Performance dispersion among target date funds

Abstract: Abstract There are significant differences in the performance of Target Date Funds (TDFs) with the same target year. Using a unique dataset from Morningstar, we show that within the same target year, funds with lower than the average expense ratio, or higher than average allocation to equities, outperform similar funds. This outperformance exists across all target year groups and is economically meaningful. Furthermore, deviations in the equity allocation have a greater impact on performance than does expense ratio. Using bootstrap simulations to investigate the impact over a longer horizon, we show that deviations from the average allocations or expense ratios have a meaningful impact on the retirement savings of an average investor.


Analysis of -ary tree algorithms with successive interference cancellation

Abstract: Abstract We calculate the mean throughput, number of collisions, successes, and idle slots for random tree algorithms with successive interference cancellation. Except for the case of the throughput for the binary tree, all the results are new. We furthermore disprove the claim that only the binary tree maximizes throughput. Our method works with many observables and can be used as a blueprint for further analysis.


Impact of Dataset Scaling on Hierarchical Clustering: A Comparative Analysis of Distance-Based and Ratio-Based Methods

Abstract: In this study, the distance-based agglomerative hierarchical clustering techniques were compared to a ratio-based approach. Two real datasets, which were also used in a prior study by Roux (2018), were considered. Firstly, it was observed that the type of scaling applied to the datasets was found to affect the results of hierarchical clustering. Thus, various scaling methods were employed prior to implementing hierarchical clustering. Furthermore, two rank-based goodness-of-fit measures were used to evaluate the hierarchical clustering methods. In contrast to Roux (2018) findings, it was observed that the distance-based methods, such as Median linkage, Average linkage, and centroid linkage, performed better than the ratio-based method. The ratio-based methods also showed issues with branch crossing in the hierarchical clustering dendrogram. Consequently, this study illustrates that, with appropriate dataset scaling, the distance-based methods outperform ratio-based methods in terms of goodness-of-fit measures.


SiamFDA: feature dynamic activation siamese network for visual tracking

Abstract: Abstract In this paper, we present a novel anchor-free visual tracking framework, referred to as feature dynamic activation siamese network (SiamFDA), which addresses the issue of ignoring global spatial information in current Siamese network-based tracking algorithms. Our approach captures long-range dependencies between distant pixels in space, which enables robustness to unreliable regions. Additionally, we introduce a hierarchical feature selector that adaptively activates features at different layers, and an adaptive sample label assignment method to further improve tracking performance. Our extensive evaluations on six benchmark datasets, including VOT-2018, VOT-2019, GOT10k, LaSOT, OTB-2015, and OTB-2013, demonstrate that SiamFDA outperforms several state-of-the-art trackers in various challenging scenarios, with a real-time frame rate of 40 frames per second.


Analysis of pre-service teachers' flow experience in educational drama practices and the effect of practices on attitudes toward creative drama courses

Abstract: Aim: The analysis of the flow experience of pre-service teachers in educational drama practices and the effect of the practices on the attitude towards creative drama lessons reveal the purpose of the study. It is of great importance for physical education teachers to use creative drama practices as an effective teaching method in physical education and sports lessons, both in terms of their personal development and professional development. Method: The study was designed with a pre-test-post-test control group experimental design, one of the true experimental designs, and one of the quantitative research methods. In addition to the demographic information form, the "Flow Experience Scale" and "Attitude Scale towards Creative Drama Lessons" were applied to the participants as measurement tools. The distribution of the participant's demographic information was analyzed by frequency analysis. T-test independent groups analyzed the participants' pre-test and post-test scale scores, and the change in the pre-test and post-test scores of the scales according to demographic information was analyzed by t-test in independent groups and one-way analysis of variance. A regression model was tested to determine the effect of reason on anxiety. Analyses were performed with SPSS 20.0 software at a 95% confidence level. Findings and Conclusions: As a result, there was no significant difference between the attitude scale scores towards flow experience and creative drama course of the students participating in the research according to their age, gender, income level, place of residence, and activity type, and between the pre-tests and post-tests of the scales. However, a significant difference was detected between the pre-test and post-test scores of the flow experience scale anxiety sub-dimension and the flow sub-dimension of the academic achievement variable. In addition, although there was no significant difference in terms of the variables mentioned, it was noted that the flow experience flow and anxiety subscale scores were low. In contrast, the attitude scores towards the creative drama course were high.


