Reviewerly/exHarmony huggingface dataset:
CR‐M‐SpanBERT: Multiple embedding‐based DNN coreference resolution using self‐attention SpanBERT

Authors:
raw_author_name: Joon‐young Jung
raw_affiliation_string: Superintelligence Creative Research Laboratory Electronics and Telecommunications Research Institute Daejeon Republic of Korea

Abstract: 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

Authors:
raw_author_name: Jihyeon Roh
raw_affiliation_string: Language Intelligence Research Group Electronics and Telecommunications Research Institute Daejeon Republic of Korea
raw_author_name: Minho Kim
raw_affiliation_string: Language Intelligence Research Group Electronics and Telecommunications Research Institute Daejeon Republic of Korea
raw_author_name: Kyoungman Bae
raw_affiliation_string: Language Intelligence Research Group Electronics and Telecommunications Research Institute Daejeon Republic of Korea

Abstract: 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

Authors:
raw_author_name: Huihui Zhou
raw_affiliation_string: Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
raw_author_name: Yan Wang
raw_affiliation_string: Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
raw_author_name: Benyan Zhang
raw_affiliation_string: Department of Gastroenterology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Chunhua Zhou
raw_affiliation_string: Department of Gastroenterology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Duowu Zou
raw_affiliation_string: Department of Gastroenterology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Qingli Li
raw_affiliation_string: Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China

Abstract: 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

Authors:
raw_author_name: Ling Ding
raw_affiliation_string: College of Intelligence and Computing, Tianjin University, Tianjin, China
raw_author_name: Chao Li
raw_affiliation_string: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
raw_author_name: Di Jin
raw_affiliation_string: College of Intelligence and Computing, Tianjin University, Tianjin, China
raw_author_name: Shifei Ding
raw_affiliation_string: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China

Abstract: 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

Authors:
raw_author_name: Józef Lisowski
raw_affiliation_string: Faculty of Electrical Engineering, Gdynia Maritime University, 81-225 Gdynia, Poland

Abstract: 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

Authors:
raw_author_name: Vincenzo Gallo
raw_affiliation_string: Department of Industrial Engineering, University of Salerno, 84084 Fisciano, Italy
raw_author_name: Irida Shallari
raw_affiliation_string: Department of Computer and Electrical Engineering, Mid Sweden University, 85170 Sundsvall, Sweden
raw_author_name: Marco Carratù
raw_affiliation_string: Department of Industrial Engineering, University of Salerno, 84084 Fisciano, Italy
raw_author_name: Consolatina Liguori
raw_affiliation_string: Department of Industrial Engineering, University of Salerno, 84084 Fisciano, Italy

Abstract: 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

Authors:
raw_author_name: Kartik Teotia
raw_affiliation_string: Max Planck Institute for Informatics, Saarbrucken and Saarland University, Saarbrucken, Germany
raw_author_name: Mallikarjun B R
raw_affiliation_string: Max Planck Institute for Informatics and Saarland University, Saarbrucken, Germany
raw_author_name: Xingang Pan
raw_affiliation_string: Max Planck Institute for Informatics,Saarbrucken, Germany and Nanyang Technological University, Singapore
raw_author_name: Hyeongwoo Kim
raw_affiliation_string: Imperial College London, London, United Kingdom
raw_author_name: Mohamed Elgharib
raw_affiliation_string: Max Planck Institute for Informatics, Saarbrucken, Germany
raw_author_name: Christian Theobalt
raw_affiliation_string: Max Planck Institute for Informatics and Saarland University, Saarbrucken, Germany

Abstract: 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

Authors:
raw_author_name: Nor Azlina Ab. Aziz
raw_affiliation_string: Faculty of Engineering &amp; Technology, Multimedia University, Melaka, Malaysia
raw_author_name: Kamarulzaman Ab. Aziz
raw_affiliation_string: Faculty of Business, Multimedia University, Melaka, Malaysia
raw_author_name: Neo Tse Kian
raw_affiliation_string: Faculty of Creative Multimedia, Multimedia University, Cyberjaya, Malaysia

