Reviewerly/exHarmony huggingface dataset:
Marketness and Governance: A Typology of Illicit Online Markets

The preceding decade has seen an explosive growth in illicit online commerce, and scholars are increasingly occupied with the study of illicit products, their buyers and sellers, and the platforms and markets in which they are found. Despite sharing one basic characteristic, the exchange of illicit goods and services, there is extensive variation between illicit online markets: Some markets employ currencies, others are based on barter. Some markets exhibit strong social control, others appear anarchic. Traditional theoretical explanations to the organization of illicit markets, which assume that optimization or evolution drives their organization, find it difficult to explain these variations. I propose a typology of illicit online markets that appreciates this heterogeneity and uses it as a theoretical point of departure. Drawing on social control theory and economic sociology, I argue that illicit online markets can be separated across two axes: administrative governance and ”marketness.” This conceptual framework is not prone to functionalist inference and instead implies a less deterministic understanding of how illicit online markets come to be. In turn, this framework encourages the classification and comparison of individual markets, as well as comparative analyses of marketplaces.
Transformer‐based reranking for improving Korean morphological analysis systems

Abstract This study introduces a new approach in Korean morphological analysis combining dictionary‐based techniques with Transformer‐based deep learning models. The key innovation is the use of a BERT‐based reranking system, significantly enhancing the accuracy of traditional morphological analysis. The method generates multiple suboptimal paths, then employs BERT models for reranking, leveraging their advanced language comprehension. Results show remarkable performance improvements, with the first‐stage reranking achieving over 20% improvement in error reduction rate compared with existing models. The second stage, using another BERT variant, further increases this improvement to over 30%. This indicates a significant leap in accuracy, validating the effectiveness of merging dictionary‐based analysis with contemporary deep learning. The study suggests future exploration in refined integrations of dictionary and deep learning methods as well as using probabilistic models for enhanced morphological analysis. This hybrid approach sets a new benchmark in the field and offers insights for similar challenges in language processing applications.
Dual‐scale BERT using multi‐trait representations for holistic and trait‐specific essay grading

Abstract As automated essay scoring (AES) has progressed from handcrafted techniques to deep learning, holistic scoring capabilities have merged. However, specific trait assessment remains a challenge because of the limited depth of earlier methods in modeling dual assessments for holistic and multi‐trait tasks. To overcome this challenge, we explore providing comprehensive feedback while modeling the interconnections between holistic and trait representations. We introduce the DualBERT‐Trans‐CNN model, which combines transformer‐based representations with a novel dual‐scale bidirectional encoder representations from transformers (BERT) encoding approach at the document‐level. By explicitly leveraging multi‐trait representations in a multi‐task learning (MTL) framework, our DualBERT‐Trans‐CNN emphasizes the interrelation between holistic and trait‐based score predictions, aiming for improved accuracy. For validation, we conducted extensive tests on the ASAP++ and TOEFL11 datasets. Against models of the same MTL setting, ours showed a 2.0% increase in its holistic score. Additionally, compared with single‐task learning (STL) models, ours demonstrated a 3.6% enhancement in average multi‐trait performance on the ASAP++ dataset.
Spoken‐to‐written text conversion for enhancement of Korean–English readability and machine translation

Abstract The Korean language has written (formal) and spoken (phonetic) forms that differ in their application, which can lead to confusion, especially when dealing with numbers and embedded Western words and phrases. This fact makes it difficult to automate Korean speech recognition models due to the need for a complete transcription training dataset. Because such datasets are frequently constructed using broadcast audio and their accompanying transcriptions, they do not follow a discrete rule‐based matching pattern. Furthermore, these mismatches are exacerbated over time due to changing tacit policies. To mitigate this problem, we introduce a data‐driven Korean spoken‐to‐written transcription conversion technique that enhances the automatic conversion of numbers and Western phrases to improve automatic translation model performance.
CR‐M‐SpanBERT: Multiple embedding‐based DNN coreference resolution using self‐attention SpanBERT