Content Modeling in Multi-Platform Multilingual Social Media Data

Abstract: An increase in the use of social media as the primary news source for the general population has created an ecosystem in which organic conversation commingles with inorganically seeded and amplified narratives, which can include public relations and marketing activity but also covert and malign influence operations. An efficient and easily understandable analysis of such data is important, as it allows relevant stakeholders to protect online communities and free discussion while better identifying activity and content that may violate social media platform terms of service. To accomplish this, we propose a method of large-scale social media data analysis, which allows for multilingual conversations to be analyzed in depth across any number of social media platforms simultaneously. Our method uses a text embedding model, i.e., a natural language processing model that holds semantic and contextual understandings of language. The model uses an “understanding” of language to represent posts as coordinates in a high-dimensional space, such that posts with similar meanings are assigned coordinates close together. We then cluster and analyze the posts to identify online topics of conversation existing across multiple social media platforms. We explicitly show how our method can be applied to four different datasets, three consisting of Chinese social media posts related to the Belt and Road Initiative and one relating to the Russia-Ukraine war, and we find politically-influenced conversations that contain misleading information relating to the Chinese government and the Russia-Ukraine war.


Penerapan Model Pembelajaran Berbasis Maslah Untuk Meningkatkan Hasil Belajar Siswa Kelas XI Pada Materi Hukum Pascal

Abstract: Research on the problem based learning model on Pascal's law material aims to determine student learning outcomes in class XII. This research is classroom action research that uses Kurt Lewin's model or method. Kurt Lewin's classroom action research model consists of several stages, namely planning, action, observation and reflection. In this research, two cycles of research were carried out. Model based learning is often called problem based learning. By providing problems, it is hoped that students can be active in searching for and finding solutions to the problems given. With problem-based learning, educators try to invite students to be active in learning activities. Based on the results of data analysis, it was found that this learning experienced an increase in the percentage of learning completion and an increase in the class average. So the percentage of students who do not complete has decreased. The data results in cycle I showed that student learning completeness was 82.35% and in cycle II it rose to 88.48%. In cycle II the average student score was 76.47 and in cycle II the average student score rose to 85.25%. Meanwhile, the percentage of student failure to complete in cycle I was 17.65% and in cycle II it decreased to 11.52%. From the data results it can be concluded that the use of model based learning in Pascal's law material for class XII students can improve student learning outcomes.


An Analysis of Narratives on Vocational Consignment Education Experience of Students in General High Schools

Abstract: The purpose of this study is to understand in-depth the context of the situation and the subjective meaning composition in the selection of vocational consignment education, the life process, and the process of returning to school and adaptation after the interruption, focusing on the experience story that constitutes the personal and social life of students who experience vocational consignment education in general high schools. Therefore, this study examined the selection and life process of vocational consignment education and the process of returning to school and adaptation after the interruption of consignment education by using narrative analysis method for the stories directly dictated by students of vocational consignment education in general high schools. The results of this study are as follows: First, the choice of vocational consignment education for general high schools considers one's aptitude and talent, and the interaction with the surrounding people and the composition of one's meaning play a very important role in the difference of the choice decision. Next, in the career consignment education life of general high schools, it was found that there was a great psychological and emotional reward that strengthened career awareness by having a sense of accomplishment or confidence due to the pride of preparing for the desired job in accordance with the aptitude and talent of the students, the support of the teacher, and the learning success experience coming from the consignment curriculum. However, there was a case where students in the consignment educational institution did not form a sense of identity or belonging properly. In addition, psychological and cultural factors such as the burden and difficulty of acquiring practical training and certification, the sense of heterogeneity felt in the collective culture among the consignment education students, the difficulties in communication and exchanges with the consignment education participants, and the reconsideration of their interests and interests through consignment education and the turning of their career goals to college were the complex factors that determined their return to school after the interruption of consignment education.


