Reviewerly/exHarmony huggingface dataset:
Exploring the Potential: Smart Contracts and the Fight against Trade-Based Money Laundering in International Trade

In the evolving landscape of international trade, this comprehensive exploration delves into the paradigm shift enabled by smart contracts, challenging the traditional role of Documentary Credits (LCs). By prioritizing transparency, the article showcases how the integration of smart contracts revolutionizes proforma invoicing submission and offers a robust mechanism for scrutinizing over and under-invoicing—an entrenched tactic in Trade-Based Money Laundering (TBML). The innovative concept of introducing a bidding process within smart contracts opens doors for global participation, reducing entry barriers for startups and entrepreneurs, thereby transforming poverty into prosperity. These transformative mechanisms, rooted in blockchain technology, mark a compelling shift towards combatting TBML and fostering a more inclusive and dynamic global economy.
Learnable Information-Preserving Image Resizer for Face Forgery Detection

Resizing input face images of arbitrary sizes to a uniform size is an essential preprocessing to satisfy the architectural requirements of face forgery detectors. In this letter, we reveal an important observation that traditional resizing methods degrade the performance of face forgery detectors due to the loss of high-frequency information. To address this issue, we propose a simple yet effective learnable information-preserving resizer to replace its lossy traditional counterparts. Specifically, we use Haar transform to separate low-and high-frequency components, and then perform learnable resizing on the high-frequency subbands. We conduct experiments to compare our learnable resizer with other methods and evaluate three existing detectors with and without incorporating our resizer. Experimental results show that our resizer outperforms other resizers and consistently enhances the detection performance of tested detectors, confirming the effectiveness of our proposed resizer
Crowd Counting From Limited Labeled Data Using Active Learning

Deep learning models have provided dramatic performance improvement for various computer vision tasks. These models, however, require huge amounts of labeled data to perform well. Collecting and labeling large datasets is often nontrivial and requires significant human effort. Crowd counting is one such task that demands a large amount of labeled training data. This labeling process requires a human annotator to manually mark a dot at the center of the head of each person present in the image, which is a laborious and tedious task, especially in densely crowded scenes. In this work, we investigate an active learning framework for crowd counting. Evaluations on mainstream datasets demonstrate the effectiveness of the proposed framework in reducing the annotation effort significantly with minimal compromise on count performance. Our method surpasses existing methods that focus on counting with limited labeled data
Extended Tropical Circuits

In this chapter, we first show that just one division $$(/)$$ gate as the output gate can exponentially decrease the size of monotone arithmetic $$(+,\times )$$ circuits. As a consequence, just one subtraction $$(-)$$ gate at the “very end,” as the output gate, can exponentially decrease the size of tropical $$(\min ,+)$$ circuits. We then show that even $$(\min ,\max ,+)$$ circuits (with monotone $$\max $$ operation instead of non-monotone subtraction) can be exponentially smaller than tropical $$(\min ,+)$$ circuits. Finally, we show that, in contrast, subtractions at the “very beginning,” at input gates, are of almost no help. Namely, the size of $$(\min ,+)$$ circuits cannot be substantially reduced by allowing the circuits, besides input variables $$x_1,\ldots ,x_n$$ , to also use their tropical reciprocals $$-x_1,\ldots ,-x_n$$ as inputs. The question of whether reciprocal inputs can substantially decrease the size of $$(\min ,\max ,+)$$ circuits remains open.
Statistical Methods

Chapter 2 describes two models of statistical inference: the population model first put forward by J. Neyman and E. Pearson in 1928 and the permutation model developed by R.A Fisher, R.C. Geary, T. Eden, F. Yates, H. Hotelling, M. R. Pabst, and E.J.G. Pitman in the 1920s and 1930s. First, the Neyman–Pearson population model of statistical inference is described. Second, the assumptions underlying the population model of independence, random sampling, normality, homogeneity of variance, and homogeneity of variance are examined. Third, a historical perspective on the Fisher–Pitman permutation model of statistical inference is presented. Fourth, the two models of statistical inference are compared. Fifth, the differences between the Neyman–Pearson population model and the Fisher–Pitman permutation model are illustrated with the analysis of a small dataset. Finally, exact and Monte Carlo permutation methods are described and illustrated utilizing a historical dataset.
Stable Remote Optimal Filtering and Fusion With Communication Driven by Cumulative Estimate Innovation

This article studies remote filtering for a discrete-time linear system observed by one or more sensors with limited communication resources. We propose new communication schemes based on cumulative estimate innovation and derive the corresponding minimum mean square error (MMSE) filter and fuser. Our communication schemes balance communication cost and estimation performance. The proposed remote filter and fuser can improve estimation under limited communication resources by leveraging the information of no transmission. Further, it is proved that these filters have guaranteed stability—the expected norms of the mean square error (MSE) matrices are bounded and their upper bounds are obtained. We also give the transmission rate, conditional probabilities of a future transmission and conditional probabilities based on the elapsed time since the latest transmission. The effectiveness of the proposed methods is illustrated by simulation examples.
Bounds for Approximating Circuits

