Questions-and Answers Research Articles

Question-Attentive Review-Level Explanation for Neural Rating Regression

Recommendation explanations help to improve their acceptance by end users. Explanations come in many different forms. One that is of interest here is presenting an existing review of the recommended item as the explanation. The challenge is in selecting a suitable review, which is customarily addressed by assessing the relative importance or “attention” of each review to the recommendation objective. Our focus is improving review-level explanation by leveraging additional information in the form of questions and answers (QA). The proposed framework employs QA in an attention mechanism that aligns reviews to various QAs of an item and assesses their contribution jointly to the recommendation objective. The benefits are two-fold. For one, QA aids in selecting more useful reviews. For another, QA itself could accompany a well-aligned review in an expanded form of explanation. Experiments on datasets of ten product categories showcase the efficacies of our method as compared to comparable baselines in identifying useful reviews and QAs, while maintaining parity in recommendation performance.

Breast Cancer Prevention Education Through Community Empowerment and TPS (Transactional, Precise, and Skilled) Communication Model in Bontang City

The prevalence of breast cancer in Indonesia is still increasing. Datafrom the Global Cancer Observatory shows that the most common cancer case inIndonesia is breast cancer. Meanwhile, the incidence of breast and cervical cancerin Bontang City reaches 30% of the overall cancer data in Bontang (Akurasi.id,2024). Therefore, there is a need for a health promotion campaign and breast cancerprevention using simple communication methods. This program aimed to provideeducation to health cadres (PKK) in three sub-districts of Bontang, targeting 100participants. Five sub-districts were selected from each of the three sub-districts, withseven participants sampled from 10 sub-districts and six participants sampled fromfive sub-districts. Participants were selected using purposive sampling. The programemployed a participatory research approach based on community empowerment,using the TPS (Transactional, Precise, and Skilled) communication model. The programwas conducted in three stages. In the first stage, theoretical information relatedto breast cancer and its prevention was provided, along with 100 questions andanswers shared online beforehand. The second stage involved practicing breast selfexamination(SADARI). The third stage combined theory and practice, incorporatingeducational strategies through games and evaluations. The program results showedthat 83% of participants agreed that knowledge about breast cancer and its preventionwas necessary. The program successfully improved the knowledge and attitudes ofPKK/health cadres through an interactive, participatory approach. Although therewere barriers related to participant agility, support from trained organizers provedeffective in overcoming them. These findings confirm the importance of healtheducation that emphasizes two-way communication and breast cancer prevention.

Automatic question-answer pairs generation using pre-trained large language models in higher education

The process of manually generating question and answer (QA) pairs for assessments is known to be a time-consuming and energy-intensive task for teachers, specifically in higher education. Several studies have proposed various methods utilising pre-trained large language models for the generation of QA pairs. However, it is worth noting that these methods have primarily been evaluated on datasets that are not specifically educational in nature. Furthermore, the evaluation metrics and strategies employed in these studies differ significantly from those typically used in educational contexts. The present discourse fails to present a compelling case regarding the efficacy and practicality of stated methods within the context of higher education. This study aimed to examine multiple QA pairs generation approaches in relation to their performance and the efficacy and constraints within the context of higher education. The various approaches encompassed in this study comprise pipeline, joint, multi-task approach. The performance of these approaches under consideration was assessed on three datasets related to distinct courses. The evaluation integrates three automated methods, teacher assessments, and real-world educational evaluations to provide a comprehensive analysis. The comparison of various approaches was conducted by directly assessing their performance using the average scores of different automatic metrics on three datasets. The results of the teachers and real educational evaluation indicate that the assessments generated were beneficial in enhancing the understanding of concepts and overall performance of students. The implications of the findings from this study hold significant importance in enhancing the efficacy of QA pair generation tools within the context of higher education.

