Reviewerly/exHarmony huggingface dataset:
A Study for examining the method of commentary on 『Shijing(The Classics of Poetry)』 through ｢Dúshīsuíbǐ(讀詩隨筆)｣ by a scholarin the late Joseon Dynasty, Lee Sang-soo

The purpose of this paper is to examine the method of commentary on 『Shijing(The Classics of Poetry)』 through ｢Dúshīsuíbǐ｣ by a scholar in the late Joseon Dynasty, Lee Sang-soo(1820~1882).&#x0D; He was a Horon scholar and a nongam literaturer in the late Joseon Dynasty. He was associated with various litterateurs and scholars of the Horon and Nongam worlds, and combined the strengths of Philology and rhetoric. This provides an opportunity to use various commentary methods in the study of Chinese classics. A typical example is ｢Dúshīsuíbǐ｣. It is a commentary on 『Shijing』, and a collection of short essays that describe opinions, impressions, and opinions in a free form. It is in the form of aimed at poems about the ruling party's mismanagement, analyzed the background of the incident and the feelings of the characters, and advanced one's own opinion. This is a case that reveals not only the insight of a scholar of scripture but also the aspect of a writer.&#x0D; As a result of examining the method of its commentary on 『Shijing』, It was found that the pattern consisted of two main parts. First, Reinforcement of reasoning through narrative. Second, Reinforcement of reasoning through lyricism. First, It is to secure the trust of the readers, and reflects his bibliographical opinion, which judged that historical records close to the time of the writing of 『Shijing』should be used as a basis. It was intended to elicit sympathy from readers, and his rhetorical view was reflected in his understanding of the literary quality of 『Shijing』as ‘inducing empathy through empathy’. Therefore, his way of interpreting the scriptures can be said to be a case of succession and transformation of the merits of Philology and rhetoric.
Natural Evolution Strategy for Mixed-Integer Black-Box Optimization

This paper proposes a natural evolution strategy (NES) for mixed-integer black-box optimization (MI-BBO) that appears in real-world problems such as hyperparameter optimization of machine learning and materials design. This problem is difficult to optimize because plateaus where the values do not change appear when the integer variables are relaxed to the continuous ones. CMA-ES w. Margin that addresses the plateaus reportedly showed good performance on MI-BBO benchmark problems. However, it has been observed that the search performance of CMA-ES w. Margin deteriorates when continuous variables contribute more to the objective function value than integer ones. In order to address the problem of CMA-ES w. Margin, we propose Distance-weighted eXponential Natural Evolution Strategy taking account of Implicit Constraint and Integer (DX-NES-ICI). We compare the search performance of DX-NES-ICI with that of CMA-ES w. Margin through numerical experiments. As a result, DX-NES-ICI was up to 3.7 times better than CMA-ES w. Margin in terms of a rate of finding the optimal solutions on benchmark problems where continuous variables contribute more to the objective function value than integer ones. DX-NES-ICI also outperformed CMA-ES w. Margin on problems where CMA-ES w. Margin originally showed good performance.
A Study Guide to Uncover Industrial Hazards

Industry, environments, workers, information security, and licenced invention rights typically make up an industrial ecosystem. Working carefully in a steady industrial environment has always been dangerous, and protecting it is an enormous challenge. This study's primary goals are to accurately reduce potential risk, strictly control risks, and monitor events in the industrial ecosystem. To help foresee potential risks related to the industrial ecosystem, this research will rigorously evaluate the implied commitment of Internet-of-Things (IoT) advances. It might typically identify the precise evaluation of IoT-based devices for regularly avoiding and carefully navigating the industrial environment. The thorough approach cautiously suggests purposefully lowering likely risks in the industrial setup. The suggested application successfully achieves the highest hit rate with the fewest false positives and optimises the monitoring efforts, resulting in less time spent on maintenance and lower operating expenses. An overview and a review of the relevant literature are included at the beginning of this research article. The research then conceptualises the ethical thinking by dissecting how fundamental causes typically result in helpless circumstances in the industrial environment. The most disaster-prone areas for employees, the environment, and industry were determined through this research. To forecast the risks in the sector, a discourse analysis of unstructured data including video, pictures, and text information utilising CNN, NLP, and other mixed algorithms is presented. Process discovery and computational learning models are covered by the topic execution technique. It provides a quick overview of how to create an intellectual learning system that incorporates past data, eliminates duplicates, and determines the logical connections and relative weight of the various features. One of the difficulties in an industry that is well-known is occupational dangers. The drivers frequently have too much work. To measure the driver's level of weariness, a straightforward test is used in this research report. The research's future direction and major obstacles are then examined.
Traffic sign recognition and vehicle using phyton

