Reviewerly/exHarmony huggingface dataset:
Pre-Trained Models for Intent Classification in Chatbot: Comparative Study and Critical Analysis

The emergence of pre-trained models based on deep learning has considerably enhanced the development of many applications, such as chatbots. These models can be refined for specific tasks to improve chatbot accuracy. The core of the chatbot is its ability to understand the user’s intent through its Natural Language Understanding (NLU) component. Within NLU, intent classification is a central task. Recently, transformer models have revolutionized the resolution of this task by capturing the semantic relations between words in a sentence. This article presents a comparative study and critical analysis of four transformer models, which are Bert, Albert, Roberta, and Gpt2, to identify which offers the best accuracy for an existing dataset for the intent classification task.
Real-Time Collision-Free Motion Planning and Control for Mobile Manipulation with Quadrupeds

In this paper, we study the mobile manipulation problem with a quadruped-arm system in cluttered enviornment. To address this problem systematically, we propose a real-time collision-free motion planning and control framework that distributes the collision avoidance task into multiple layers. In details, we decompose the overall legged mobile manipulator system into a quadruped subsystem and an arm subsystem, plan the collision-free trajectories for the base and the end-effector while respecting their inherent kinematic constraint. Then with the guidance of the base and end-effector trajectories, we can find feasible velocity commands for the quadruped and the arm respectively by solving a quadratic programming problem with the collision-free constraint to guarantee safety of the whole body system. After getting the velocity commands for the quadruped, we further synthesize a customized model predicitve controller to track the given reference while incorporating the effect of the moving arm. The framework is validated by the navigation and manipulation tasks in simulation with a real legged-mobile manipulator system. In the experiments, the average computation time for our motion planner is 76 ms and the planned trajectory is smoother and faster than the existing motion planners. Simultaneously, the collision avoidance requirement can be obeyed during the whole process.
Increasing Exploration in Sampling-based Motion Planning for Manipulator

Solutions for a wide range of applications, including industrial robots, drones, and autonomous vehicles, have been provided via motion planning techniques. Due to their high dimensionality, manipulators are more challenging to implement, mainly when workplace impediments and their end-effectors restrict them and must adhere to specific orientation requirements. It is difficult in this situation to carry out the task’s motion planning. Planners often utilized have low success rates or require a lengthy time to finish tasks.The sampling-based motion planning method is used in this research to explore the configuration space by sampling the properties of the workspace under investigation. They combine the advantages of local exploration generated by a single query planner and extensive exploration produced by several query planners. The master tree is generated in the free space with a high sampling success rate to reduce samples and arithmetic power. Optimize the distribution of sample locations near obstacles while producing free arms in the constrained area with a low sampling success rate to increase the workspace exploration rate. The kinematic challenge in manipulator motion planning is resolved by iteratively projecting joint values onto the confined pop. According to simulation data, the proposed method takes less time and follows shorter paths while maintaining a high success rate. Additionally, the benefits become more apparent as the workplace becomes more constrained.
Uncertainty in Bayesian Reinforcement Learning for Robot Manipulation Tasks with Sparse Rewards

This paper aims to explore the application of Bayesian deep reinforcement learning (BDRL) in robot manipulation tasks with sparse rewards, focusing on addressing the uncertainty in complex and sparsely rewarded environments. Conventional deep reinforcement learning (DRL) algorithms still face significant challenges in the context of robot manipulation tasks. To address this issue, this paper proposes a general algorithm framework called BDRL that combines reinforcement learning algorithms with Bayesian networks to quantify the model uncertainty, aleatoric uncertainty in neural networks, and uncertainty in the reward function. The effectiveness and generality of the proposed algorithm are validated through simulation experiments on multiple sets of different sparsely rewarded tasks, employing various advanced DRL algorithms. The research results demonstrate that the DRL algorithm based on the Bayesian network mechanism significantly improves the convergence speed of the algorithms in sparse reward tasks by accurately estimating the model uncertainty.
An Improved Slime Mold Algorithm for Cooperative Path Planning of Multi-UAVs*

Unmanned Aerial Vehicles (UAV) are widely used in military and civilian applications as an emerging control technology. Due to the stringent coordination requirements and constraints, multi-UAV path planning in three-dimensional complex environments is more challenging. To solve this problem, A collaborative method for planning multiple UAVs paths in complex environments is proposed. The SMA algorithm is improved in terms of the initial position of the population, the feedback factor and the individual position update method, which improves the convergence speed and convergence accuracy as well as the robustness of the algorithm. Simulation results in complex cases show that the proposed algorithm can obtain effective paths in multi-UAV collaborative path planning, and the overall performance is improved by 9.5% compared to the SMA algorithm.
A rapid calibration method of underwater structured light 3D reconstruction system for hull bottom cleaning and inspection