A Survey on Conversational AI Question answering System for Low Resource Language

Question-and-answer (QA) systems allow users to pose questions about the information accessible in a variety of formats, such as structured and unstructured information in natural languages. It contributes significantly to conversational artificial intelligence (AI), which has led to the creation of a novel research field on conversational question answering (CQA), in which a system is required to understand the provided context before engaging in multi-turn QA to satisfy a user's informational needs. While the majority of currently done research focuses on single-turn QA, pre-trained language models and the availability of large multi-turn QA datasets have recently made multi-turn QA more significant. This study aims to give a thorough analysis of the most recent CQA research trends, mostly based on publications that have been reviewed in recent years. Our data indicates a shift in the industry's preference for multi-turn QA over single-turn QA, which has numerous advantages for conversational AI. In order to create a solid basis for the field of CQA, this survey aims to serve as the pinnacle for the research community.

Edukasi Pembuatan Pembukuan Keuangan Sederhana Menggunakan Aplikasi Digital Pada Usaha Desa Ambulu

Ambulu is one of the villages located in Sumberasih Subdistrict, Probolinggo Regency. Ambulu Village has 5hamlets consisting of Damaran Hamlet, Talang Hamlet, Krajan Hamlet, Central Hamlet and Kramat Hamlet.Based on the village's main data in 2022, this village has approximately 100 Micro, Small and MediumEnterprises engaged in fisheries, food, fashion, and also design. Micro, Small and Medium Enterprises (MSMEs)are one of the mainstays of the economy in Indonesia. However, there are still many MSMEs that do not usefinancial records to support their businesses. One of the reasons why MSMEs do not do bookkeeping is becausethey are considered difficult and not important. As a result, the development of the business, especially regardingfinancial results, cannot be clearly known. Therefore, the background of this socialization activity was held withthe aim that MSME actors in Ambulu Village, Sumberasih District were able to increase their knowledge and howto apply the practice of recording simple financial bookkeeping so that MSME actors knew the financial conditionof their business. The implementation method used is door to door by starting with a simple explanation offinancial bookkeeping records, followed by application and then ending with a discussion and question andanswer session. Based on the results of this activity, 75% of participants already knew the importance of financialmanagement, recording, and financial reporting. From the results of the socialization of the importance of makingsimple financial bookkeeping can increase insight as well as knowledge of making business financial bookkeeping.

Conversational Chatbot for Cigarette Smoking Cessation: Results From the 11-Step User-Centered Design Development Process and Randomized Controlled Trial.

Conversational chatbots are an emerging digital intervention for smoking cessation. No studies have reported on the entire development process of a cessation chatbot. We aim to report results of the user-centered design development process and randomized controlled trial for a novel and comprehensive quit smoking conversational chatbot called QuitBot. The 4 years of formative research for developing QuitBot followed an 11-step process: (1) specifying a conceptual model; (2) conducting content analysis of existing interventions (63 hours of intervention transcripts); (3) assessing user needs; (4) developing the chat's persona ("personality"); (5) prototyping content and persona; (6) developing full functionality; (7) programming the QuitBot; (8) conducting a diary study; (9) conducting a pilot randomized controlled trial (RCT); (10) reviewing results of the RCT; and (11) adding a free-form question and answer (QnA) function, based on user feedback from pilot RCT results. The process of adding a QnA function itself involved a three-step process: (1) generating QnA pairs, (2) fine-tuning large language models (LLMs) on QnA pairs, and (3) evaluating the LLM outputs. We developed a quit smoking program spanning 42 days of 2- to 3-minute conversations covering topics ranging from motivations to quit, setting a quit date, choosing Food and Drug Administration-approved cessation medications, coping with triggers, and recovering from lapses and relapses. In a pilot RCT with 96% three-month outcome data retention, QuitBot demonstrated high user engagement and promising cessation rates compared to the National Cancer Institute's SmokefreeTXT text messaging program, particularly among those who viewed all 42 days of program content: 30-day, complete-case, point prevalence abstinence rates at 3-month follow-up were 63% (39/62) for QuitBot versus 38.5% (45/117) for SmokefreeTXT (odds ratio 2.58, 95% CI 1.34-4.99; P=.005). However, Facebook Messenger intermittently blocked participants' access to QuitBot, so we transitioned from Facebook Messenger to a stand-alone smartphone app as the communication channel. Participants' frustration with QuitBot's inability to answer their open-ended questions led to us develop a core conversational feature, enabling users to ask open-ended questions about quitting cigarette smoking and for the QuitBot to respond with accurate and professional answers. To support this functionality, we developed a library of 11,000 QnA pairs on topics associated with quitting cigarette smoking. Model testing results showed that Microsoft's Azure-based QnA maker effectively handled questions that matched our library of 11,000 QnA pairs. A fine-tuned, contextualized GPT-3.5 (OpenAI) responds to questions that are not within our library of QnA pairs. The development process yielded the first LLM-based quit smoking program delivered as a conversational chatbot. Iterative testing led to significant enhancements, including improvements to the delivery channel. A pivotal addition was the inclusion of a core LLM-supported conversational feature allowing users to ask open-ended questions. ClinicalTrials.gov NCT03585231; https://clinicaltrials.gov/study/NCT03585231.

