Reviewerly/exHarmony huggingface dataset:
Robot Behavior Tree Manipulation Using Language Models

In recent years high-performance language models have demonstrated superior multitasking capabilities that can be applied to many domains. This breakthrough has spawned emerging research in robot control aimed at bridging the gap between natural language instructions and robot control functions by utilizing the commonsense and logical reasoning capabilities of language models. Behavior trees are modular in nature, better readable, and easily extensible, while supporting nesting and reuse compared to direct code use, making them ideal tools for robot control. We propose an indirect behavior tree manipulator based on a language model that implements querying, generating, and modifying functions for behavior trees, allowing non-specialists to use natural language to adapt the behavior tree and thus modify the robot's operation logic. Furthermore, we validate the capability of this approach in solving multiple tree manipulation tasks and apply the generated behavior trees to real robot control.
Mental Fatigue Evaluation for Passive and Active BCI Methods for Wheelchair-Robot During Human-in-the-Loop Control

Human-Robot Interaction (HRI) and Brain-Computer Interfaces (BCIs) aim to develop practical and realistic solutions to assist both healthy and disabled individuals in their Activities of Daily Living (ADL). This paper focuses on the application of BCIs and Brain Machine Interfaces (BMIs) to correct or perform actions by machines through brain stimulation. The authors investigate mental load and fatigue during extended tasks using mental load indices in BCI human-in-the-loop experiments, specifically involving human observation and error perception to rectify errors in obstacle detection by a wheelchair-mobile robot. The study compares the passive method utilizing Error-Related Potentials (ErrPs) with potential active methods such as P300 or Steady-State Visually Evoked Potential (SSVEP) in terms of mental load and signal classification accuracy. The Mental Fatigue Index (MFI) and other power-ratio indices are used to estimate mental load. Preliminary results indicate that passive BCIs induce lower mental fatigue compared to active methods for the same task, making them suitable for applications involving elderly or disabled individuals. However, the passive method utilizing ErrPs exhibits lower task engagement, causing participants to lose attention and feel drowsy, leading to reduced classification accuracy compared to other BCI methods such as SSVEP.
An Assistive Robot in an Indoor Scenario: the Stretch Hello Robot as Environment Organizer

Assistive robots are common in the research community and are also becoming widespread as commercial solutions, especially for indoor applications, such as domestic settings, offices, or care facilities. The use of a robot in such scenarios can provide value in the form of an increase in efficiency, a reduction in physical strain on humans, and an improvement in accessibility for individuals with disabilities. Motivated by this value addition, this paper proposes the use of the Hello Robot Stretch RE-2 as an assistive robot for organizing an indoor environment. The robot has an arm with three degrees of freedom and a gripper able to grasp and release items. It uses an adversarial comparison of default and current environment scan to perform organization tasks. The study focuses on the utilization of the Stretch RE-2 developed by Hello-Robot Inc. to improve indoor environments, simulated with tables and different objects, providing a comprehensive analysis of the encountered challenges, and offering recommendations for future works.
Ethical Challenges of Using Artificial Intelligence in Judiciary

Artificial intelligence (AI) has emerged as a ubiqui-tous concept in numerous domains, including the legal system. AI has the potential to revolutionize the functioning of the judiciary and the dispensation of justice. Incorporating AI into the legal system offers the prospect of enhancing decision-making for judges, lawyers, and legal professionals, while concurrently providing the public with more streamlined, efficient, and cost-effective services. The integration of AI into the legal landscape offers manifold benefits, encompassing tasks such as document review, legal research, contract analysis, case prediction, and decision-making. By automating laborious and error-prone procedures, AI has the capacity to alleviate the burden associated with these arduous tasks. Consequently, courts around the world have begun embracing AI technology as a means to enhance the administration of justice. However, alongside its potential advantages, the use of AI in the judiciary poses a range of ethical challenges. These ethical quandaries must be duly addressed to ensure the responsible and equitable deployment of AI systems. This article delineates the principal ethical challenges entailed in employing AI within the judiciary and provides recommendations to effectively address these issues.
A Data Mining Method for Analyzing Alarm Logs Generated by Network Boundary Defense Equipment In Power Grid

Network boundary defense equipment is crucial for the power grid cyber-security. Alert logs generated by network boundary equipment serve as important data source for detecting potential attacks. However, the lack of direct correlation between alerts and network attacks make it difficult to pinpoint equipment at risk of attack from the extensive pool of alert logs. This paper proposes a data mining method for analyzing alarm logs generated by network boundary defense equipment. The method conducts data mining in four perspectives: ontology mining, carrier mining, transverse mining, and vertical mining. Compared to traditional alert analysis techniques, this method integrates cyber-security with power grid characteristics such as topology, enabling the identification of boundary equipment with substantial state changes and facilitating precise localization of devices at risk of attack.
Research on Learning Motivation Based on Matrix Operation