USE OF SONG MEDIA IN LEARNING AS AN EFFORT TO INCREASE STUDENTS' LEARNING MOTIVATION ON THE SUBJECTS OF SCIENCE

Abstract: This Best Practice writing aims to describe the use of song media to improve students' understanding of the material taught by the teacher. The subjects of this Best Practice writing were 25 students in class IV A of Wirasakti Atambua State Elementary School, West Atambua District. Writing this Best Practice uses a descriptive method. The achievement of Best Practice activities is 100%. This is because all activities have been carried out and the output obtained is in line with expectations. The use of song media to increase student motivation towards the material taught by the teacher is effective. The results of the implementation of Best Practice that have been implemented are: 1). Learning activities using song media related to the material studied every day are effective. 2). The use of song media in learning really helps students' understanding of the material being taught because it can be sung repeatedly both in class and at home. 3). Based on the learning that has been carried out, differences can be felt between students who carry out learning using song media and using the lecture learning model. 4). The activities that have been designed in the RPP (Teaching Module) can be implemented fully in accordance with what has been planned. The increase in students' understanding of the material being taught can be seen from the achievement of student learning outcomes, through the results of the pre-test and post-test that have been carried out by the teacher, where in the pre-test of the total number of students in class IV A, only 7 students or 2.24% who are able to achieve the KKM value. Meanwhile, in the post test results, there was a significant increase, namely 25 students, 7 students or 2.24% achieved the KKM score and 18 students or 92.8% were able to achieve a score above the KKM score.


Prolegomena about the cluster-network model organization of the educational system in musical academies of Ukraine

Abstract: The article „Prolegomena about the cluster-network model organization of the educational system in the musical academies of Ukraine” discusses the complexity of higher musical education and the need for new scientific approaches to address the changing pedagogical reality. The authors propose a cluster-network model for organizing the educational system in musical academies, which is based on the following components: conceptual – tasks, methods, forms, means; axiological– values; psychological and pedagogical – mechanisms of learning values; organizational and management – management of elements of the educational structure. The cluster-network model aims to increase the competitiveness of the educational system by developing the students’ competencies that meet the requirements of the social order. It also emphasizes the importance of spirituality in education and the need for a holistic approach to human development. The authors argue that the ideal of human education is constantly changing, but the ability to correlate the concepts, ideas, and theories acquired during cognitive activity with an individual value system is a key indicator of successful education. The article highlights the importance of understanding the inner class and spiritual being of a person, as well as the need to implement some methods that contribute to penetrating into the phenomenological secrets of human consciousness for researching and revealing the most important mental structures of man. It also discusses the role of dialogue as a way of existing and coexisting- with the „other” in the communication process. The proposed model aims to address the challenges of the postmodern era, where the world is thought of as an open text not limited in its manifestations and interpretations. It emphasizes the importance of continuous education and the integration of knowledge from related disciplines to develop digital education and innovative work practices.


Six miniatures for violin and piano by A. Mulear: analytical references

Abstract: Alexandr Mulear - one of the prominent and profoundly original personalities of the local music culture, has been prodigiously active over the years in the field of musical creation, leaving a portfolio that abounds in notable works, substantially contributing to the enrichment of the interpretive repertoire. The genres approached by the composer are diverse and extend over a vast compositional area. However, a major proportion of the highlighted works are instrumental works with cyclical forms. Six Miniatures for Violin and Piano is one of the works that marks the stage of the master’s interest in compositional experimentation. In this article, we propose some keys for decoding the form, language, compositional aspects, reception and valorization of A. Mulear’s music through the prism of the analyzed work.


Rag-Fusion: A New Take on Retrieval Augmented Generation

Abstract: Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots, but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines RAG and reciprocal rank fusion (RRF) by generating multiple queries, reranking them with reciprocal scores and fusing the documents and scores. Through manually evaluating answers on accuracy, relevance, and comprehensiveness, I found that RAG-Fusion was able to provide accurate and comprehensive answers due to the generated queries contextualizing the original query from various perspectives. However, some answers strayed off topic when the generated queries' relevance to the original query is insufficient. This research marks significant progress in artificial intelligence (AI) and natural language processing (NLP) applications and demonstrates transformations in a global and multiindustry context.