THE LOGIC OF TURKISH LANGUAGE FOR NLP

One of the applications of NLP is Question and Answering (QA) system. In this field there are many researches and applications. Some competition is organized to increase the performance of the systems. Turkish is a language designed as if by a scientific committee. In Turkish language, rules are clear and transparent. In order to prove these features of Turkish, rules regarding phonology, morphology and syn-tax features have been deduced, a logical expression has been written for each rule and a flow diagram has been drawn. Indexing of pages written in Turkish is somewhat difficult than any India-European pages. Although an English word can get one prefix and one suffix Turkish words can take many suffixes. Therefore, we need to discover the root or stem of the word. The rules of Turkish were tested with the programs developed in accordance with the flow diagram. As a result of the tests, it has been proven that the rules of Turkish are intact. Finally, a study was con-ducted to artificially create a sentence and a program was prepared.

Effect of Artificial Intelligence-based Health Education Accurately Linking System (AI-HEALS) for Type 2 diabetes self-management: protocol for a mixed-methods study

BackgroundPatients with type 2 diabetes (T2DM) have an increasing need for personalized and Precise management as medical technology advances. Artificial intelligence (AI) technologies on mobile devices are being developed gradually in a variety of healthcare fields. As an AI field, knowledge graph (KG) is being developed to extract and store structured knowledge from massive data sets. It has great prospects for T2DM medical information retrieval, clinical decision-making, and individual intelligent question and answering (QA), but has yet to be thoroughly researched in T2DM intervention. Therefore, we designed an artificial intelligence-based health education accurately linking system (AI-HEALS) to evaluate if the AI-HEALS-based intervention could help patients with T2DM improve their self-management abilities and blood glucose control in primary healthcare.MethodsThis is a nested mixed-method study that includes a community-based cluster-randomized control trial and personal in-depth interviews. Individuals with T2DM between the ages of 18 and 75 will be recruited from 40-45 community health centers in Beijing, China. Participants will either receive standard diabetes primary care (SDPC) (control, 3 months) or SDPC plus AI-HEALS online health education program (intervention, 3 months). The AI-HEALS runs in the WeChat service platform, which includes a KBQA, a system of physiological indicators and lifestyle recording and monitoring, medication and blood glucose monitoring reminders, and automated, personalized message sending. Data on sociodemography, medical examination, blood glucose, and self-management behavior will be collected at baseline, as well as 1,3,6,12, and 18 months later. The primary outcome is to reduce HbA1c levels. Secondary outcomes include changes in self-management behavior, social cognition, psychology, T2DM skills, and health literacy. Furthermore, the cost-effectiveness of the AI-HEALS-based intervention will be evaluated.DiscussionKBQA system is an innovative and cost-effective technology for health education and promotion for T2DM patients, but it is not yet widely used in the T2DM interventions. This trial will provide evidence on the efficacy of AI and mHealth-based personalized interventions in primary care for improving T2DM outcomes and self-management behaviors.Trial registrationBiomedical Ethics Committee of Peking University: IRB00001052-22,058, 2022/06/06; Clinical Trials: ChiCTR2300068952, 02/03/2023.