Based on literature analysis and expert consultation, 19 main factors influencing the learning motivation of secondary vocational students have been selected. A model has been established based on the AISM, obtaining the original adjacency matrix, a series of matrix operations and topological operations, and in turn obtaining multiplication matrix, reachable matrix, skeleton matrix and general skeleton matrix, etc. Finally, a set of directed topological hierarchy graphs with game antagonistic properties are obtained to analyze the relationship between the influencing factors.
Time Series Forecasting Based on ARIMA and LSTM Models

Prediction is to speculate and judge the future development trend through scientific methods according to the past and present information of the predicted object. Nowadays, prediction models are applied in various fields which made them have great research value in the academic field. In this paper, two different sets of time series data are used for forecasting research. Through preliminary analysis, we find that these two groups of data have the characteristics of large sample size and nonlinearity. After studying the relevant literature, we find that the LSTM model seems to have more advantages than the ARIMA model when studying data with large sample size and nonlinear characteristics. To verify this finding, we forecast our two sets of data separately with these two models, and then compare the prediction accuracy of the two models by correlation coefficient test, and find that the LSTM model has better results in predicting these two sets of data. This provides some theoretical support for our conjecture.
Quality of life and its relationship to the level of leadership skills of educational leaders

Leadership skills are very important for reaching educational objectives and improving the education system in Saudi Arabia. Making the lives of educational leaders better is key to enhancing their work performance, boosting their abilities, and fulfilling the education system's aims, aligning with Saudi Arabia's vision for 2030. This study looked into how the quality of life relates to leadership among educational leaders in Saudi Arabia. It explored how much quality of life contributes to leadership skills and checked if there were any differences between men and women in terms of quality of life and leadership abilities. The research used a mix of number-based and detailed methods to accurately describe the quality of life and leadership skills among educational leaders. Information was gathered through surveys on general data, quality of life, and leadership skills from 322 educational leaders, both men and women, with different educational levels and years of work experience. The main discovery was a positive link between quality of life and leadership skills that was statistically significant. Also, there were notable differences in certain aspects of quality of life and leadership based on gender. Furthermore, a strong positive relationship was found between the educational level, years of work, quality of life, and leadership skills among educational leaders.
Robust Network Alignment with the Combination of Structure and Attribute Embeddings

The task of network alignment is to obtain the node pairs which belong to the same entity from different networks. Existing embedding-based network alignment methods either use node structural or attribute information as inputs for node embeddings. These pieces of information are not always available in real-world datasets, and current methods that consider single information embedding may fail when there is excessive network noise. To address the aforementioned issue, we utilize a multi-layer Graph Attention Networks(GATs) to design an unsupervised node embedding model, which trains two GATs for structural and attribute information in a single graph and embeds the source nodes and target nodes into the same embedding space. By applying graph augmentation techniques, the model learns structural embeddings and attribute embeddings for every node in the networks based on structural and attribute consistency. Moreover, we apply a topological alignment refinement process to get aligned node pairs, which further enhances the accuracy of network alignment by leveraging the similarity of the structure between networks. Through extensive experiments, we have demonstrated that our model outperforms the state-of-the-art models in terms of alignment accuracy and its ability to handle attribute and structural noise. Additionally, our model exhibits relatively low complexity.
INTERNET OF THINGS (IOT): A RISING TECHNOLOGY IN THE HEALTHCARE SECTOR

The concept of "Internet of Things" (IoT) has permeated various industries, including healthcare, signifying the integration of technology into business operations. It has fundamentally reshaped the interactions and operational processes within frontline healthcare. IoT refers to a network of physical devices utilizing connectivity to enable data exchange. Currently, healthcare and technology companies are making substantial investments in IoT projects. Various technological devices, including wearables like biosensors and X-ray machines with Wi-Fi or Bluetooth capabilities, now feature integrated IoT technology. These medical devices with IoT capabilities play a crucial role in analyzing vital data, aiding healthcare practitioners in their duties [1].
DEVELOPMENT OF A MATHEMATICAL MODEL OF THE THERMAL FIELD OF AN INTEGRAL STRUCTURE IN THE IMPLEMENTATION OF SENSORS FOR BIOMEDICAL RESEARCH