Biofouling on ship hull reduces speed and increases energy consumption. An effective evaluation of fouling status is needed to optimize the cleaning schedule and quantify the cleaning performance. However, the specialized equipment for underwater 3D reconstruction is costly and difficult to calibrate quickly, which leads to time-consuming data collection and processing. For above problem, our study proposed a novel method of rapid calibration, which can improve the continuous operation capability of underwater 3D reconstruction system. Firstly, camera calibration parameters is obtained by a single-viewpoint model, and a fast correction of the pixel coordinates of feature points is utilized based on the radial distortion properties of the camera. Secondly, the fit of the light plane in the camera coordinate system is improved by extracting the feature points of the linear laser projection on the special stereo calibration target and establishing the constraint relationship of this points. Finally, the velocity vector in the specified world coordinate system is calibrated by calculating the translation vector from the camera coordinate system to the world coordinate system for different frame images, and generates 3D point clouds of the target object. The effectiveness of the proposed method is verified by underwater 3D reconstruction experiments.
An Improved Local Integration Algorithm For Polarization Gradient Fields*

The polarization three-dimensional reconstruction technology mainly uses polarization information to construct the normal gradient field, and integrate the normal gradient field to restore the surface morphology of the object. Aiming at the problems of long iteration time of local integrator algorithm and error introduced by target edge, an improved method of local integrator is proposed: the target mask extraction algorithm based on polarization degree-refractive index is used to segment the image, the target mask region is subdivided internally and at the edge, and different differential iterations are performed for different regions of the target mask to restore the depth. Finally, the improved local integrator and the classical local integrator are compared experimentally, the depth map is reconstructed, and the influence of the number of iterations on the accuracy of the depth map is analyzed. In addition, the ideal hemispherical gradient field and the hemispherical gradient field after adding noise are simulated to verify the integration performance of different integrators.
Understanding How to Diversify the Cybersecurity Workforce: A Qualitative Analysis

A robust cybersecurity workforce is critical for protection against a range of malicious attacks. However, it has been noted that there are many vacancies and a shortage of individuals entering the cybersecurity workforce. This workforce shortage has partly been attributed to the lack of diversity in the cybersecurity field, with women, African Americans, and Hispanics remaining underrepresented in educational and professional settings. Using a qualitative approach, this work sought to investigate what led underrepresented minorities currently involved in cybersecurity to the industry, with the goal of determining methods to attract and diversify the workforce. A thematic analysis was conducted using data collected during interviews with 23 participants including underrepresented minority students, underrepresented minority professionals, college instructors, and a high school administrator. The interview questions aimed to address (a) what attracted minorities to the field, (b) how they overcame educational and professional roadblocks, (c) how they built non-technical knowledge, skills, and attitudes, and (d) how they maintained engagement. Findings revealed 17 themes that were related to characteristics of (a) the learner, (b) the instruction, and (c) the environment. Based on these findings, recommendations are presented to illustrate how these themes can be implemented by instructors with the goal of increasing the participation and involvement of underrepresented minorities and fostering diversity in the cybersecurity field.
AR Presentation of Team Members’ Performance and Inner Status to Their Leader: A Comparative Study

Real-time and high-intensity teamwork management is complex, as team leaders must ensure good results while also considering the well-being of team members. Given that stress and other factors directly impact team members’ output volume and error rate, these team leaders must be aware of and manage team stress levels in combination with allocating new work. This paper examines methods for visualizing each team member’s status in mixed reality, which, combined with a simulated stress model for virtual team members, allows the team leader to take into account team members’ individual statuses when choosing whom to allocate work. Using simulated Augmented Reality in Virtual Reality, a user study was conducted where participants acted as team leaders, putting simulated team members under stress by allocating several required work tasks while also being able to review the stress and status of each team member. The results showed that providing Augmented Reality feedback on team members’ internal status increases the team’s overall performance, as team leaders can better allocate new work to reduce team members’ stress-related errors while maximizing output. Participants preferred having a graph representation for stress levels despite performing better with a text representation.
Comparative Analysis Cryptocurrencies Versus Stocks