AN ANALYSIS OF ASSESSMENT USED IN THE ENGLISH TEXTBOOK OF MERDEKA BELAJAR CURRICULUM FOR THE FIRST-YEAR STUDENTS OF JUNIOR HIGH SCHOOL

Assessment is one of the important processes in education that is useful for assessing the effectiveness of learning and curriculum achievement. The assessment process is very important to evaluate as well as improve the learning process. The aimed this study is to explore the dominant forms of assessments used in English in Mind the textbook of Merdeka Belajar curriculum for first-year students of Junior High School and to know the types of assessment used in the English textbook of Merdeka Belajar curriculum for first-year students of Junior High School. The writer used the mixed method. Based on the data analysis the writer found 22 types of assessments of English in Mind in which it devided into 16 assessments after analyzed by each aspect skills, they are directed responses, read-aloud, sentence/dialogues complexion andoral questionnaire, pictures-cued task, translation, questionand answer, giving instruction and direction, information transfer, dictation, listening cloze, sentence repetition, reading aloud, written response, multiple choice, matching task, short answer, grammatical transformation, and vocabulary assessment. Then, the dominant assessment used in the English textbook of Merdeka Belajar curriculum for the first-year students of Junior High School was a grammatical transformation from writing skills.

Argumentative of Dialogue of Abu Hayyan Al-Tawhidi (The Sixth Night of the Book of Enjoyment and Sociability as an Applied Model)

Argumentative studies are among the most important studies that investigate the relationship of discourse with the recipient in terms of influence and persuasion. There was a need to research the argumentative discourse mechanisms in order to know the intent of the discourse as a message that includes persuasive and influential dimensions. The argumentative view of the discourse goes beyond the aesthetic goal to the affective element through the communicative goal of the discourse, and also devotes rhetorical and artistic aesthetics to the formulation of influence and persuasion and highlighting it in the discourse. Argumentation is one of the most important means for the speaker to convey his perceptions to the recipient, relying on a number of mechanisms that contribute to achieving his goals. The purpose of this study is to investigate the mechanisms of argumentation of AlTawhidi in his book enjoyment and sociability, through an applied study for one sample,which is the sixth night; that dealt comparison between Arabs and Persians, relying on contemporary linguistic perceptions of the concept of pilgrims and its various mechanisms, the research is based on an applied textual study that attempts to reveal the reservoirs of ingenuity in arguing with the Persian other in a language based on argument and proof in presenting the monotheistic point of view defending the Arabs. The study discussed the mechanisms of Al-Tawhidi in justifying his argumentation in the studied sample; And the methods that he followed in forming an argumentative image are based on evidence and proof. The study adopted the analytical descriptive approach as a systematic path of applied research on the sixth night of enjoyment and sociability by examining the argumentative mechanisms adopted by Al-Tawhidi in defending the Arabs. These mechanisms have emerged in several methods of question and answer, and discussion of the issues, adopting the method of luring in order to reach the results. In addition to the method of measurement and presentation of evidence, the importance of argumentative mechanisms appeared in diversifying the method of presenting issues for discussion with multiple mechanisms that contributed to providing a communicative environment for discourse so that it was not confined to one side, but was interactive, which gave the results reached by al-Tawhidi a greater influential and persuasive value. An effective argumentative value also emerged for the linguistic mechanisms on which the discourse was based in the studied sample, in addition to investing the Japanese and artistic mechanisms in formulating the influential discourse in the formation of the argumentative discourse. The applied study of the pilgrims in the enjoyment and sociability of the sixth night is a model that gives an integrated picture of the contribution made by the pilgrims studies in studying the mechanisms of influence and persuasion in the traditional discourse in Arabic literature. The study concluded several findings, such as; the appearance of the argumentative structure in the text studied in two forms: the first: in which Al-Tawhidi appeared as a narrator and transmitter, as he questioned the heritage and conveyed what he heard in his style and method. The second: Al-Tawhidi appeared in it as an interlocutor and an argument that pushes the opponent's statements through various argumentative tools and methods. Keywords: Argumentation, Dialogue, Al-Tawhidi, Sixth night, Enjoyment and sociability.

