Reviewerly/exHarmony huggingface dataset:
A Case Study on the Self-Conspecting Essay Teaching of the Process Centered in University Writing Education

The purpose of this study was to examine the self-searching and reflection process through self-reflection class cases for college freshmen, and to examine the process in which the results lead to writing. The effectiveness of self-reflection writing and the effectiveness of self-reflection writing through actual class examples are summarized. First, reflection writing provides a chance to reflect on life and express it in writing to students who become college students but have difficulty adapting. Second, reflective writing is effective for college freshmen who are burdened with writing to take the first step in writing. Third, we realized that writing is also a tool for communication by generating the contents of a sentence through oral writing. Fourth, through peer feedback, we realize the importance of articulating impressions and enlightenment from the reader's point of view. This self-reflection writing case is meaningful in that it faithfully fulfilled its original role in writing education.
Transition to a DLT-Supported Governance Framework

This chapter considers the practical and legislative challenges of and requirements for achieving an effective crypto-legal structure for DLT in the carbon trading economy. It also explores the roles of different stakeholders in this transitioning process to achieve an efficient carbon economy.
Aspect Based Opinion Summarization Using Rule-Based Method and Support Vector Machine for Indonesian Reviews

E-commerce's reviews feature can help users find information about the desired skin care products to choose the right one. However, the reviews number of a product grows rapidly due to the popularity of e-commerce and the product itself. A user becomes difficult to read all reviews one by one and extract useful information. To deal with this problem, we summarize aspects using the Rule-Based method and Support Vector Machine. We propose a Rule-Based method that is used to break down a review into several segments based on its aspect. Support Vector Machine is used to classify sentence segments according to their polarity. The data used in this study is Indonesian reviews of skin care products obtained from the Female Daily website. The average accuracy results using 10-fold cross-validation of sentiment classification is 74%. We experimented on 462 reviews where the accuracy is 92% in aspect categorization and 71.2% in sentiment classification. Based on humans, the lowest value is the suitability of a sentence with its sentiment/polarity. The highest value is the suitability of sentences with its aspect and usability of summary to helps users to find specific information so they can decide whether to buy that product or not. It can be concluded that the reader can well receive the summary. Future work can consider the negation word to reduce misclassification in the sentiment classification step.
Generating Quantum Light: New Prospects from Superradiance

We discover a property universal to all superradiant processes: the radiation undergoes a transition from classical to quantum, distinguished by a negative Wigner function, opening new avenues toward the generation of engineered quantum light states.
Seeing through unknown, random diffusers using diffractive optical networks

Diffractive deep neural networks composed of spatially-engineered transmissive layers were designed and experimental demonstrated to see through unknown, random phase diffusers, instantaneously reconstructing images of unknown objects without the need for a digital computer.
A Review of Literature on the Music-using Apps and Developmental Strategies

This study is to review of literature on the music-using apps and developmental strategies. The results of analyzing 50 research from 2012 to 2021 were as follows. Music-using app research were performed research fields on music education and interdisciplinary research, topic on effectiveness of developing and applying program for middle and high school students the most in 2015. Apps mainly dealt with ‘Garage Band’ and virtual instruments were used in an integrated way. Positive aspects were ‘interaction with smart devices’, ‘aesthetic music experience’, ‘voluntary participation’, ‘learning effect’ and ‘strategic use in educational fields’, ‘economical high utility’ and ‘expanding use of music in life’. Negative aspects were ‘system limitations’, ‘lack of music-centered experience’, ‘need a strategy for learners’, ‘Insufficient strategies for learning attitudes in education’ and ‘difficulties in operating apps’. Developmental strategies for music-using app is to improve functions for useful apps and explore utilization in the educational field. Future, it is expected to develop music app to enjoy aesthetic experience of music and to increase usability in life.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing Publishing. • Type of peer review: Single Anonymous • Conference submission management system: Morressier • Number of submissions received: 36 • Number of submissions sent for review: 29 • Number of submissions accepted: 13 • Acceptance Rate (Submissions Accepted / Submissions Received × 100): 36.1 • Average number of reviews per paper: 3 • Total number of reviewers involved: 16 • Contact person for queries: Name: Dr Geetha Ganesan Email: geetha@advancedcomputingresearchsociety.org Affiliation: Advanced Computing Research Society
An Efficient Optimization based Hybrid Dual Faster R-CNN for Classification of Municipal Solid Waste