As the limit between reality and the digital field seems to blur more and more, and this fusion has an impact on several fields of study, such as accounting and audit, the similarity between cryptocurrencies and the stocks of companies has to be analysed, as investment items. In the absence of comparisons between the cryptocurrency market and the capital market, investors cannot apply the proper accounting treatment for the activity in which they are involved. Consequently, the aim of this article is setting the existing characteristics and correlations between the cryptocurrency market and the securities market. Besides, the authors consider that this article may contribute to the accounting research, providing useful information to investors and regulatory authorities in accounting. Cryptocurrencies and the prices of stocks are somehow correlated after considering the volatility of cryptocurrency. Many of the factors affecting the prices of stocks also have an impact on the prices of cryptocurrencies. Investors and traders treat cryptocurrency in the same way in which they treat the stocks, so that prices tend to have the same trend. We should be cautious when we invest in cryptocurrencies, as they are still new – the market explores its role as an asset. At the same time, the analysis and presentation of certain statistical data was pursued, like the number of companies listed at the largest stock exchange operators in the world, the stock exchange capitalization, the number of cryptocurrencies at world level, the market capitalization of cryptocurrencies, resulting in a similarity between the two studied elements.
Harnessing Causal Structure Alignment for Enhanced Cross-Domain Named Entity Recognition

Cross-domain named entity recognition (NER) is a crucial task in various practical applications, particularly when faced with the challenge of limited data availability in target domains. Existing methodologies primarily depend on feature representation or model parameter sharing mechanisms to enable the transfer of entity recognition capabilities across domains. However, these approaches often ignore the latent causal relationships inherent in invariant features. To address this limitation, we propose a novel framework, the Causal Structure Alignment-based Cross-Domain Named Entity Recognition (CSA-NER) framework, designed to harness the causally invariant features within causal structures to enhance the cross-domain transfer of entity recognition competence. Initially, CSA-NER constructs a causal feature graph utilizing causal discovery to ascertain causal relationships between entities and contextual features across source and target domains. Subsequently, it performs graph structure alignment to extract causal invariant knowledge across domains via the graph optimal transport (GOT) method. Finally, the acquired causal invariant knowledge is refined and utilized through the integration of Gated Attention Units (GAUs). Comprehensive experiments conducted on five English datasets and a specific CD-NER dataset exhibit a notable improvement in the average performance of the CSA-NER model in comparison to existing cross-domain methods. These findings underscore the significance of unearthing and employing latent causal invariant knowledge to effectively augment the entity recognition capabilities in target domains, thereby contributing a robust methodology to the broader realm of cross-domain natural language processing.
Effect STEAM Based Discovery Learning Model on Students Thinking Ability: Meta-Analysis Study

This study aims to determine the effect of the STEAM-based discivery learning model on students' critical thinking skills. This research is a type of meta-analysis research. The researchsample came from 17 national and international journals. Samples were obtained through databases in the journals Springer, Wiley, ScienceDirect, Google Scholar, Taylor of Francis, SAGE, and the Education Resources Information Center (ERIC). The inclusion criteria are research published in 2019-2023; The study had experimental and control classes, journals had to be indexed Science and Technology Index (SINTA), Scopus, and Web of Science (WOS) and the sample size in this study was 1,078 students. Data analysis calculated the value of summary effect size, heterogeneity test and publication bias with JASP 0.8.0.4 application. These results conclude that the summary effect size value (rE = 0.902; p &lt; 0.001) then this result shows that the STEAM-based discovery learning model has a positive effect on students' critical thinking skills at high criteria. This finding shows that the application of the STEAM-based discovery learning model has a significant influence on students' critical thinking skills in learning at school.
The Role of Language in the Survival of Bangladeshi Theatre Artists during the COVID-19 Pandemic: A Perspective on Resurging Society's Hope and Changing Realities

The COVID-19 pandemic has had a profound impact on the theatre industry and its performers, causing psychological and socio-economic distress. This study examines the survival challenges faced by theatre artists in Meherpur, Bangladesh, during the pandemic and explores the role of language in their efforts to inspire hope and rejuvenate society. The paper investigates how these artists utilized online platforms and employed their native language, Bengali (L1), along with English, to communicate with the local population. By analyzing qualitative interviews with fifteen artists and administering a quantitative questionnaire to fifty-five participants, this study reveals the artists' resilience in overcoming psychological trauma and economic distress. It illustrates how their use of language, including English, facilitated their connection with the community, providing a source of support and encouragement during the lockdown period.
Predicting Postoperative Hospital Stays Using Nursing Narratives and the Reverse Time Attention (RETAIN) Model: Retrospective Cohort Study

