Reviewerly/exHarmony huggingface dataset:
HG-CBS Planner: Heuristic Group-based Motion Planning for Multi-robot

Multi-Agent Path Finding (MAPF) has been widely studied in the field of artificial intelligence and multi-agent path search, and researchers have made improvements to make such algorithms applicable to actual nonholonomic constrained robots. In this paper, the collision detection is improved by body-occupied collision detection, which improves the efficiency of collision detection. In addition, to solve the problem of low efficiency in centralized search for multi-robot solutions, reasonable grouping is performed to reduce the number of collision nodes expanded in the centralized Conflict-based search(CBS), and feasible results are obtained faster. At the same time, the heuristic multi-robot search based on collision frequency is improved to reduce the search time. Finally, we constructed the trajectory smoothing problem based on the multi-robot path search results and applied the solution to real robot trajectory tracking. Our research provides valuable insights into solving trajectory planning problems for nonholonomic constrained mobile robots in practical scenarios.
BATINeT: Background-Aware Text to Image Synthesis and Manipulation Network

Background-Induced Text2Image (BIT2I) aims to generate foreground content according to the text on the given background image. Most studies focus on generating high-quality foreground content, although they ignore the relationship between the two contents. In this study, we analyzed a novel Background-Aware Text2Image (BAT2I) task in which the generated content matches the input background. We proposed a Background-Aware Text to Image synthesis and manipulation Network (BATINet), which contains two key components: Position Detect Network (PDN) and Harmonize Network (HN). The PDN detects the most plausible position of the text-relevant object in the background image. The HN harmonizes the generated content referring to background style information. Finally, we reconstructed the generation network, which consists of the multi-GAN and attention module to match more user preferences. Moreover, we can apply BATINet to text-guided image manipulation. It solves the most challenging task of manipulating the shape of an object. We demonstrated through qualitative and quantitative evaluations on the CUB dataset that the proposed model outperforms other state-of-the-art methods.
A Study on using Puppets to Teach Children in the Foundational Years

The research paper is based on using of puppets in the classroom to promote children's learning. It talks about the different ways through which puppets can be used to help children learn, the different challenges associated with the use of puppets in the classroom and what can be done to facilitate better learning through the use of puppets.
Interactive Learning for Network Anomaly Monitoring and Detection with Human Guidance in the Loop

With the advancement in big data and cloud computing technology, we have witnessed tremendous developments in applying intelligent techniques in network operation and management. However, learning- and data-based solutions for network operation and maintenance cannot effectively adapt to the dynamic security situation or satisfy administrators' expectations alone. Anomaly detection of time-series monitoring indicators has been a major challenge for network administrative personnel. Monitored indicators in network operations are characterized by multiple instances with high dimensions and fluctuating time-series features and rely on system resource deployment and business environment variations. Hence, there is a growing consensus that conducting anomaly detection with machine intelligence under the operation and maintenance personnel's guidance is more effective than solely using learning and modeling. This paper intends to model the anomaly detection task as a Markov Decision Process and adopts the Double Deep Q-Network algorithm to train an anomaly detection agent, in which the multidimensional temporal convolution network is applied as the principal structure of the Q network and the interactive guidance information from the operation and maintenance personnel is introduced into the procedure to facilitate model convergence. Experimental results on the SMD dataset indicate that the proposed modeling and detection method achieves higher precision and recall rates compared to other learning-based methods. Our method achieves model optimization by using human-computer interactions continuously, which guarantees a faster and more consistent model training procedure and convergence.
Combination of Classroom and Distance Learning in the Conditions of Deficiency of Contact Hours

Distance learning methods are already become an essential part of education, but their widespread use has also revealed some problems. Despite the abundance of forms, distance education cannot completely replace the classical method of face-to-face interaction between a teacher and a student. For more effective learning, distance and full-time forms should not compete, but mutually complement each other. In such a situation, the student has maximum opportunities both for obtaining information and for the most effective understanding of it. If, according to the curriculum, the number of classroom lessons is small, the remote part in the form of recorded task analyzes or explanations of methods becomes especially relevant. In this article, I propose one variant of combining full-time and remote parts of practical classes in the discipline “Computational Mathematics” for students of a technical university. Based on my experience I will consider which themes from the curriculum should be necessarily analyzed in a practical lesson, and which ones can be offered as independent work using a pre-recorded video course.
Artificial Intelligence and Blockchain Technology in Healthcare System : A Systematic Review