Abstract This study introduces CR‐M‐SpanBERT, a coreference resolution (CR) model that utilizes multiple embedding‐based span bidirectional encoder representations from transformers, for antecedent recognition in natural language (NL) text. Information extraction studies aimed to extract knowledge from NL text autonomously and cost‐effectively. However, the extracted information may not represent knowledge accurately owing to the presence of ambiguous entities. Therefore, we propose a CR model that identifies mentions referring to the same entity in NL text. In the case of CR, it is necessary to understand both the syntax and semantics of the NL text simultaneously. Therefore, multiple embeddings are generated for CR, which can include syntactic and semantic information for each word. We evaluate the effectiveness of CR‐M‐SpanBERT by comparing it to a model that uses SpanBERT as the language model in CR studies. The results demonstrate that our proposed deep neural network model achieves high‐recognition accuracy for extracting antecedents from NL text. Additionally, it requires fewer epochs to achieve an average F1 accuracy greater than 75% compared with the conventional SpanBERT approach.
Towards a small language model powered chain‐of‐reasoning for open‐domain question answering

Abstract We focus on open‐domain question‐answering tasks that involve a chain‐of‐reasoning, which are primarily implemented using large language models. With an emphasis on cost‐effectiveness, we designed EffiChainQA , an architecture centered on the use of small language models. We employed a retrieval‐based language model to address the limitations of large language models, such as the hallucination issue and the lack of updated knowledge. To enhance reasoning capabilities, we introduced a question decomposer that leverages a generative language model and serves as a key component in the chain‐of‐reasoning process. To generate training data for our question decomposer, we leveraged ChatGPT, which is known for its data augmentation ability. Comprehensive experiments were conducted using the HotpotQA dataset. Our method outperformed several established approaches, including the Chain‐of‐Thoughts approach, which is based on large language models. Moreover, our results are on par with those of state‐of‐the‐art Retrieve‐then‐Read methods that utilize large language models.
Unsupervised domain adaptation for histopathology image segmentation with incomplete labels

Stain variations pose a major challenge to deep learning segmentation algorithms in histopathology images. Current unsupervised domain adaptation methods show promise in improving model generalization across diverse staining appearances but demand abundant accurately labeled source domain data. This paper assumes a novel scenario, namely, unsupervised domain adaptation based segmentation task with incompletely labeled source data. This paper propose a Stain-Adaptive Segmentation Network with Incomplete Labels (SASN-IL). Specifically, the algorithm consists of two stages. The first stage is an incomplete label correction stage, involving a reliable model selection process and label correction to rectify false-negative regions in incomplete labels. The second stage is the unsupervised domain adaptation stage, achieving segmentation on the target domain. In this stage, we introduce an adaptive stain transformation module to adjust the degree of transformation based on segmentation performance. We evaluate our method on a gastric cancer dataset, demonstrating significant improvements, with a 10.01% increase in Dice coefficient compared to the baseline and competitive performance relative to existing methods.
Survey of Spectral Clustering Based on Graph Theory

Spectral clustering converts the data clustering problem to the graph cut problem. It is based on graph theory. Due to the reliable theoretical basis and good clustering performance, spectral clustering has been successfully applied in many fields. Although spectral clustering has many advantages, it faces the challenges of high time and space complexity when dealing with large scale complex data. Firstly, this paper introduces the basic concept of graph theory, reviews the properties of Laplacian matrix and the traditional graph cuts method. Then, it focuses on four aspects of the realization process of spectral clustering, including the construction of similarity matrix, the establishment of Laplacian matrix, the selection of eigenvectors and the determination of the number of clusters. In addition, some successful applications of spectral clustering are summarized. In each aspect, the shortcomings of spectral clustering and some representative improved algorithms are emphatically analyzed. Finally, the paper comprehensively analyzes some research on spectral clustering that has not yet been in-depth, and gives prospects on some valuable research directions.
Creating Autonomous Multi-Object Safe Control via Different Forms of Neural Constraints of Dynamic Programming

The aim of this work, which is an extension of previous research, is a comparative analysis of the results of the dynamic optimization of safe multi-object control, with different representations of the constraints of process state variables. These constraints are generated with an artificial neural network and take movable shapes in the form of a parabola, ellipse, hexagon, and circle. The developed algorithm allows one to determine a safe and optimal trajectory of an object when passing other multi-objects. The obtained results of the simulation tests of the algorithm allow for the selection of the best representation of the motion of passing objects in the form of neural constraints. Moreover, the obtained characteristics of the sensitivity of the object’s trajectory to the inaccuracy of the input data make it possible to select the best representation of the motion of other objects in the form of an excessive approximation area as neural constraints of the control process.
Design and Characterization of a Powered Wheelchair Autonomous Guidance System