Traffic signs are a basic piece of our everyday lives. Traffic sign acknowledgment is the process of consequently recognizing which of the accompanying class the sign belongs to. The prior computer vision procedures required lots of difficult work in data processing. Now, profound learning methods have come to the rescue and to assemble a traffic acknowledgment framework for autonomous vehicles. This article gives a brief description about the traffic sign recognition to classify the traffic signs in order to properly parse and understand the roadway and also to detect the objects around it. In addition to this, a system is proposed to count the vehicles in a specific region.
Detection of Requirements Discordances Among Stakeholders Under Fuzzy Environment

Software requirements (SRs) elicitation is one of the key sub-processes of software development that is used in identifying the stakeholders’ need. Several stakeholders participate in the requirements elicitation process. During the analysis, it has been observed that there are requirements discordances among stakeholders because each stakeholder has a different opinion on the same requirement. Based on our review, we found that one of the reasons for the failure of software is the disagreement among the stakeholders. Few methods have been developed for the detection of discordances among the stakeholders in which crisp values were used to record the stakeholders’ preferences on SRs, and small-scale software systems were used during the analysis. Linguistic variables find their relevance in real-life applications to identify the preferences of requirements over other requirements rather than crisp values. Therefore, to deal with this issue, a method is presented to detect the requirements discordances among stakeholders in which fuzzy logic is used to address imprecision and vagueness. An example is shown to discuss the applicability of the proposed method.
Part-Wise Adaptive Topology Graph Convolutional Network for Skeleton-Based Action Recognition

Human action recognition is a computer vision challenge that involves identifying and classifying human movements and activities. The behavior of humans comprises movements of multiple body parts, and Graph Convolutional Networks (GCNs) have emerged as a promising approach for this task. However, most contemporary GCN methods perform graph convolution on the entire skeleton graph without considering that the human body consists of distinct body parts. To address these shortcomings, we propose a novel method that optimizes the representation of the skeleton graph by designing temporal and spatial convolutional blocks while introducing the Part-wise Adaptive Topology Graph Convolution (PAT-GC) technique. PAT-GC adaptively learns the segmentation of different body parts and dynamically integrates the spatial relevance between them. Furthermore, we utilize hierarchical modeling to divide the skeleton graph, capturing a more comprehensive representation of the human body. We evaluate our approach on three publicly available large datasets: NTU RGB + D 60, NTU RGB + D 120, and Kinetics Skeleton 400. Our experimental results demonstrate that our approach achieves state-of-the-art performance, thus validating the efficiency of our proposed technique for human action recognition.
Pid-Inspired Modifications in Response Threshold Models In Swarm Intelligent Systems

In this study, we investigate the effectiveness of using the PID (Proportional - Integral - Derivative) control loop factors for modifying response thresholds in a decentralized, non-communicating, threshold-based swarm. Each agent in our swarm has a set of four thresholds, each corresponding to a task the agent is capable of performing. The agent will act on a particular task if the stimulus is higher than its corresponding threshold. The ability to modify their thresholds allows the agents to specialize dynamically in response to task demands. Current approaches to dynamic thresholds typically use a learning and forgetting process to adjust thresholds. These methods are able to effectively specialize once, but can have difficulty re-specializing if the task demands change. Our approach, inspired by the PID control loop, alters the threshold values based on the current task demand value, the change in task demand, and the cumulative sum of previous task demands. We show that our PID-inspired method is scalable and outperforms fixed and current learning and forgetting response thresholds with non-changing, constant, and abrupt changes in task demand. This superior performance is due to the ability of our method to re-specialize repeatedly in response to changing task demands.
Effects of utterance length on word and phrase durations

This study examined the effects of utterance length on word and phrase durations to investigate how the planned length of an utterance influences the rate of speech. An experiment was conducted in which the visual stimuli cued the production of sentences with one, two, or three subject noun phrases (NPs)—e.g., “Nine green rhinos and eight red weasels and eight blue llamas live in the zoo.” In addition, a novel, delayed stimuli condition was tested in which the visual stimuli that cued non-initial NPs (“eight red weasels and eight blue llamas”) were presented after participants started production, and thus sentence length changed after utterance initiation. Word and NP durations were analyzed. NP durations were longer in sentences with more NPs, and moreover, durational increases were primarily observed in the rightmost words of the NPs. These findings are important first because they show that the previously observed effects of number of words on word durations extend to phrases, and second because they show that phrase duration effects are localized to phrase ends. When the utterance length changed after the start of production, participants lengthened the end of the initial NP and the following conjunction to plan for the newly presented NPs.
TAPE: Temporal Attention-Based Probabilistic Human Pose and Shape Estimation

