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

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

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

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.
Global and Multiscale Aggregate Network for Saliency Object Detection in Optical Remote Sensing Images

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

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

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.