Abstract Background Nursing narratives are an intriguing feature in the prediction of short-term clinical outcomes. However, it is unclear which nursing narratives significantly impact the prediction of postoperative length of stay (LOS) in deep learning models. Objective Therefore, we applied the Reverse Time Attention (RETAIN) model to predict LOS, entering nursing narratives as the main input. Methods A total of 354 patients who underwent ovarian cancer surgery at the Seoul National University Bundang Hospital from 2014 to 2020 were retrospectively enrolled. Nursing narratives collected within 3 postoperative days were used to predict prolonged LOS (≥10 days). The physician’s assessment was conducted based on a retrospective review of the physician’s note within the same period of the data model used. Results The model performed better than the physician’s assessment (area under the receiver operating curve of 0.81 vs 0.58; P =.02). Nursing narratives entered on the first day were the most influential predictors in prolonged LOS. The likelihood of prolonged LOS increased if the physician had to check the patient often and if the patient received intravenous fluids or intravenous patient-controlled analgesia late. Conclusions The use of the RETAIN model on nursing narratives predicted postoperative LOS effectively for patients who underwent ovarian cancer surgery. These findings suggest that accurate and interpretable deep learning information obtained shortly after surgery may accurately predict prolonged LOS.
Efektifitas Instagram dalam Pembelajaran Bahasa Inggris di Program Studi Teknik Informatika

One of the main subjects that students must study in their chosen field is English. English for specific purposes (ESP) is the ability to speak English suitable for a specific purpose. Writing is one of the skills whose performance is less than optimal for students in the Informatics Engineering study program. In an era where digital technology has become an important consumption and need for society, social media has become one of the things that young people really like. Therefore, this research uses social media, especially Instagram, as a teaching tool and can motivate students to learn English. Data collection techniques include written results, pre and post tests, as well as observation and documentation. To determine the effectiveness of Instagram social media as a learning tool, it will be evaluated based on the results of a student questionnaire. The sample for this research was 30 IT students. The steps in the data collection process in this research include, for example. (1) observation of the research object, (2) socialization stage to students about learning through social media, especially Instagram, (3), implementation of this research, in this phase students receive material at the beginning where they not only get the material, but also must look for other supporting materials and discuss and receive suggestions and comments from lecturers and friends, (4) The final stage is the introduction stage of using Instagram as a means of discussion and debate. as a place to publish student work.
Using Descriptive Logic in Intelligent Educational Systems

The purpose of the article is to study and solve the problems of using the basic principles of the modern formalism of descriptive (terminological) logic in intelligent learning systems that use knowledge bases of educational content. The research methods are the main methodological approaches and technological tools for the development of intelligent learning systems. In particular, systemic and comparative analyses to identify the features of intelligent learning systems, the method of expert evaluation, which involves the analysis of literature and information resources, interviews and surveys of experts in the field of information technology and mathematical logic, as well as the process of developing and testing an intelligent system. The scientific novelty of the study is the development of a system for obtaining expert knowledge for an intelligent system that combines the formation of an expert knowledge base, and the formalism for representing which is descriptive logic. The proposed approach of formalized knowledge representation using descriptive logic can be used in the development of authors’ intelligent learning systems in the field of online education and cognitive skills development. Conclusions. The scope of application of descriptive logic in intelligent systems is constantly expanding, increasing the capabilities of these systems to formally derive new knowledge, including in conditions of incomplete certainty. Descriptive logic, used in intelligent learning systems, provides ample opportunities for building a conclusion (i.e., forming an answer), in particular in situations such as forming and answering various types of queries to the system’s knowledge base (e.g., fully defined and clearly formulated, incompletely defined and vaguely formulated, completely vague, incompletely defined and clearly formulated, completely vague, completely vague and vaguely formulated), helping to specify the query. To do this, it is necessary to formulate a rigorous number system (to build an axiomatic theory). Thus, as a means of solving semantic and logical problems, descriptive logic has become a unique and powerful mechanism for representing knowledge. The successful use of descriptive logic in the author’s intelligent educational system will ensure the improvement of the quality of the educational process; monitoring of the learning process and control of its results; will facilitate the integration of this system into many modern information educational systems; will provide various consultations, advice, interpretations, explanations regarding the educational content or educational process, as well as a wide range of topics of educational content and tests of different levels.