Abstract The authors have requested that this preprint be removed from Research Square.
Federated Learning over Wireless IoT Networks with Optimized Communication and Resources

To leverage massive distributed data and computation resources, machine learning in the network edge is considered to be a promising technique especially for large-scale model training. Federated learning (FL), as a paradigm of collaborative learning techniques, has obtained increasing research attention with the benefits of communication efficiency and improved data privacy. Due to the lossy communication channels and limited communication resources (e.g., bandwidth and power), it is of interest to investigate fast responding and accurate FL schemes over wireless systems. Hence, we investigate the problem of jointly optimized communication efficiency and resources for FL over wireless Internet of things (IoT) networks. To reduce complexity, we divide the overall optimization problem into two sub-problems, i.e., the client scheduling problem and the resource allocation problem. To reduce the communication costs for FL in wireless IoT networks, a new client scheduling policy is proposed by reusing stale local model parameters. To maximize successful information exchange over networks, a Lagrange multiplier method is first leveraged by decoupling variables including power variables, bandwidth variables and transmission indicators. Then a linear-search based power and bandwidth allocation method is developed. Given appropriate hyper-parameters, we show that the proposed communication-efficient federated learning (CEFL) framework converges at a strong linear rate. Through extensive experiments, it is revealed that the proposed CEFL framework substantially boosts both the communication efficiency and learning performance of both training loss and test accuracy for FL over wireless IoT networks compared to a basic FL approach with uniform resource allocation.
“I’m in the center of a vortex”: Mapping the affective experiences of trolling victims

This study investigates how the landscape of affect in trolling is constituted from the perspective of victims. It is based on a series of semi-structured interviews conducted in 2018 and 2019 with people who had experienced trolling on Chinese social media and were willing to talk about it. All participants described trolling incidents as beginning from something seemingly inconsequential. By the end of their descriptions, however, a messy, uncontrolled situation emerged that became the source of stress, frustration, and anxiety. Victims worked, sometimes unsuccessfully, to escape from trolling and regain control, not just of their social media communications but also of their identities. Their posts and, inevitably, themselves became the central point of attraction for content to stick to. Screenshots played a particular role in creating this stickiness. Even though some factors were considered a normal but unfortunate part of using social media, crossing the line into private spaces was considered unacceptable. A mapping of the landscape of affect showed that the emotions expressed have a clear starting point in surprise and a fairly clear sequence, with timing, including rapid escalation and drawn-out continuation of the experience, alongside a lack of logical explanations and reactions, leading to a sense of powerlessness. The potential for damage to a victim’s everyday life and for harm to their sense of self, along with the difficulties in reclaiming an identity over which they have control, indicates that a focus on the victims of trolling is a topic worthy of further research.
Fast Computation of Neck-Like Features

Locating neck-like features, or locally narrow parts, of a surface is crucial in various applications such as segmentation, shape analysis, path planning, and robotics. Topological methods are often utilized to find the set of shortest loops around handles and tunnels. However, there are abundant neck-like features on genus-0 shapes without any handles. While 3D geometry-aware topological approaches exist to find neck loops, their construction can be cumbersome and may even lead to geometrically wide loops. Thus we propose a "topology-aware geometric approach" to compute the tightest loops around neck features on surfaces, including genus-0 surfaces. Our algorithm starts with a volumetric representation of an input surface and then calculates the distance function of mesh points to the boundary surface as a Morse function. All neck features induce critical points of this Morse function where the Hessian matrix has precisely one positive eigenvalue, i.e., type-2 saddles. As we focus on geometric neck features, we bypass a topological construction such as the Morse-Smale complex or a lower-star filtration. Instead, we directly create a cutting plane through each neck feature. Each resulting loop can then be tightened to form a closed geodesic representation of the neck feature. Moreover, we offer criteria to measure the significance of a neck feature through the evolution of critical points when smoothing the distance function. Furthermore, we speed up the detection process through mesh simplification without compromising the quality of the output loops.
Using English Poetry in English Education: Prejudices, Difficulties, and Suggestions