The paper deals with the implementation of a complex technique for electrothermal modeling of measuring transducers of thermal flow sensors, which combines the synthesis of the impulse temperature relaxation substitution circuit and the method of forming the I-V converters in the mode of their self-heating by the supply current. The issue of assessing the instability of iteration processes in the analysis of I/V measuring converters with negative differential resistance, which is caused by self-heating of these converters, is considered. An express method for determining the limits in which correct electrothermal DC analysis is provided for use in biomedical devices and systems has been developed.
MAGNET: Multi-Interest Attentive Group Recommender with Deep Reinforcement Learning

In real-life group activities, providing relevant content recommendations is challenging due to the dynamic nature of group interests. We propose MAGNET, a deep reinforcement learning-based group recommendation system. MAGNET employs multiple vectors to represent group interests collectively, addressing the challenge of inconsistent long-term and short-term preferences. We treat the recommendation task as a sequential decision problem, extracting points of interest from short-term interactions and using them to predict the next item. A neural network assigns weights to group members based on their current interests for a more accurate representation of long-term preferences. Our experiments on two datasets demonstrate the competitive performance of MAGNET, confirming its effectiveness. Ablation experiments highlight the contributions of individual components. MAGNET has the potential to enhance content recommendations for groups in real-life scenarios.
The Open System for Storing and Processing of a Dataset of Combinational Circuits

This paper presents an open-source software for generation, storage, and analysis of combinational circuits. The previously created methods for generating combinational circuits have been optimized, and a dataset has been formed. The generation of combinational circuits is carried out on various devices. The application implements the possibility to combine the generated datasets into a single storage (Synology Drive), as well as analyze the fault tolerance of combinational circuits using various methods for their evaluation. New possible methods for assessing combinational circuits’ reliability using machine learning are proposed.
Design and Implementation of an Assamese Language Chatbot Using Neural Networks

The International Scientific Research Organization for Science, Engineering and Technology (ISROSET) is a Non-Profit Organization; The ISROSET is dedicated to improvement in academic sectors of Science (Chemistry, Bio-chemistry, Zoology, Botany, Biotechnology, Pharmaceutical Science, Bioscience, Bioinformatics, Biometrics, Biostatistics, Microbiology, Environmental Management, Medical Science, Forensic Science, Home Science, Library Science, Material Science Military Science, Physical Science, Physical Education Science, Educational Science, Fisheries, seed technology, Agriculture, Forestry Science, Mathematics, Physics, Statistics and Geology/Earth Science), Computer Science, Engineering and Information Technology, Commerce, Management, Economics Sociology and Social Science.
Assessment of Heavy Metal Pollution from Granite Quarrying in Akure, Nigeria

This study investigated the heavy metals pollution of granite quarrying activities in Akure, Ondo State, Nigeria. Three quarries and a control site were selected for this study. Plants and soil samples were collected at 0, 50, 100, 150 and 200 m away from the crushing points, while samples were collected away from roadside for control. Water samples were collected at just a point. Concentration of heavy metals in collected samples was determined using Buck Scientific Atomic Absorption Spectrophotometer model 210 VGP. The mean total heavy metal contents in C. odorata and P. purpurem collected within 0 to 200 m showed that Pb, Fe, Zn, Cr Co and Cd contents were above the WHO permissible limit while only Pb content (190.8, 299.20 and 208.80 ppm) and Fe (7559.50, 13386.50 and 19019.50 ppm for Dortmond, Ebenezer and Samchase quarries respectively) were above the permissible limit in soil samples. Elevated Pb, Fe, Cr, and Ni were recorded in water samples collected from quarry sites while values recorded for control sites falls within the permissible limit except for the lead concentration of 0.10 ppm as against the maximum concentration of 0.05 ppm set by WHO. Zn, Mn, Cd and and Cu were not detected in all water samples. Higher pH values were also recorded in soil and water samples from quarry sites. This study has shown that quarrying introduces toxic heavy metals into nearby environment.
The Categorization of Documents Using Support Vector Machines

The International Scientific Research Organization for Science, Engineering and Technology (ISROSET) is a Non-Profit Organization; The ISROSET is dedicated to improvement in academic sectors of Science (Chemistry, Bio-chemistry, Zoology, Botany, Biotechnology, Pharmaceutical Science, Bioscience, Bioinformatics, Biometrics, Biostatistics, Microbiology, Environmental Management, Medical Science, Forensic Science, Home Science, Library Science, Material Science Military Science, Physical Science, Physical Education Science, Educational Science, Fisheries, seed technology, Agriculture, Forestry Science, Mathematics, Physics, Statistics and Geology/Earth Science), Computer Science, Engineering and Information Technology, Commerce, Management, Economics Sociology and Social Science.
Digitalization of Bale Kampoeng Radjoet SMEs Stock Opname Recording Systems based on Website with Waterfall Method