We have seen that some optimization problems cannot be solved by tropical circuits using a polynomial number of gates. But what happens if we allow the circuits to only output “almost optimal” values: can at least then the circuit size be substantially reduced? The goal of this chapter is to present recently emerged arguments for proving lower bounds on the size of approximating tropical circuits. It turns out that the task of proving lower bounds for approximating $$(\min ,+)$$ circuits is much easier than for $$(\max ,+)$$ circuits. While the size of the former circuits is lower bounded by the size of monotone Boolean circuits, approximating $$(\max ,+)$$ circuits are much more powerful. For them, neither the Boolean bound nor even counting arguments work. Still, also for $$(\max ,+)$$ circuits, the situation is not hopeless: there is a general combinatorial bound leading to strong lower bounds on the size of such circuits approximating explicit maximization problems, as well.
Seeking Public Comment on CSL 1.0.2 Release

Over the past few months, Citation Style Language developers have worked to address a backlog of feature requests. This work will be reflected in two upcoming releases. The first of these, 1.0.2, is slated for release shortly. Its focus is on easy to implement, non-controversial additions, principally new item types, fields, and terms.
Let's Explore! The Factor, Reliability, and Validity Analyses of Readiness for a Knowledge-Based Economy Among Undergraduate Students

&lt;p style="text-align:justify"&gt;Knowledge-based economy is an economic model students need to be prepared for a future economic model that uses knowledge as its main resource. Therefore, this study developed and validated instruments for constructing knowledge-based economy readiness among undergraduate students. This study used an online questionnaire with 120 respondents of economic education students in educational universities in East Java, Indonesia, for exploratory factor analysis and 417 respondents for confirmatory factor analysis. Then, statistical analysis was conducted using exploratory factor analysis in SPSS and confirmatory factor analysis in AMOS. This study first developed five factors of knowledge of economics, readiness for economic challenges, readiness for education, readiness for infrastructure, and readiness for innovation, consisting of 27 items. However, one item was removed because the loading factor was below .50. Consequently, 26 items were retained because the loading factor was significantly greater than .50. The Cronbach's alpha value for each item of the knowledge-based economy readiness construct was &gt;.60 and met all goodness of fit index criteria, which means that it meets the requirements and can measure the construct of knowledge-based economy readiness. Since this study meets the validity and reliability requirements of the constructs leading to knowledge-based economy readiness, these results will help students prepare for the current and future knowledge-based economy. They can be used in developing economic education curricula in higher education.&lt;/p&gt;
Situated Learning and Education: Development and Validation of the Future Teacher Attitudes Scale in the Application of Augmented Reality in the Classroom

&lt;p style="text-align:justify"&gt;This research article focuses on the design and validation of a questionnaire to analyse future teachers' perceptions of professional skills through the use of Augmented Reality (AR) in higher education, specifically for students in the field of Educational Sciences. The sample consisted of 575 students of Early Childhood Education, Primary Education and Pedagogy during the academic year (2021/2022). The focus of this study is to authenticate a questionnaire that measures the influence of Augmented Reality (AR) on aspects such as situated learning, motivation, and the necessary instructional preparations for the successful integration of AR within classroom educational encounters. The questionnaire is an online Likert-type scale developed based on three dimensions: situated learning, motivation and training. The data were analysed using the Statistical Package for the Social Sciences (SPSS) version 25 and JASP 0.17.1. The questionnaire met the standards recommended for validation. However, improvements to the instrument are suggested. In conclusion, validation of instruments is necessary to gain a rigorous understanding of the impact of new learning environments.&lt;/p&gt;
A 3D navigation algorithm switching between waypoint and Bezier curves based local plans for micro air vehicles