Improving Dialogue Management Through Data Optimization

Abstract: In task-oriented dialogue systems, the ability for users to effortlessly communicate with machines and computers through natural language stands as a critical advancement. Central to these systems is the dialogue manager, a pivotal component tasked with navigating the conversation to effectively meet user goals by selecting the most appropriate response. Traditionally, the development of sophisticated dialogue management has embraced a variety of methodologies, including rule-based systems, reinforcement learning, and supervised learning, all aimed at optimizing response selection in light of user inputs. This research casts a spotlight on the pivotal role of data quality in enhancing the performance of dialogue managers. Through a detailed examination of prevalent errors within acclaimed datasets, such as Multiwoz 2.1 and SGD, we introduce an innovative synthetic dialogue generator designed to control the introduction of errors precisely. Our comprehensive analysis underscores the critical impact of dataset imperfections, especially mislabeling, on the challenges inherent in refining dialogue management processes


Evaluation of Medium-Sized Language Models in German and English Language

Abstract: Large language models (LLMs) have garnered significant attention, but the definition of “large” lacks clarity. This paper focuses on medium-sized language models (MLMs), defined as having at least six billion parameters but less than 100 billion. The study evaluates MLMs regarding zero-shot generative question answering, which requires models to provide elaborate answers without external document retrieval. The paper introduces an own test dataset and presents results from human evaluation. Results show that combining the best answers from different MLMs yielded an overall correct answer rate of 82.7% which is better than the 60.9% of ChatGPT. The best MLM achieved 71.8% and has 33B parameters, which highlights the importance of using appropriate training data for fine-tuning rather than solely relying on the number of parameters. More fine-grained feedback should be used to further improve the quality of answers. The open source community is quickly closing the gap to the best commercial models.


The development of traditional and electronic forms of the book in the context of innovative technologies

Abstract: Modern technologies have a decisive impact on almost all spheres of human activity, thereby providing opportunities for the development of various forms of objects that are already familiar to us. This study is devoted to the research of new forms of printed and electronic books, the analysis of the specifics and features of the transformation of familiar forms in the context of the development of innovative technologies. An attempt was made to predict further development paths and prospects for long-term coexistence of traditional and electronic publications, areas of their confrontation and interaction.


Document Author Classification using Parsed Language Structure

Abstract: Over the years there has been ongoing interest in detecting authorship of a text based on statistical properties of the text, such as by using occurrence rates of noncontextual words. In previous work, these techniques have been used, for example, to determine authorship of all of The Federalist Papers. Such methods may be useful in more modern times to detect fake or AI authorship. Progress in statistical natural language parsers introduces the possibility of using grammatical structure to detect authorship. In this paper we explore a new possibility for detecting authorship using grammatical structural information extracted using a statistical natural language parser. This paper provides a proof of concept, testing author classification based on grammatical structure on a set of “proof texts,” The Federalist Papers and Sanditon which have been as test cases in previous authorship detection studies. Several features extracted from the statistical natural language parser were explored: all sub trees of some depth from any level; rooted subtrees of some depth, part of speech, and part of speech by level in the parse tree. It was found to be helpful to project the features into a lower dimensional space. Statistical experiments on these documents demonstrate that information from a statistical parser can, in fact, assist in distinguishing authors.


Integration of Latex Formula in Computer-Based Test Application for Academic Purposes

Abstract: LaTeX is a free document preparation system that handles the typesetting of mathematical expressions smoothly and elegantly. It has become the standard format for creating and publishing research articles in mathematics and many scientific fields. Computer-based testing (CBT) has become widespread in recent years. Most establishments now use it to deliver assessments as an alternative to using the pen- paper method. To deliver an assessment, the examiner would first add a new exam or edit an existing exam using a CBT editor. Thus, the implementation of CBT should comprise both support for setting and administering questions. Existing CBT applications used in the academic space lacks the capacity to handle advanced formulas, programming codes, and tables, thereby resorting to converting them into images which takes a lot of time and storage space. In this paper, we discuss how we solvde this problem by integrating latex technology into our CBT applications. This enables seamless manipulation and accurate rendering of tables, programming codes, and equations to increase readability and clarity on both the setting and administering of questions platforms. Furthermore, this implementation has reduced drastically the sizes of system resources allocated to converting tables, codes, and equations to images. Those in mathematics, statistics, computer science, engineering, chemistry, etc. will find this application useful.


A Comparative Study of Text Comprehension in IELTS Reading Exam using GPT-3

Abstract: This paper discusses the capabilities and limitations of GPT-3 (0), a state-of-the-art language model, in the context of text understanding. We begin by describing the architecture and training process of GPT-3, and provide an overview of its impressive performance across a wide range of natural language processing tasks, such as language translation, question-answering, and text completion. Throughout this research project, a summarizing tool was also created to help us retrieve content from any types of document, specifically IELTS (0) Reading Test data in this project. We also aimed to improve the accuracy of the summarizing, as well as question-answering capabilities of GPT-3 (0) via long text.