Blockchain technology considers a decentralized system in which a focal authority is pointless. As a result of the work of cryptographic standards, monetary dealings can be relied upon while likewise being totally protected. To a great extent because of the ascent in worth of digital currencies, blockchain technology has as of late become in vogue and spread to different regions. The healthcare business is one that could benefit extraordinarily from blockchain technology on account of the developing accentuation on patient-focused care, the need to coordinate siloed systems, and the longing to work on the unwavering quality of EHRs. In this thorough review, we look at the latest discoveries from blockchain concentrates on in the clinical calling. The objective is to reveal insight into the technology's possible advantages and cause to notice the hardships and likely future improvements of blockchain research in the healthcare area. The paper starts with some initial material prior to jumping into an itemized clarification of the techniques utilized here. A synopsis of the bibliometrics, an assessment of the data and its credits, and the discoveries of a writing quality evaluation follow. Eventually, the investigation results are examined. Data sharing, wellbeing record the executives, and authorization the board are where blockchain technology sparkles in this field of study. The option is an uncommon event. Most investigations have as their objective the presentation of a few new systems, designs, or models into the field. The outcomes likewise show that the majority of the review uncovers no model execution or execution subtleties, and that specialized insights regarding the utilized blockchain parts are not given in a large portion of the dissected papers. Indeed, even with a functioning model, information in regards to the Blockchains parts is frequently held back.
COGNITIVE CULTURE: SUBJECTIVE LEVELS, OPERATIONAL MECHANISMS AND CREATIVE PRODUCTS

Objective of the study is to review the main author's ideas and publications on the formation of the concept of "cognitive culture". The methodological basis of the research is presented as an author's concept with a number of components of logically interrelated issues of understanding the subject of cognition, types, genera and types of cognitive culture, its relationship with the problem of truth. Research results. Pointing to his numerous publications, the author of the article argues that the refinement of subjective metrics in modern science and culture is difficult both by the complexity of isolating individual consciousness from collective consciousness and by the existentialization of subjective operations in which human interest is maximally maximized. Subject metrics in a number of publications unfold, according to the author, both by splitting the integral subject and its identification blurring as a result of the decentralization of culture and cognition. The author's work traces the dynamics of the subject in its epistemological and epistemological sections from the "elimination of the subject" to the "death of the subject". Classification into rational and irrational types of cognitive culture, according to the author of the article, is productive in clarifying the connection between truth and knowledge, substantiating the idea of inquiry and extranition of the existence of truth, which is important for determining the truth. The identification of the complex influence of subjective levels, operational mechanisms and their creative products on concrete historical interpretations of truth makes it possible to establish the correspondence of one or another kind of cognitive culture to the known concepts of truth. Prospects of the study. As a result, philosophical and conceptual understanding of the truth is quite correctly permissible by the methodology of the polylogue of cognitive cultures.
Empirical Observations on Requirements Engineering Practices in Palestine

Requirements Engineering (RE) is critical to the success of software development projects. Industrial software projects that apply poor RE practices usually suffer from severe quality challenges and even project failures. Even though RE has been drawing more attention in the literature, there is a lack of empirical evidence of RE practices and challenges at industrial contexts. To address this we carried out a study to evaluate the perspectives of software engineers on their RE practices to understand more about how software engineers approach RE process and what are the challenges they face. We conducted a multi-case study by interviewing 8 participants from 5 software development companies in Palestine. Our results show that for all the RE process seems to be fairly systematic with whole team involvement. Further, the agile RE model is the dominant model, and over half reported that key challenges are caused by issues that originated from the client side. Finally, we highlight interesting future RE research from the perspective of industrial practitioners.
Towards Quality Assessment of OntoUML Conceptual Models Using RDF and SPARQL

OntoUML, an ontology-driven conceptual modelling language based on the Unified Foundational Ontology (UFO), has been demonstrated to have superior clarity in comparison to other commonly used modeling languages, as supported by empirical evidence. Furthermore, the use of RDF technology has led to the creation of the gUFO ontology, enabling the interoperability and reuse of RDF tools, thereby providing new opportunities to explore and evaluate large OntoUML/UFO models and their instances/data. This study utilizes the SPARQL query language to evaluate the quality of OntoUML models and develop queries for identifying and retrieving instances of anti-patterns defined for OntoUML. The effectiveness of these queries is assessed by applying them to a set of conceptual models of varying complexity that have been captured using gUFO.
Knowledge of Language and Natural Language Processing