Reconstructing 3D human pose and shape from monocular videos is a well-studied but challenging problem. Common challenges include occlusions, the inherent ambiguities in the 2D to 3D mapping and the computational complexity of video processing. Existing methods ignore the ambiguities of the reconstruction and provide a single deterministic estimate for the 3D pose. In order to address these issues, we present a Temporal Attention based Probabilistic human pose and shape Estimation method (TAPE) that operates on an RGB video. More specifically, we propose to use a neural network to encode video frames to temporal features using an attention-based neural network. Given these features, we output a per-frame but temporally-informed probability distribution for the human pose using Normalizing Flows. We show that TAPE outperforms state-of-the-art methods in standard benchmarks and serves as an effective video-based prior for optimization-based human pose and shape estimation.
Research on local path planning of intelligent vehicle based on improved dynamic window approach

In order to make the intelligent vehicle run safely and improve the applicability of local path planning algorithm in intelligent vehicle, the paper presents a local path planning method for intelligent vehicles. Firstly, establish vehicle model of Ackerman steering. In addition, the minimum turning radius constraint is added to the speed screening mechanism based on the traditional dynamic window approach. Then, in order to avoid excessive changes in the driving speed of intelligent vehicles, the curvature retention evaluation index is added to the evaluation function. The simulation results show that the improved algorithm meets the requirements of intelligent vehicle dynamic obstacle avoidance and can plan a safe and reasonable path.
Forecasting User Interests Through Topic Tag Predictions in Online Health Communities

The increasing reliance on online communities for healthcare information by patients and caregivers has led to the increase in the spread of misinformation, or subjective, anecdotal and inaccurate or non-specific recommendations, which, if acted on, could cause serious harm to the patients. Hence, there is an urgent need to connect users with accurate and tailored health information in a timely manner to prevent such harm. This article proposes an innovative approach to suggesting reliable information to participants in online communities as they move through different stages in their disease or treatment. We hypothesize that patients with similar histories of disease progression or course of treatment would have similar information needs at comparable stages. Specifically, we pose the problem of predicting topic tags or keywords that describe the future information needs of users based on their profiles, traces of their online interactions within the community (past posts, replies) and the profiles and traces of online interactions of other users with similar profiles and similar traces of past interaction with the target users. The result is a variant of the collaborative information filtering or recommendation system tailored to the needs of users of online health communities. We report results of our experiments on two unique datasets from two different social media platforms which demonstrates the superiority of the proposed approach over the state of the art baselines with respect to accurate and timely prediction of topic tags (and hence information sources of interest).
Improving NASA’s Earth Satellite and Model Data Discoverability for Interdisciplinary Research, Applications, and Education

Since the Internet era began, numerous earth science data services have been developed to facilitate data discovery (e.g., data sources, documents, facts, visualization, opinions) and data access for research and application activities. For example, a large collection of NASA&rsquo;s earth science data has been made searchable and freely downloadable over the Internet. Some value-added services even allow users to analyze and visualize many variables online (e.g., 2,000+ in NASA Giovanni) without downloading data and software. However, finding and discovering suitable datasets and information for interdisciplinary research (involving two or more scientific disciplines), applications, education, and other emerging activities (e.g., water, food, energy nexus) has been a challenge not only for users, especially for those who are unfamiliar with scientific disciplines, measurements, or models, but also for data producers and service developers who want their data to be discoverable. NASA earth science data are currently archived and distributed at twelve NASA discipline-oriented Distributed Active Archive Centers (DAACs). Even though most datasets are online, search results often contain many similar datasets with limited information for self-guided or heuristic dataset discovery, so some users simply send an inquiry to DAAC support staff for advice. Conducting interdisciplinary research often requires multiple datasets from different data repositories, which can make it even harder to find and discover suitable datasets without additional data services to accommodate these emerging user needs. In this article, we assess current data discovery practices and publications (e.g., reports from working groups) to identify challenges and make actionable recommendations for improving earth science data discoverability and facilitating interdisciplinary activities. Highlights: Status review of earth science data discoverability for interdisciplinary research and applications at NASA GES DISC. Review of data discovery research and working group activity. Discuss challenges and opportunities for interdisciplinary data discovery with recommendations for practitioners.
Structural features of the model of formation of students' readiness for design and research activities in the system of mathematical education