A novel use of an artificially intelligent Chatbot and a live, synchronous virtual question-and answer session for fellowship recruitment

IntroductionAcademic departments universally communicate information about their programs using static websites. In addition to websites, some programs have even ventured out into social media (SM). These bidirectional forms of SM interaction show great promise; even hosting a live Question and Answer (Q&A) session has the potential for program branding. Artificial Intelligence (AI) usage in the form of a chatbot has expanded on websites and in SM. The potential use of chatbots, for the purposes of trainee recruitment, is novel and underutilized. With this pilot study, we aimed to answer the question; can the use of an Artificially Intelligent Chatbot and a Virtual Question-and-Answer Session aid in recruitment in a Post-COVID-19 era?MethodsWe held three structured Question-and-Answer Sessions over a period of 2 weeks. This preliminary study was performed after completion of the three Q&A sessions, in March–May, 2021. All 258 applicants to the pain fellowship program were invited via email to participate in the survey after attending one of the Q&A sessions. A 16-item survey assessing participants’ perception of the chatbot was administered.ResultsForty-eight pain fellowship applicants completed the survey, for an average response rate of 18.6%. In all, 35 (73%) of survey respondents had used the website chatbot, and 84% indicated that it had found them the information they were seeking.ConclusionWe employed an artificially intelligent chatbot on the department website to engage in a bidirectional exchange with users to adapt to changes brought on by the pandemic. SM engagement via chatbot and Q&A sessions can leave a favorable impression and improve the perception of a program.

Intrance: Designing an Interactive Enhancement System for the Development of QA Chatbots

User input is essential for the successful development of question-and-answer (QA) chatbots. Therefore, interactive development systems are emerging that allow developers to involve test-users in the QA chatbot development process. Although the feasibility and effectiveness of this approach have been demonstrated, there is a lack of knowledge on how to design interactive QA chatbot development systems to increase test-user engagement as well as data quality in this time-consuming and tedious task. To address this research gap, we propose an interactive system design based on the interactivity effects model. We instantiate the proposed design and introduce Intrance, an interactive enhancement system for QA chatbots. Subsequently, we show in two online experiments that the proposed design significantly increases subjective and objective engagement of test-users and has a positive effect on data quality, conceptualized as data completeness and data accuracy. We discuss design implications for an implementation of the proposed design in commercial chatbot development systems.

Pendampingan Remaja SMKN 5 Surabaya Terhadap Bahaya Penyalahgunaan NAPZA dan HIV/Aids

Adolescence is a period of transition and experiencing rapid development ranging from physical, psychological and intellectual organs so that it requires good and supportive assistance to properly recognize these changes. Management of psicology which is emotions in adolescents who are not directed will cause adolescents to enter into risky behavior including drug abuse and risky sexual behavior. Increasing awareness of adolescents about the dangers of drug abuse, health problems caused by drug abuse and risky sexual behavior that causes sexually transmitted infections (STIs) including HIV and Aids through peer educators was urgently needed by adolescents. The target of the service was the youth of Civil Vocational High School (SMKN) 5 Surabaya and in its implementation it worked with the youth community who are members of the SCARFS (School and Communities Caring for HIV & AIDS) community 30 people. The methods used in this service activity were lectures / counseling, discussions, questions and answers (QnA) and evaluations using pre and post test questionnaires. The results of the service activities carried out received good enthusiasm from the participants. The enthusiasm of the participants was during the case discussion session, participants asked an average of 4 questions to the facilitator and there was an increase in knowledge about drugs and HIV/Aids.

Sosialisasi dan Pengajaran Pendidikan Karakter pada Anak-Anak TPQ dengan Media Video Animasi

This community service is one of the activities in the community partnership program. The targets are people who are not economically productive, namely the students of the Qur'an Education Park/ TPQ Asy Syifa' Darussalam in Ringinarum District, Kendal Regency. The activity carried out in this community partnership program is to provide teaching about character education to children with video media. Animated videos were chosen as learning materials because children are usually more interested and easier to learn and understand something based on audio visuals. The study method on the results of this community service uses a qualitative descriptive study. The results of the activity showed that the children were quite enthusiastic in learning the material from the videos provided. The children looked more interactive in the discussion and question and answer (QA) sessions after the video screening and presentation of the material. They are also motivated to learn and practice moral values in their daily lives, so that the children not only will be able to learn religious education, but they will also have a good attitude and behavior.