Abstract: 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

Authors:
raw_author_name: Samuel Duffield
raw_affiliation_string: Quantinuum, Partnership House, Carlisle Place, London SW1P 1BX, United Kingdom
raw_author_name: Yuta Kikuchi
raw_affiliation_string: Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS), RIKEN, Wako, Saitama 351-0198, Japan; Quantinuum K.K., Otemachi Financial City Grand Cube 3F, 1-9-2 Otemachi, Chiyoda-ku, Tokyo, Japan
raw_author_name: David Muñoz Ramo
raw_affiliation_string: Quantinuum, Terrington House, 13-15 Hills Road, Cambridge CB2 1NL, United Kingdom

Abstract: 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.
Vision-Based On-Road Nighttime Vehicle Detection and Tracking Using Improved HOG Features

Authors:
raw_author_name: Li Zhang
raw_affiliation_string: Changzhou Xingyu Automotive Lighting System Co., Ltd., 182 Qinling Road, Changzhou 213000, China; School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
raw_author_name: Weiyue Xu
raw_affiliation_string: School of Mechanical Engineering & Rail Transit, Changzhou University, Changzhou 213164, China
raw_author_name: Yingping Huang
raw_affiliation_string: School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Abstract: 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

Authors:
raw_author_name: Vida Lang
raw_affiliation_string: University of Maribor, Slovenia
raw_author_name: Andrej Šorgo
raw_affiliation_string: University of Maribor, Slovenia

Abstract: 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
Comparative study: enhancing legibility of ancient Indian script images from diverse stone background structures using 34 different pre-processing methods

Authors:
raw_author_name: J. Jayanthi
raw_affiliation_string: Department of Computer Science and Engineering, Anna University, Guindy Campus, Chennai, 600025, India
raw_author_name: P. Uma Maheswari
raw_affiliation_string: Department of Computer Science and Engineering, Anna University, Guindy Campus, Chennai, 600025, India

Abstract: Abstract In recent times, there has been a proactive effort by various institutions and organizations to preserve historic manuscripts as repositories of traditional knowledge and cultural heritage. Leveraging digital media and emerging technologies has proven to be an efficient way to safeguard these invaluable documents. Such technologies not only facilitate the extraction of knowledge from historic manuscripts but also hold promise for global applications. However, transforming inscribed stone artifacts into binary formats presents significant challenges due to angle distortion, subtle differences between foreground and background, background noise, variations in text size, and related issues. A pivotal aspect of effective image processing in preserving the rich information and wisdom encoded in stone inscriptions lies in employing appropriate pre-processing methods and techniques. This research paper places a special focus on elucidating various preprocessing techniques, encompassing resizing, grayscale conversion, enhancement of brightness and contrast, smoothening, noise removal, morphological operations, and thresholding. To comprehensively assess these techniques, we undertake a study involving stone inscription images extracted from the Tanjore Brihadeeswar Temple, dating back to the eleventh century during the reign of Raja Raja Chola. This choice is informed by the manifold challenges associated with image correction, such as distortion and blurring. We undertake an evaluation encompassing a diverse array of stone background structures, including types like flawless-bright-moderately legible, dark-illegible, flawless-bright-illegible, flawless-dull, flawless-irregular-moderate, highly impaired-dark-legible, highly impaired-irregular-illegible, impaired-dark-moderate, impaired-dull-moderately legible, impaired-dusky dark-moderate, and very impaired-dusky dark-legible. Subsequently, the processed outputs are subjected to character recognition and information extraction, with a focus on comparing the outcomes of various pre-processing methods, including binarization and grayscale conversion. This study seeks to contribute insights into the most effective pre-processing strategies for enhancing the legibility and preservation of ancient Indian script images etched onto diverse stone background structures.