This essay aims to explore how to apply English poetry to the site of English language education in Korea by retracing prejudices and difficulties surrounding teaching English poetry in the classroom. Since early nineteenth century, English poetry has been suggested by literary elites as a solution to correct the social problems and the dehumanization caused by the Western industrialization. Many humanistic educators, on this basis, have insisted that English poetry should be included in the curriculum. However, the social and cultural significance imposed on English poetry has excessively increased and mystified the value of English poetry and made the practice of reading poetry a privileged exclusive property of a small number of elite humanistic intellectuals, thus separating the general public from English poetry. This separation between humanistic intellectuals and the public has brought about ordinary people’s prejudices against poetry, from which pedagogical difficulties of teaching English poetry have emerged not only in Anglophone countries but in Korea as well. Faced with these difficulties of teaching English poetry, this essay tries to provide practical answers to how Korean educators of English poetry will carry out their challenging task. (Seoul National University)
Hexagon path planning algorithm

The paper presents a novel 2D geometrical path plan algorithm that reduces calculation load and time by filtering obstacles before path planning starts by the newly introduced hexagon filter and also while path search is in progress. Unlike other methods that use only obstacle filtering before path planning starts. The suggested algorithm was able to solve a randomly created maze that contains 400 obstacles with 800 nodes, which was considered before, as next to impossible to solve. This is because computational time is proportional to n2log(n) where n is the number of obstacles, node suggested algorithm reduced the computational time to about 1 in 2500 times compared to the best of (ESOVG, DVG or ECoVG). In addition, the suggested algorithm can be used in the case of a fixed start point and different target points (e.g. a swarm of robots leaving from the same start point with different targets).
Convolutional Neural Network for Denoising Left Ventricle Magnetic Resonance Images

Medical image processing is critical in disease detection and prediction. For example, they locate lesions and measure an organ's morphological structures. Currently, cardiac magnetic resonance imaging (CMRI) plays an essential role in cardiac motion tracking and analyzing regional and global heart functions with high accuracy and reproducibility. Cardiac MRI datasets are images taken during the heart's cardiac cycles. These datasets require expert labeling to accurately recognize features and train neural networks to predict cardiac disease. Any erroneous prediction caused by image impairment will impact patients' diagnostic decisions. As a result, image preprocessing is used, including enhancement tools such as filtering and denoising. This paper introduces a denoising algorithm that uses a convolution neural network (CNN) to delineate left ventricle (LV) contours (endocardium and epicardium borders) from MRI images. With only a small amount of training data from the EMIDEC database, this network performs well for MRI image denoising.
Quark: A Gradient-Free Quantum Learning Framework for Classification Tasks

As more practical and scalable quantum computers emerge, much attention has been focused on realizing quantum supremacy in machine learning. Existing quantum ML methods either (1) embed a classical model into a target Hamiltonian to enable quantum optimization or (2) represent a quantum model using variational quantum circuits and apply classical gradient-based optimization. The former method leverages the power of quantum optimization but only supports simple ML models, while the latter provides flexibility in model design but relies on gradient calculation, resulting in barren plateau (i.e., gradient vanishing) and frequent classical-quantum interactions. To address the limitations of existing quantum ML methods, we introduce Quark, a gradient-free quantum learning framework that optimizes quantum ML models using quantum optimization. Quark does not rely on gradient computation and therefore avoids barren plateau and frequent classical-quantum interactions. In addition, Quark can support more general ML models than prior quantum ML methods and achieves a dataset-size-independent optimization complexity. Theoretically, we prove that Quark can outperform classical gradient-based methods by reducing model query complexity for highly non-convex problems; empirically, evaluations on the Edge Detection and Tiny-MNIST tasks show that Quark can support complex ML models and significantly reduce the number of measurements needed for discovering near-optimal weights for these tasks.
Self-improving Multiplane-to-layer Images for Novel View Synthesis

We present a new method for lightweight novel-view synthesis that generalizes to an arbitrary forward-facing scene. Recent approaches are computationally expensive, require per-scene optimization, or produce a memory-expensive representation. We start by representing the scene with a set of fronto-parallel semitransparent planes and afterward convert them to deformable layers in an end-to-end manner. Additionally, we employ a feed-forward refinement procedure that corrects the estimated representation by aggregating information from input views. Our method does not require fine-tuning when a new scene is processed and can handle an arbitrary number of views without restrictions. Experimental results show that our approach surpasses recent models in terms of common metrics and human evaluation, with the noticeable advantage in inference speed and compactness of the inferred layered geometry, see https://samsunglabs.github.io/MLI
Duo Lingo Application for Teaching Speaking Skills Class: Students' Perception of Speech Ability