The current technological revolution driven by advances in machine learning has motivated a wide range of applications aiming to improve our quality of life. Representative of such applications are autonomous and semiautonomous Powered Wheelchairs (PWs), where the focus is on providing a degree of autonomy to the wheelchair user as a matter of guidance and interaction with the environment. Based on these perspectives, the focus of the current research has been on the design of lightweight systems that provide the necessary accuracy in the navigation system while enabling an embedded implementation. This motivated us to develop a real-time measurement methodology that relies on a monocular RGB camera to detect the caregiver's feet based on a deep learning method, followed by the distance measurement of the caregiver from the PW. An important contribution of this article is the metrological characterization of the proposed methodology in comparison with measurements made with dedicated depth cameras. Our results show that despite shifting from 3D imaging to 2D imaging, we can still obtain comparable metrological performances in distance estimation as compared with Light Detection and Ranging (LiDAR) or even improved compared with stereo cameras. In particular, we obtained comparable instrument classes with LiDAR and stereo cameras, with measurement uncertainties within a magnitude of 10 cm. This is further complemented by the significant reduction in data volume and object detection complexity, thus facilitating its deployment, primarily due to the reduced complexity of initial calibration, positioning, and deployment compared with three-dimensional segmentation algorithms.
HQ3DAvatar: High Quality Implicit 3D Head Avatar

Multi-view volumetric rendering techniques have recently shown great potential in modeling and synthesizing high-quality head avatars. A common approach to capture full head dynamic performances is to track the underlying geometry using a mesh-based template or 3D cube-based graphics primitives. While these model-based approaches achieve promising results, they often fail to learn complex geometric details such as the mouth interior, hair, and topological changes over time. This paper presents a novel approach to building highly photorealistic digital head avatars. Our method learns a canonical space via an implicit function parameterized by a neural network. It leverages multiresolution hash encoding in the learned feature space, allowing for high-quality, faster training and high-resolution rendering. At test time, our method is driven by a monocular RGB video. Here, an image encoder extracts face-specific features that also condition the learnable canonical space. This encourages deformation-dependent texture variations during training. We also propose a novel optical flow based loss that ensures correspondences in the learned canonical space, thus encouraging artifact-free and temporally consistent renderings. We show results on challenging facial expressions and show free-viewpoint renderings at interactive real-time rates for a resolution of 480 x 270. Our method outperforms related approaches both visually and numerically. We will release our multiple-identity dataset to encourage further research.
The usage of virtual reality in engineering education

The state of virtual reality (VR) development is mature enough to be explored for application in various fields including higher education teaching, research, and training including engineering education. Traditionally, engineering education relied on diagrams and images to describe various systems and objects, sometimes accompanied by laboratory experiments involving existing systems for hands-on practice. However, these traditional approaches have limited capability. Visualization for effective illustration using the traditional approaches is challenging for topics such as electromagnetic which discusses abstract concepts of electric and magnetic fields. VR is a promising solution. It offers a more engaging and immersive visualization experience, leading to a greater understanding of the subject matter. The immersiveness of VR eliminates distracting external stimuli, and allows better user engagement with the lesson. Therefore, this study explores the application of VR in engineering education. An in-house developed VR laboratory named Merlin’s Playground is incorporated as part of assignment activities for a group of first-year students taking bachelor degree in electronics engineering. The students’ perspectives and attitudes towards the VR activities are studied. The student population finds this approach to be an appealing technology. The VR activity achieved the course aims by allowing the topic’s conception more successfully. The VR approach boosted the students’ understanding and problem-solving skills. This is demonstrated by the cohort of students achieving higher average marks on end-of-topic assessments compared to their seniors when VR activities were not employed.
Demonstrating Bayesian quantum phase estimation with quantum error detection

Quantum phase estimation (QPE) serves as a building block of many different quantum algorithms and finds important applications in computational chemistry problems. Despite the rapid development of quantum hardware, experimental demonstration of QPE for chemistry problems remains challenging due to its large circuit depth and the lack of quantum resources to protect the hardware from noise with fully fault-tolerant protocols. In the present work, we take a step towards fault-tolerant quantum computing by demonstrating a QPE algorithm on a Quantinuum trapped-ion computer. We employ a Bayesian approach to QPE and introduce a routine for optimal parameter selection, which we combine with a $[[n+2,n,2]]$ quantum error detection code carefully tailored to the hardware capabilities. As a simple quantum chemistry example, we take a hydrogen molecule represented by a two-qubit Hamiltonian and estimate its ground state energy using our QPE protocol. In the experiment, we use the quantum circuits containing as many as 920 physical two-qubit gates to estimate the ground state energy within $6\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}3}$ hartree of the exact value.
Medical long-tailed learning for imbalanced data: Bibliometric analysis