The Influence of Parenting Style on Student Academic Achievement Based on Gender

Abstract: The literature recognizes the importance of parenting and its impact on children's academic achievement at school. Many factors influence children's academic achievement, and parenting patterns and gender are significant factors that influence children's academic achievement outcomes at school. However, few studies still discuss the relationship between parenting and children's educational achievement outcomes based on gender. This study used a systematic literature review method. This study used three databases (Google Scholar, JSTOR, and Springer Link). The results of this study show that, based on authoritative, authoritarian, permissive, and neglectful parenting, authoritative parenting is the most effective parenting method in improving children's academic achievement, both girls and boys. In addition, with authoritative parenting, children can have good social and emotional skills.


Analisis Kemampuan Komunikasi Siswa Autis Kelas VII di SLB Negeri Autis Sumatera Utara (Studi Kasus Siswa Autis di SLB Negeri Autis Sumatera Utara)

Abstract: This research was conducted with the aim of knowing how the communication development of class VII autistic students at SLB Negeri Autism North Sumatra and to find out the forms of verbal and non-verbal communication. The method used in this study is a qualitative method with a case study approach. The primary subjects in this study were two students with autistic disorder in class VII autistic, and the secondary subjects were teachers and parents. Data were collected using interviews, observation, and documentation studies. The results showed that the development of communication in autistic students at the North Sumatra Autistic Public SLB was influenced by several things including the teacher's stimulus when teaching, therapy, children's hobbies, and parenting strategies. Forms of children's verbal communication is speaking, writing, reading, and listening. While the forms of non-verbal communication are touch, movement, and eye contact. Based on the research findings, it can be concluded that communication disorders in autistic children can be helped by providing stimulus by being invited to communicate more often and implementing appropriate parenting strategies.


Analisis Gaya Belajar Mahasiswa Program Studi Pendidikan Bahasa Indonesia Universitas Katolik Weetebula

Abstract: The purpose of this study is to describe learning styles and describe solutions in presenting learning in accordance with the learning styles of students of the Indonesian Education Study Program, Weetebula Catholic University. This type of research is survey research with a qualitative descriptive approach. The subject of this study was a student of PBI Study Program, Weetebula Catholic University. The data collection technique used in this study was questionnaire. Based on the results of the study, it was found that students who tended to visual learning style as much as 53.74%, auditorial learning style as much as 29.43%, and kinesthetic learning style as much as 16.83%. The results of these findings show that the most dominant type of learning style of Indonesian Education Study Program students is the visual learning style type. Lecturers in presenting learning the main type of learning used is the visual type of learning. To accommodate the other two learning styles, learning needs to be supported by auditorial and kinesthetic learning types. Research related to learning styles is important. The results of the research can be information for teachers or lecturers in making learning designs that are in accordance with the learning styles of students so that students can absorb the material optimally


Pengaruh Discovery Learning Terhadap Upaya Pengembangan Keterampilan Komunikasi Siswa: Studi Literatur

Abstract: Communication skills are one of the skills that students really need. Students' communication skills can be developed using discovery learning methods. The discovery learning method trains students to be more active in learning activities. This research aims to describe efforts to improve communication using discovery learning methods. The type of research we use is literature study sourced from various journals and articles obtained by searching for topic-related data in academic databases by searching for relevant keywords then accessing them online on the official website of scientific journals or digital libraries and then analyzing the data contained in the articles. and journals according to research needs. This research proves that the discovery learning method has proven to be effective and able to improve students' communication skills. This is because the discovery learning method focuses more on the child's own activity, so it is not impossible that children will hone their communication skills even more. Children will be encouraged to search for material, exchange material, and express opinions. The teacher will act as a facilitator


Teacher’s Talk in English Classroom Using Foreign Language Interaction (FLINT) System