Verification of a Dataset for Korean Machine Reading Comprehension with Numerical Discrete Reasoning over Paragraphs

Numerical reasoning in machine reading comprehension (MRC) has demonstrated significant performance improvements in the past few years. However, due to the process being restricted to specific languages, low-resource languages are not considered, and MRC studies on such languages are limited. In addition, the methods that rely on existing information extracted within the span of a paragraph have limitations in responding to questions requiring actual reasoning. To overcome these shortcomings, this study establishes a dataset for learning Korean Question and Answering (QA) models that not only answer within the span of passages but also perform numerical reasoning on passages and questions. Its efficacy was verified by training the model. We recruited eight annotators to tag the ground truth label, and they annotated datasets with 920, 115, and 115 passages in the train, dev, and test, respectively. A simple yet sophisticated automatic inter-annotation tool was created by effectively reducing the possibility of inaccuracy and error entailed by humans in the data construction process. This tool used common KoBERT and KoELECTRA. We defined four general conditions, and six conditions humans must inspect and fine-tune the pre-trained language models with numerically aware architecture. The KoELECTRA and NumNet+ with KoELECTRA were fine-tuned, and experiments in identical hyperparameter settings showed that compared with other models, the performance of NumNet+ with KoELECTRA was higher by more than 1.3 points. Our research contributes to the Korean MRC research and suggests potential and insight into MRC models capable of numerical reasoning.

Intelligent question and answer system for building information modeling and artificial intelligence of things based on the bidirectional encoder representations from transformers model

In recent years, building information modeling and artificial intelligence of things (BIM-AIOTs) in the construction industry have gained much attention. Construction engineers and researchers learn about BIM-AIOT and increase their professional knowledge through internet searches. However, the large amount of information on the internet makes it difficult to find specific information. Although some previous work of BIM-related searches exists, most still search with a combination of keywords or longer terms. This paper utilizes a machine learning model with natural language processing (NLP) technique of bidirectional encoder representations from transformers (BERT) integrated with a mobile chatbot as a question and answer (QnA) system. The dataset used for modeling contained 3334 text paragraphs that shortened to 10,002 questions. The result shows an F1 score of around 65% accuracy, which is acceptable for model prediction. Then, the system verifies to synchronize to the server and user interface. The system works well for information search and offers a supporting automation information system in the construction industry. This study achieved conversational machine understanding and a user-friendly BIM-AIOT integration information searches platform. The proposed system has a reliable research-based information source. It is verified as an effective and efficient way to produce fast decision-making. The system is deemed a future application for research-based problem-solving solutions in Architecture, Engineering, and Construction (AEC).

Multimodal In-training Examination in an Emergency Medicine Residency Training Program: A Longitudinal Observational Study.

BackgroundIn-training examination (ITE) has been widely adopted as an assessment tool to measure residents' competency. We incorporated different formats of assessments into the emergency medicine (EM) residency training program to form a multimodal, multistation ITE. This study was conducted to examine the cost and effectiveness of its different testing formats.MethodsWe conducted a longitudinal study in a tertiary teaching hospital in Taiwan. Nine EM residents were enrolled and followed for 4 years, and the biannual ITE scores were recorded and analyzed. Each ITE consisted of 8–10 stations and was categorized into four formats: multiple-choice question (MCQ), question and answer (QA), oral examination (OE), and high-fidelity simulation (HFS) formats. The learner satisfaction, validity, reliability, and costs were analyzed.Results486 station scores were recorded during the 4 years. The numbers of MCQ, OE, QA, and HFS stations were 45 (9.26%), 90 (18.5%), 198 (40.7%), and 135 (27.8%), respectively. The overall Cronbach's alpha reached 0.968, indicating good overall internal consistency. The correlation with EM board examination was highest for HFS (ρ = 0.657). The average costs of an MCQ station, an OE station, and an HFS station were ~3, 14, and 21 times that of a QA station.ConclusionsMulti-dimensional assessment contributes to good reliability. HFS correlates best with the final training exam score but is also the most expensive format among ITEs. Increased testing domains with various formats improve ITE's overall reliability. Program directors must understand each test format's strengths and limitations to bring forth the best combination of exams under the local context.