Micro Air Vehicles (MAVs) are playing an increasingly prominent role in our lives. Numerous studies have been conducted on autonomous mobility. In this study, an architecture has been developed for 3D mapping and 3D navigation for MAVs. The flight time of a MAV is considerably shorter than ground vehicles. Therefore, they are expected to perform tasks more efficiently and with higher performance. MAVs can plan a route to a given target and follow this path using the developed 3D navigation algorithm. Tracking paths produced by classical waypoint-based planners is slow. For this reason, a local path planner based on Bezier curves has been developed for MAVs, with path planning represented as a curve on the low-level control card. Since it is not always possible to reach the given target with a single curve, the multi-hop Bezier curves approach has been proposed. Particularly in environments with low complexity, this approach yields very effective results. In cases where the Bezier path cannot be generated in high-complexity environments, a metric has been developed to measure the complexity, allowing for a switch between waypoint and multi-hop Bezier curves based local plans on the environment complexity metric. Thanks to these developed methods, the aim is to extend the flight time of MAVs by optimizing their battery usage. Within the scope of this study, all experiments were conducted in the ROS-GAZEBO simulation environment. Firstly, waypoint-based local planning methods such as Dijkstra and A*, which will function within the navigation algorithm, were implemented to operate in 3D, and performance tests were conducted in the designed simulation environments. Subsequently, a multi-hop Bezier curves-based local planner was developed, and performance comparisons were made with waypoint-based methods using the same simulation environments, taking into account environmental complexity parameters.
Event-Triggered bundled target traversing path planning using a dynamic elliptical guidance region for unmanned surface vehicles

Multiple problems such as target assignment timeliness and path energy optimization make it challenging for USVs traversing targets. This paper proposes a novel multiple targets traversing path planning algorithm for USVs, which includes Event-Triggered bundled (ET-Bundled) genetic target assignment method and dynamic elliptical guidance region (DEGR) path planning method. In terms of solving above problems, comparative simulation studies with the state-of-the-art methods demonstrate that ET-Bundled can not only ensures that the traversing time and path length do not exceed the constraint range, but also reduces the average CPU time of membrane structure by 60%, and DEGR can optimize the path energy consumption of EGR path by 34%.
The impact of mixed reality serious games on mortise and tenon learning in college students

As an intangible cultural heritage, traditional handicrafts are significant in artistic education. Due to limited approaches to communicating traditional handicrafts and appreciating content, it cannot be easy to carry out effective skill inheritance in an art education situation in a modern teaching environment. This study uses mixed reality technology to develop a serious game for learning traditional handicrafts. The objective is to improve students' attitudes by promoting a digital teaching environment. This research uses traditional Chinese mortise and tenon joints for the case study. The effects of serious games using MR technology and traditional teaching methods were compared through quasi-experiments on students' academic performance, learning motivation and situational interest. The results show that serious games can have advantages in acquiring and retaining knowledge, stimulating students' learning motivation and situational interest. This study aims to support cultural heritage education methods.
A step-size follow-the-leader optimization algorithm with an improved step parameters

Follow the leader (FTL) algorithm is a newly developed optimization algorithm inspired by a sheep’s movement within a flock. FTL has been successfully implemented to solve power prediction problems. However, the probability of falling in local optima is high due to randomness in the step parameter. This paper proposes a step-size follow-the-leader (SFTL) algorithm with decreasing and increasing step-size combinations. The improved step parameter tunes the search space by generating a new solution to improve the accuracy and convergence rate of the FTL algorithm. Four different FTL variants have been presented in this paper to show the impact of the dynamic step-size parameter. FTL improvement is verified by testing SFTL over thirty-two fixed unimodal, unimodal, fixed multimodal, and multimodal benchmark functions. The computational results indicate that SFTL significantly improves the basic FTL algorithm and converges early compared to other algorithms. SFTL has also been tested on five real engineering design problems and obtained results show that SFTL outperformed FTL and other popular optimization algorithms.
The acceleration of blockchain technology adoption in Taiwan

This study explores the factors influencing the intention to adopt blockchain technology and the alignment between respondents' work characteristics and blockchain technology. It addresses the lack of attention from previous literature on these factors in the context of blockchain adoption, especially amidst the recent blockchain hype. By integrating Task-Technology Fit (TTF) and a modified version of the Unified Theory of Acceptance and Use of Technology (UTAUT), this study employs a questionnaire survey. The results demonstrate that the alignment between blockchain and work characteristics significantly impacts performance expectations and the recognition of opportunities, thereby influencing the intention to adopt blockchain. Furthermore, factors such as effort expectancy, social influence, facilitating conditions, and perceived policy uncertainty influence adoption intention by identifying opportunities. Exposure to blockchain-related knowledge reinforces the relationships mentioned above. Additionally, digital natives display greater confidence in blockchain's potential to enhance work performance than digital immigrants. These study findings offer valuable insights and strategies for blockchain technology providers and provide a comprehensive survey for individuals and professionals interested in embracing or understanding the dynamics of blockchain adoption.
Ore genesis of the Laguhe Au deposit, West Qinling, China: Evidence from sulfide geochemistry and machine learning