Using knowledge rather than data is key in knowledge science and enables artificial systems to solve novel problems. We distinguish the knowledge of language internal to the mind from the externalized language. We differentiate the Generative Model of Language from Large Language Models. We take Structure Dependency to be a First Principle of the internal language. We address the question whether Large Language Models provide reliable natural language processing. We identify limits of ChatGPT for answering queries including sentence embeddings, covert constituents, and pronominal anaphora, which rely on Structure Dependency. We draw consequences for reliable natural language processing systems.
Pelatihan Membuat Slide Presentasi Berbasis AI Menggunakan Tome.app Kepada Siswa SMKN 1 Pangkalan Kuras

The rapid development of AI can help users in completing their tasks. As a teacher, administrative tasks are one of the burdens that must be completed to support the learning process. By using AI, this task can be completed more quickly. This is supported by the existence of a platform that helps in doing tasks that are usually done independently. SMKN 1 Pangkalan Kuras currently lacks educators and education personnel who are qualified in the field of information technology, this will certainly cause SMK graduates who can work to become the demands of the world of work. Because SMK is based on producing graduates who have competence in their fields. With this condition, it is necessary to conduct training activities and workshops to increase knowledge in the field of information technology to educators. Lecturers of the Faculty of Computer Science of Lancang Kuning University provided this training to educators of SMKN 1 Pangkalan Kuras to overcome problems in students. The implementation was carried out using lecture, practice and discussion methods in the computer laboratory. This activity will produce scientific journals, news of activities published in the mass media and draft teaching modules that can be useful for participants.
A Systematic Review on KFL Teacher Research: Focusing on Doctoral Dissertations From Year 2004 to 2023

This study analyzes the research trend by meta information and contents of 41 doctoral dissertations related to KFL teachers from year 2004 to 2023. The research trend studied by meta information are reviewed by year, nationality of researchers, and awarding institutions. The research trend analyzed by content are reviewed based on research methods, core concepts, and theories in accordance with each subject. According to the research subject, study related to KFL teachers is categorized into research on KFL teacher education, KFL teacher psychology, KFL teacher behavior and classroom interaction, and KFL teacher policy. Furthermore, for the improvement of internalization and quality improvement of KFL teacher-related research, it is necessary to promote the diversification of research subjects, diversification of research topics, standardization of research methods, specification of research context, and theorization of research results.(Yonsei UniversityㆍYonsei UniversityㆍMejiro University)
Smart Journalism

Journalism is an essential system in this modern era. In this project entitled “Smart Journalism” is trying to build up a SaaS based application that would help a journalist to adapt to their surroundings; record telephone interviews, record live events audio video streams capture pictures etc. This is a perfect tool for a journalist in breaking news situations. The job often in cludes working odd or irregular hours and travelling to conduct research or interviews. Some reporters also put themselves in dangerous situations, such as disaster sites or war zones, in order to get a story. In such situations Journalists can report the admin in any emergency situations. SaaS based concept is been implemented in this project via giving all the Media Admins to safely register over the system “Smart Journalist” where they have to select the plan which helps in adding the number of post they want to post in this application, After Successful registration of Media Admin, MA is used to create a profile for the journalist specifying the current media and thus generating a ID which uniquely identifies each of the Journalist from other Media Admins. An ID card and login detail is generated and emailed to the journalist. From which journalist is to login and create New Post with uploading images, audi os, videos, text etc. according to news category. Which once submitted by the Journalist is analyzed by the Media Admin for Approval, after Approval the Post is shared to the common user page created by the Media Admin with a specific design. Usually, some Journalists sometimes put themselves in dangerous situations to cover the breaking news. In such situations they can also state the message about the situation by which the Media Admin can manage &amp; overcome the situations. The website utilizes various technologies and frameworks such as React, NodeJS, Express, MongoDB
Biological age and brain age in midlife: relationship to multimorbidity and mental health