A Comparison of K-Means and Agglomerative Clustering for Users Segmentation based on Question Answerer Reputation in Brainly Platform

Brainly is a question and answer (QA) site that students can use as a media for questions and answers. Students can also use Brainly to find and share educational information that helps students solve their homework problems. In Brainly, users can answer questions according to their interests. However, it could be that the interest is not necessarily following the competencies possessed. It causes many answers to the questions given not to have a high rating because the answers given are of low quality to be prioritized as the main answer. This study aims to apply the K-Means and Agglomerative Clustering methods to segment users based on the reputation of the answerers by conducting clustering based on track records in answering questions on mathematics subjects. This study used the number of the brightest scores and the number of answers that did not get a rating as the basic features for clustering. The comparison between the two methods used is based on the Silhouette Score, representing the quality of the clustering results, calculated by applying the Silhouette Coefficient method. This study result indicates that the K-Means method gives better results than the Agglomerative Clustering. The Silhouette Score generated by the K-Means method is higher at 0.9081 than the Agglomerative Clustering method, which is 0.8990, which produces two clusters or two segments.

VR-enabled engineering consultation chatbot for integrated and intelligent manufacturing services

Industrial equipment manufacturing, such as electric power transformers, require highly customized design, assembly, installation and maintenance services for customers. Engineering consultation is an essential manufacturing service which requires deep domain knowledge. This research develops and implements the framework and the prototype of a Virtual Reality (VR) enabled intelligent engineering consultation chatbot system with natural language communication capabilities. The consultation chatbot case focuses on the power transformer knowledge domain. The system is constructed using a comprehensive knowledge base of Frequently Asked Questions (FAQs) and answers from a power transformer manufacturer. A total of 490,000 technical articles (with more than 1.1 billion words) from Wikipedia and a comprehensive domain-specific document set are used to train a word embedding model for retrieving questions and answers (QA) from the FAQ knowledge base. Immersive VR technology provides a highly interactive QA chatbot with realistic graphical views for informative engineering counselling. The case example demonstrates the accuracy of the proposed VR-enabled chatbot for transformer QAs exceeds 91%.

Investigative errors in the preparation of the interrogation protocol and their use by the defense

In the article, investigative errors in the preparation of the interrogation protocol are considered in the context of their use for the purposes of defense in a criminal case. Emphasis is placed on the most common mistake made in pre-trial proceedings — lack of records of the questions asked and the replies received by the person being questioned (question-and answer part of the interrogation protocol). For the first time in the legal literature, certain types of tactics for protecting the rights and legitimate interests of the principal, based on the use of such errors, are considered in detail. The choice of defensive tactics is associated with two grounds: first, the direct personal participation of the lawyer in the interrogation process is taken into account, followed by familiarization with the text of the protocol; secondly, the absence of a lawyer during the interrogation and familiarization with the text of the interrogation protocol after the end of the preliminary investigation or inquiry are taken into account. For each of the grounds, typical protective situations affecting the tactical features of the lawyer’s behavior are considered. Reasonable defense tactics require the construction of business, professional relations between the defense lawyer and the investigator (interrogator). The chosen tactic involves the submission by the lawyer of applications for the addition and clarification of the interrogation protocol, refusal to sign the interrogation protocol, filing motions and filing complaints about illegal actions of officials who refuse to eliminate the violations committed in the preparation of the interrogation protocol. In addition, the provision by the defence counsel of assistance to the investigator and the inquirer aimed at preventing and eliminating errors is not excluded. When drafting petitions and complaints, attention is paid to the reasoning of the stated requirements and recommendations are given on their content.