Bale Kampoeng Radjoet is an SME in Bandung that markets knitting products from 12 artisans. In the business process, recording product data is still done manually using a notebook. This has an impact on the stock opname process, namely the occurrence of product data discrepancies in the warehouse with records and difficulties in knowing product stock. These problems are caused by the absence of a system that helps record all product data. This is the basis for improving the stock opname recording system by digitalization, namely the use of a website-based information system. The development of the stock opname recording website uses the waterfall method, with system modeling using the Unified Modeling Language (UML). The system verification process uses blackbox testing and validation using User Acceptance Testing (UAT) which refers to ISO 25010. UAT results show the characteristics of functional suitability 89.33%, performance 92%, usability 91.2%, and reliability 93.33%. The results adjusted to the interpretation criteria show that the four characteristics are in the range of 81%-100%, which means that the website can be accepted by users because it fulfills user needs and can be run according to its function.
Design of a Web Based Information System to Manage Delivery Activities

The information and data processing in logistics activities are necessary for improving company performance. One logistics activity that needs to be managed is the shipment of goods. Some companies manually record and store shipment data, facing issues related to accuracy and pace. This research aims to design a web-based goods delivery information system for recording, storing, processing, and reporting shipments swiftly and faultlessly. The system is designed using stages in the Waterfall model, including analysis of needs, design, coding, and testing. The result is a web-based goods delivery application that can manage shipment activity data automatically and in real-time, reducing errors in recording and processing time. The system can also generate shipping documents, such as waybills and invoices, automatically and can be printed directly. Furthermore, it provides automatic real-time shipment reports, allowing monitoring of delivery performance through a dashboard on the main page of the application. This information system can assist companies in managing shipment activities.
THE PLACE AND IMPORTANCE OF THE DEVELOPMENT OF READING ACTIVITY IN THE LESSONS OF “LITERARY READING” IN ELEMENTARY SCHOOL

The article describes the formation and development of the reading activity of primary school students in the lessons of literary reading. In the article, we will consider the importance and place of the formation and development of students' reading activity in the lessons of “Literary reading” in elementary school. It also gives an idea of the concepts of a qualified reader and reading culture. The lesson “Literary reading", reading activity, qualified reader, reading culture. Literary studies and literature classes have an important task – to prepare a qualified and literate reader who is able to understand and aesthetically evaluate the literary work of different eras and peoples, who is able to analyze a large number of domestic and foreign literature. To successfully solve the problem, it is necessary to get acquainted with the concept of "reading culture", consider its main components and further work on the formation of a literate reader through conscious and purposeful formation of reading activity from class to class. In the methodological literature, the concept of reading culture is a combination of interest in a book, extensive acquaintance with various literature, as well as special knowledge about the book and methods of working with it, skills and knowledge will help you read the book to the end. maximum.
On the Significance of One Periodical for the History of Philosophical Generations in Russia

The article is a reflection on the topic of “philosophical generations” in Russia. The author begins by saying that if it is difficult to talk about generations in general, it is difficult to talk about “philosophical generations” at all. Therefore, he focuses on describing his experience with academic philosophy in Russia. The author devotes a special place to the Faculty of Philosophy of Moscow State University, the Institute of Philosophy of the Russian Academy of Sciences and most importantly to the journal Logos. Using the latter as an example, the author shows how in Russia over the past fifteen years humanities science in general (and philosophy in particular) has been conquering new spaces and coming closer to equal the level of world research.
Innovations For Energy Efficient Generative AI

While the explosion in the capabilities of generative artificial intelligence (AI) and the associated benefits has received a tremendous amount of attention, both in the technology world and popular press, so has the unprecedented amount of compute and energy required to train and serve these extremely complex models. Many of these concerns are well founded given the hundreds of megawatthours required to train large language models (LLMs). However, our contention is that history and recent developments indicate that our industry’s ability to innovate is up to the task of delivering the benefits of LLMs while reining in energy use. This paper will review some of that history and the approaches that are likely to provide the required efficiency gains.
Analysis of techniques for automatic summarization of hotel opinions

This paper presents a comparison of different techniques aimed at automatic summarization of textual content found in hotel reviews. Extractive techniques that generate an aspect-based summary as well as techniques that generate a general summary are analyzed. The reviews themselves were extracted from a novel corpus comprising data collected from the TripAdvisor platform, focusing on hotels from different regions of Brazil. All automatic summaries were evaluated using the ROUGE set of metrics against summaries created by human annotators. The results revealed some key limitations within ROUGE when used on shorter, informal documents, as well as variations in the effectiveness of different techniques in addressing specific aspects of summarization.