The subject of the study is the model and pedagogical conditions for the formation of design and research readiness of future mathematics teachers. The purpose of the study is to determine the structure of the model of formation of readiness of students of pedagogical specialties for design and research activities in the system of mathematical education. The article examines the potential of the model and pedagogical conditions for the formation of students' readiness for project research activities based on the application of the project method. The conditions of its integration into the university mathematics course are presented. The article describes the model of formation of readiness of students of pedagogical specialties for design and research activities in the system of mathematical education. The scientific novelty of the work lies in the theoretical substantiation and development of a model of design and research activities of future mathematics teachers, which is implemented using interdisciplinary connections and is aimed at increasing students' interest in vocational education, obtaining fundamental knowledge. The study used a set of theoretical and practical methods: analysis, synthesis, generalization of psychological and pedagogical literature on the problem of research, pedagogical experiment, observation. As a result, it is proved that this model and pedagogical conditions contribute to the development of creative abilities of students, as well as the development of design and research activities and increase interest in obtaining professional knowledge.
Path Planning of Quadrotor Using A* and LQR

Quadrotor is an aerial robotic vehicle that can play a pivotal role in areas like medicine delivery, surveillance, and mapping. These unmanned aerial vehicles have a capability to take off and land from compromised areas as well as difficult terrains. Control mechanism and Path Planning form important research areas of Quadrotors. A Quadrotor model is simulated using Simulink. Backstepping controlling method is used to achieve a stable controlled system. Next, Path Planning techniques, A* (A star) and LQR algorithms are implemented for the Quadrotor system. The A star algorithm was able to reach the target even in the presence of obstacles whereas the LQR technique was able to reach the destination in less time in the absence of any obstacles.
Multi-person tracking for virtual reality surrounding awareness

Virtual reality devices are designed to cover our vision so we are unaware of our surroundings and completely emerged into a different world. This limits VR user's ability to move freely in crowded areas, such as classrooms, which limits the user interactions in VR. We present a framework that enables VR users to see other people around them inside the VR environment with the help of an external 3D depth camera. The proposed framework enables multi-VR users to use one tracking camera and provides a cost-effective solution. The proposed framework can also help in performing collaborative activities.
Techniques for Immersive Data Storytelling

Developments in immersive technologies has led to the emergence of immersive analytics. Immersive analytics takes data visualisation beyond the traditional 2D displays. At the same time, data storytelling has become a prevalent method for communicating data. Combining both data storytelling and immersive analytics gives rise to “immersive data storytelling”. Immersive data storytelling refers to telling visually compelling data stories in an immersive environment such as as virtual reality, augmented reality, mixed reality and 360 videos. Currently, immersive data storytelling is under-explored from an academic stand point. The research presented here aims to develop techniques and guidelines for effective data storytelling in various immersive environments.
Dynamic Scene Adjustment for Player Engagement in VR Game

Virtual reality (VR) produces a highly realistic simulated environment with controllable environment variables. This paper proposes a Dynamic Scene Adjustment (DSA) mechanism based on the user interaction status and performance, which aims to adjust the VR experiment variables to improve the user's game engagement. We combined the DSA mechanism with a musical rhythm VR game. The experimental results show that the DSA mechanism can improve the user's game engagement (task performance).
Optimal Resource Management for Hierarchical Federated Learning Over HetNets With Wireless Energy Transfer

Remote monitoring systems analyze the environment dynamics in different smart industrial applications, such as occupational health and safety, and environmental monitoring. Specifically, in industrial Internet of Things (IoT) systems, the huge number of devices and the expected performance put pressure on resources, such as computational, network, and device energy. Distributed training of Machine and Deep Learning (ML/DL) models for intelligent industrial IoT applications is very challenging for resource limited devices over heterogeneous wireless networks (HetNets). Hierarchical Federated Learning (HFL) performs training at multiple layers offloading the tasks to nearby Multi-Access Edge Computing (MEC) units. In this paper, we propose a novel energy-efficient HFL framework enabled by Wireless Energy Transfer (WET) and designed for heterogeneous networks with massive Multiple-Input Multiple-Output (MIMO) wireless backhaul. Our energy-efficiency approach is formulated as a Mixed-Integer Non-Linear Programming (MINLP) problem, where we optimize the HFL device association and manage the wireless transmitted energy. However due to its high complexity, we design a Heuristic Resource Management Algorithm, namely H2RMA, that respects energy, channel quality, and accuracy constraints, while presenting a low computational complexity. We also improve the energy consumption of the network using an efficient device scheduling scheme. Finally, we investigate device mobility and its impact on the HFL performance. Our extensive experiments confirm the high performance of the proposed resource management approach in HFL over HetNets, in terms of training loss and grid energy costs.
The Liability of a Share Hosting Platform with General Knowledge of Infringing Content

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