In the last decade, long-tail learning has become a popular research focus in deep learning applications in medicine. However, no scientometric reports have provided a systematic overview of this scientific field. We utilized bibliometric techniques to identify and analyze the literature on long-tailed learning in deep learning applications in medicine and investigate research trends, core authors, and core journals. We expanded our understanding of the primary components and principal methodologies of long-tail learning research in the medical field.Web of Science was utilized to collect all articles on long-tailed learning in medicine published until December 2023. The suitability of all retrieved titles and abstracts was evaluated. For bibliometric analysis, all numerical data were extracted. CiteSpace was used to create clustered and visual knowledge graphs based on keywords.A total of 579 articles met the evaluation criteria. Over the last decade, the annual number of publications and citation frequency both showed significant growth, following a power-law and exponential trend, respectively. Noteworthy contributors to this field include Husanbir Singh Pannu, Fadi Thabtah, and Talha Mahboob Alam, while leading journals such as IEEE ACCESS, COMPUTERS IN BIOLOGY AND MEDICINE, IEEE TRANSACTIONS ON MEDICAL IMAGING, and COMPUTERIZED MEDICAL IMAGING AND GRAPHICS have emerged as pivotal platforms for disseminating research in this area. The core of long-tailed learning research within the medical domain is encapsulated in six principal themes: deep learning for imbalanced data, model optimization, neural networks in image analysis, data imbalance in health records, CNN in diagnostics and risk assessment, and genetic information in disease mechanisms.This study summarizes recent advancements in applying long-tail learning to deep learning in medicine through bibliometric analysis and visual knowledge graphs. It explains new trends, sources, core authors, journals, and research hotspots. Although this field has shown great promise in medical deep learning research, our findings will provide pertinent and valuable insights for future research and clinical practice.
Vision-Based On-Road Nighttime Vehicle Detection and Tracking Using Improved HOG Features

The lack of discernible vehicle contour features in low-light conditions poses a formidable challenge for nighttime vehicle detection under hardware cost constraints. Addressing this issue, an enhanced histogram of oriented gradients (HOGs) approach is introduced to extract relevant vehicle features. Initially, vehicle lights are extracted using a combination of background illumination removal and a saliency model. Subsequently, these lights are integrated with a template-based approach to delineate regions containing potential vehicles. In the next step, the fusion of superpixel and HOG (S-HOG) features within these regions is performed, and the support vector machine (SVM) is employed for classification. A non-maximum suppression (NMS) method is applied to eliminate overlapping areas, incorporating the fusion of vertical histograms of symmetrical features of oriented gradients (V-HOGs). Finally, the Kalman filter is utilized for tracking candidate vehicles over time. Experimental results demonstrate a significant improvement in the accuracy of vehicle recognition in nighttime scenarios with the proposed method.
DIFFERENCES IN THE WISHES OF STUDENTS, TEACHERS, AND PARENTS ON INTEGRATION OF SMARTPHONES AND TABLETS IN BIOLOGY LESSONS

Smartphones and tablets have permeated various aspects of life. This study explores the differences in wishes between students, parents, and teachers regarding the use of smartphones and tablets in biology classes in the upcoming school year. An online questionnaire was used for the study, which provided eight different scenarios for the use of smartphones for teaching purposes. The data were collected from 934 participants, including 465 students, 282 parents, and 188 biology teachers from various Slovenian lower secondary schools. The principal component analysis revealed the unidimensional structure of the instrument, explaining 59.7% of the variance (alpha = .91). The results showed that the use of smartphones and tablets for distance learning, teaching purposes, schoolwork and homework is generally desirable. There was less consensus on their use for laboratory and field work, evaluation of knowledge, and biology lessons. The main finding was that the differences between the groups were small or even negligible in terms of effect sizes. Statistically significant differences were found between the focus groups, with students and teachers expressing greater agreement than parents. These findings emphasize the importance of addressing parents' concerns and understanding the perspectives of stakeholders in order to effectively integrate smartphones and tablets into the classroom. Keywords: differences in wishes, lower secondary school biology, mobile learning, smartphones and tablets integration, students and parents and teachers