The precious metal Au has a wide range of utilization in various fields, and Au deposits in China are hosted mostly within the West Qinling Orogen and Jiaodong Peninsula. The Laguhe deposit is a newly discovered medium–size Au deposit in the West Qinling Orogen. Previous studies focused mainly on the nature of Au mineralization in this deposit in different exploration stages, so its genesis and ore–forming physico–chemical conditions and processes remain enigmatic. Here, we present major and trace element analyses of Au–bearing pyrite and arsenopyrite from the Laguhe Au deposit, in combination with machine learning to explore its origin and ore–forming processes. The early and late stages of arsenopyrite show As contents of 40.69–46.22 wt% and 39.12–43.22 wt%, respectively, which constrain corresponding ore–forming temperatures at 320–370 °C and 270–340 °C, respectively. The sulfur fugacities in the two stages range from 10–13.2 to 10–8.4. The arsenopyrite has high Co and Ni contents of 0.02–0.28 wt% and 0.02–0.27 wt%, respectively, which suggest that a deep source mafic magmatic water participated in the Au mineralization. The Fe/(S + As) ratios in pyrite vary from 0.838 to 0.869, which indicate that the Laguhe deposit formed at a moderate to deep ore–forming depth (1.5–8.0 km). Most of the Co/Ni ratios (1–5) and the negative correlation (–0.47) between Fe and As contents in pyrite suggest that the ore–forming hydrothermal fluids in the Laguhe deposit were moderately acidic. Based on the heterogeneous Au contents and lack of correlation between Fe and Au in pyrite and arsenopyrite, most of Au likely exist as nanoparticles, with few as lattice Au in pyrite and arsenopyrite. Machine learning results based on the geochemistry of arsenopyrite and pyrite classified the Laguhe deposit as a Carlin–like type Au deposit. The Au in the Laguhe deposit migrated mainly in the forms of Au(HS)2– and AuH3SiO4 in hydrothermal fluids, which then precipitated as a result of water–rock reaction, fluid immiscibility and fluid boiling at and around plutons and along faults.
Online Collaborative Learning: A Study of Communicative Interactions in a Latin American University Environment

The Collaborative Learning has been a reference learning theory in higher education, nevertheless, learners don’t recognize and take advantage of its potential, or how to reach approaches and coregulate with peers. The current post-pandemic scenario has generated new opportunities for interaction in virtual environments. However, the low or inconstant students’ participation in them has given way to research seeking to know the causes and whole scenario of process and suggest alternatives to enhance communicative interactions and collaboration on online platforms. In this article we study how can communicative interactions and collaboration mediated by information and communication technologies be fostered, enhanced, or increased in university e-learning environments. This research was developed using the qualitative perspective with a hermeneutic methodological approach. For the study, 124 primary studies of the literature and 42 e-learning environments of the Academic U-Virtual platform of the ‘Universidad de Medellín’ -Colombia were analyzed. The application of the research method allowed us to find four major components as a result: motivation and feedback, mediation/interaction, didactic, and technological. Such components can integrate and promote communicative interactions and online collaboration in university environments. As main conclusions, we found that the proposed components will allow: (i) to distinguish the particularities of communicative interactions; (ii) to facilitate tasks oriented to promote skills centered on collaboration, self-learning, and flexibility; and (iii) to design improved virtual environments by using synchronous and asynchronous tools that encourage the communicative interactions and the permanent collaboration of professors and students on e-learning platforms.
Classification of Software Tools Available for Download from Servers Hosted at Educational Institutions

Faculty members are expected to carry out an increasing number of teaching and administrative tasks, as well as high level research. Their work is likely to be supported by a variety of computer systems, tools and programs. This immediately raises the question of how to select those tools that are genuinely useful and do not have a steep learning curve. This work is an attempt to create a structure for hosting computer-based tools at a university, rather than presenting them by title alone. The approach is based on an eight-value logic and aims to classify these tools according to their frequency of use.
New Minds, New Methods: Pre-service Teachers Envision the Educational Future

What do people who study to become teachers dread and hope with regard to technology? Pre-service teachers (n = 21) were encouraged to consider the future of education by incorporating elements of, for example, augmented and virtual reality and Chat-GPT during three workshops that introduced them to the subject of sociotechnical imaginaries. Pre-service teachers were requested to write reflections and narratives for their logbooks. Individual thought expression can encourage group discussion and make instructors more confident in controlling future narratives. Such empowerment can mitigate risks and potential, inform elective courses of action, and improve understanding of the complexity of the future of digital education. Reflections were therefore done both individually and in groups. Data were analysed using the lens of Diffusion of Innovations theory, using thematic analysis. Three themes were identified, and key findings reveal opposing and ambivalent responses to the digitalisation of teaching, with plurality and flexibility on the one hand and restriction and isolation on the other. The results convey both passivity and hope as digital innovation was thought to solve problems by bridging the classroom to the outside world, leading to increased loneliness. The paper would interest stakeholders looking to leverage responsible digitalisation, the teaching community, and educational researchers.