Effect of Duo Lingo application to improve speaking ability. Duo Lingo to enhance student speaking ability. There are several problems with speech fluency This study is intended to find out the effectiveness of the Duolingo application on students’ perception to improve their speaking skills. Thus, this study used a qualitative approach, especially using an ethnomethodology research design. Firstly, this study used a qualitative approach because the researcher is intended to reach the data from interviews students’ procedures for creating and managing a sense of objective reality. This study is involved 27 students of grade IX from one class. Almost all students know the application of Duolingo,The result of the questionnaire indicates that 10 Neutral, 15 Agree, 2 Strongly agree that Duolingo as media improves English skills. It indicates that most of the learners agree that Duolingo is a media to improve English skills. As a result, teachers should be encouraged to use this app in language instruction and learning. Furthermore, teachers must ensure that this application is capable of being used efficiently. Another significant factor to consider is the use of cell phones in the classroom because some schools restrict mobile phone use while the classroom.
Unbiased Asymmetric Reinforcement Learning under Partial Observability

In partially observable reinforcement learning, offline training gives access to latent information which is not available during online training and/or execution, such as the system state. Asymmetric actor-critic methods exploit such information by training a history-based policy via a state-based critic. However, many asymmetric methods lack theoretical foundation, and are only evaluated on limited domains. We examine the theory of asymmetric actor-critic methods which use state-based critics, and expose fundamental issues which undermine the validity of a common variant, and limit its ability to address partial observability. We propose an unbiased asymmetric actor-critic variant which is able to exploit state information while remaining theoretically sound, maintaining the validity of the policy gradient theorem, and introducing no bias and relatively low variance into the training process. An empirical evaluation performed on domains which exhibit significant partial observability confirms our analysis, demonstrating that unbiased asymmetric actor-critic converges to better policies and/or faster than symmetric and biased asymmetric baselines.
Is the Genie Out of the Bottle?

This chapter examines the rapid expansion and acceptance of computer-mediated technology for the provision of analysis and analytic training during the Covid pandemic. It argues that computer-mediated technology introduces a variety of distortions to the interactive field and interferes with channels of implicit and unconscious communication normally utilized during in-person analysis and analytic training. The chapter explores the alterations that occur in the field when physical bodies are not mutually present, as well as highlighting the reduction in analytic ritual and frame that occurs when analysis is mediated via computer. Well over one hundred years ago, Williams James highlighted the importance of the body as the center of our experience. The world experienced comes at all times with our body as its center, center of vision, center of action, center of interest. In computer-mediated analysis, it is primarily the visual and auditory senses that come most actively into play.
The game of blocking pebbles

Graph pebbling is a well-studied single-player game on graphs. We introduce the game of blocking pebbles, which adapts Graph Pebbling into a two-player strategy game to examine it within the context of combinatorial game theory. Positions with game values matching all integers, all nimbers, and many infinitesimals and switches are found. This game joins the ranks of other combinatorial games on graphs, games with discovered moves, and partisan games with impartial movement options. The computational complexity of the general case is shown to be PSPACE-hard.
Make It Usable to Others: Documentation and Sphinx

Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. “Try This!” exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specific homework problems across the sciences and engineering.
Seven Observations, or Why Domestic Robots are Struggling to Enter the Habitats of Everyday Life

Presents the perspectives of design researcher James Auger, who claims that robots are too often a solution in search of a problem. He makes a plea for domesticating robots by better understanding the contexts in which they operate.
Aircraft Spatial Signal Testing Method Based on Big Data Driven Self-Learning

The aircraft spatial signal map is used to discern whether the aircraft signal meets the system operating requirements. The traditional acquisition of aircraft spatial signal direction map relies on test flights, and the data obtained can only plot two-dimensional directional map of aircraft signals. Given that such approach will incur high cost, long time and incomplete data, this paper proposes a novel technical route to acquire aircraft spatial signals by utilizing big data driven self-learning technology. Using neural networks and fuzzy algorithm for data processing, the data-based method is able to improve the measurement accuracy of the aircraft spatial signal, save test flight cost, promote overhaul of the aircraft communication system and maintain aircraft flight safety.