Biological age and brain age estimated using biological and neuroimaging measures have recently emerged as surrogate aging biomarkers shown to be predictive of diverse health outcomes. As aging underlies the development of many chronic conditions, surrogate aging biomarkers capture health at the whole person level, having the potential to improve our understanding of multimorbidity. Our study investigates whether elevated biological age and brain age are associated with an increased risk of multimorbidity using a large dataset from the Midlife in the United States Refresher study. Ensemble learning is utilized to combine multiple machine learning models to estimate biological age using a comprehensive set of biological markers. Brain age is obtained using Gaussian processes regression and neuroimaging data. Our study is the first to examine the relationship between accelerated brain age and multimorbidity. Furthermore, it is the first attempt to explore how biological age and brain age are related to multimorbidity in mental health. Our findings hold the potential to advance the understanding of disease accumulation and their relationship with aging.
Classification and Detections using Yolov5

Object detection is a growing field of research in the field of computer vision. Our outcome shows that YOLOv5 achieves good modern performance for a variety of sensing applications, including surveillance, robotics, and autonomous driving. Overall, this article demonstrates the potential of YOLOv5 to be an excellent tool for object detection in real-world applications.
Data Science in Critical Care

CRITICAL CARE CLINICS www.criticalcare.theclinics.com Consulting Editor GREGORY S. MARTIN October 2023 • Volume 39 • Number 4
Application of European Model to Curtail Hate Speech in the U.S.

As hate speech increases we may need to revisit the question of how to respond to it and whether to limit it within free speech or other areas of law. This article compares free speech law in the United States and the European Union in an effort to explore how one might improve our care of ourselves and each other in the realm of free speech.
Expert System for Identifying Pregnant Using Forward Chaining

Pregnancy is a biological process in which sperm and eggs meet each other to fertilize, and the fetus is formed in the uterus. But it's difficult; pregnant mothers sometimes have problems or discomfort during pregnancy. In addition, in areas far from the city, there are many obstacles to consulting an obstetrician. Therefore, it will be dangerous if mothers experience problems and find it difficult to get first aid. This research aims to create an expert system for pregnant women where it is not difficult for a mother to go to the doctor to ask about her complaints. The solution offered in this study is easy to access to the SP BUMIL website and automatically enters all mothers' complaints into the system. This system also provides a diagnosis and advice to pregnant women as to the best steps and an explanation of what the pregnant woman is suffering from. This expert system uses the forward chaining method, which has the advantage of producing a solution to a problem; in other words, being able to consider a problem and draw conclusions according to the facts. On this website, there is a disease information menu and also the results of the diagnosis
Data Management for PalMod-II – A FAIR-Based Strategy for Data Handling in Large Climate Modeling Projects

PalMod-II was a multi-institutional research project in Germany focusing on enabling and performing global numerical climate simulations with state-of-theart coupled Earth System Models spanning a full glacial cycle from 130 000 years in the past to the present and beyond. The main project goal was the dataset resulting from these simulations and making it available for reuse by the climate science community in-line with the FAIR data principles. In this paper, we present the research data management (RDM) approach developed and employed in PalMod-II to progress towards that project goal. The RDM approach was implemented by RDM professionals specifically funded by PalMod-II, which made it possible to provide RDM services tailored specifically to the project needs. The compilation and maintenance of a project-wide data management plan (DMP) has proven essential for keeping the project on track and serving as a central focal point of any data-related aspects. These include the specification of data responsible scientists, allocation of storage and computaional resources on a high-performance computing system, documentation of simulation output requirements, definition of data standardisation, and publication workflows in-line with the FAIR data principles. Since the RDM approach executed in PalMod-II was first-of-its-kind for all project partners, exhaustive communication at par with the scientists was required to create trust and a collaborative atmosphere within the project. Finally, the RDM approach implemented in PalMod-II facilitated the publication of a flagship dataset for global reuse, and will also be implemented in the follow-up project: PalMod-III.
An Adaptive Testing Approach for Competence Using Competence-Based Knowledge Space Theory

The increasing demand for personalized learning experience has driven the need for more effective and accurate computerized adaptive testing (CAT) in education. In this study, we present a novel CAT algorithm grounded in the Competence-based Knowledge Space Theory. The algorithm employs maximum likelihood estimation (MLE) for parameter estimation, utilizing a uniform prior distribution in the absence of prior information. It employs a “half split rule” for question selection, ensuring the efficient and accurate estimation of student abilities, and incorporates Laplace smoothing to mitigate overfitting. An information entropy-based termination rule is proposed to strike a balance between efficiency and accuracy in the adaptive testing process. The proposed algorithm contributes to the development of more effective and personalized intelligent tutoring system (ITS) by accurately assessment student competence state and minimizing testing time.