Abstract: The researcher conducted the research to describe the teacher and student interaction during the teaching-learning process and to analyze the frequent FLINT categories by Moskowitz (1976). The participant in this research was an English female teacher taught in class VII F at SMP Negeri 1 Sungai Raya in the academic year 2022/2023. This research was a case study design that used qualitative method. The data was collected by observing the teacher and student interaction during the teaching-learning process. The researcher used the teacher talk video transcript in analyzing the FLINT categories. The result of the research showed that the teacher’s dominant language was Bahasa Indonesia. The teacher’s utterances in Bahasa Indonesia appeared 95 times during the teaching-learning process, which means the teacher used Bahasa Indonesia in English lessons. The teacher should be built good oral during the teaching-learning process, not only focus on delivering the material. The frequent categories used by the teacher were giving information and asking questions. Moreover, the rare category the teacher used was the student’s ideas. Through analyzing the FLINT system, teacher talk in the classroom was accessible in describing the dominant language performed by the teacher and analyzed frequent categories used by the teacher during the teaching-learning process.


Pengaruh Media Pembelajaran Informasi dan Komunikasi Teknologi dan Minat Belajar Terhadap Hasil Belajar Siswa pada Mata Pelajaran IPS di SMP Negeri 1 Ingin Jaya Kabupaten Aceh Besar

Abstract: This research aims to improve the learning process with a variety of ICT-based media and examine the influence of information and communication technology learning media and learning interest on student learning outcomes in Social Sciences subjects at the Want Jaya 1 State Junior High School, Aceh Besar Regency. This research is a type of survey research with quantitative methods. the results of the research show that: there is an influence of information media and communication technology and interest in learning on student learning outcomes in social sciences subjects at the 1 Want Jaya State Junior High School, Aceh Besar Regency which together have a significant influence, this can be seen from the three variables. has a high influence, namely 34.5 for the learning media variable and 37.9 for the learning interest variable, 0.24, which means the learning media and learning interest variables, and the school environment has an influence of 24.4% on learning outcomes, while the second These variables together have an influence on student results in the Social Sciences subject at Want Jaya State Junior High School 1, amounting to 49.4.


Exploring the Adoption of the Metaverse System among Elementary Students

Abstract: The metaverse is an envisioned realm offering immersive digital spaces, bolstering highly interactive environments within educational contexts. This digital dimension amplifies synchronous communication, uniting a vast number of users as they share their unique experiences. This research aims to scrutinize the perceptions of elementary students towards the metaverse system (MS). Our proposed model underwent validation through the Partial Least Squares Structural Equation Modelling (PLS-SEM) technique. Data, sourced from 287 students of Dubai National School, revealed that the primary predictor of students' intent to engage with the MS is personal innovativeness. This is followed closely by perceived usefulness and then perceived ease of use. The findings of this research provide deeper insights into the driving factors behind the adoption of MS among young learners.


METODE ENSEMBLE ROBUST CLUSTERING USING LINKS (ROCK) UNTUK PENGELOMPOKAN PERGURUAN TINGGI SWASTA (PTS) DI KOTA SEMARANG

Abstract: The purpose of this research is to group PTS that have performance achievements in five years, through the quality of Human Resources and Students (Input), the quality of Institutional Management (process), the quality of Short-Term Performance Achievements (Output) and the quality of Long-Term Performance Achievements (Outcome). In addition, it can also be seen from the form of PTS, PTS Accreditation and PTS Research Performance. This PTS grouping uses mixed data, namely numerical data and categorical data. The method used for grouping mixed data is the ROCK ensemble method (Robust Clustering Using Links). The results of clustering numerical data obtained the optimum number of groups 3, on categorical data obtained the optimum group 4. After clustering each type of data and merging and clustering obtained the optimum group 3 with a threshold (θ) is 0.2. The results of each group are: low quality consist of 29 PTS, medium quality consist of 7 PTS, and high quality there is 1 PTS. The results of this research can be used to cluster private universities in Semarang City, so that it can be used as a reference for prospective students in choosing private universities in Semarang, and can be referenced to the Central Java LLDIKTI in determining the quality of private universities in Semarang City.


Identifikasi Faktor Kesulitan Belajar Membaca Siswa di Kelas VI di SDN Karangpawitan III

Abstract: This study aims to determine the factors that cause difficulties in learning to read for students. The type of this research is descriptive qualitative research. The subjects of this research consist of sixth grade students and the homeroom teacher of the sixth grade. Data collection methods include interviews, observations, and documentation. The data obtained is then analyzed to determine the internal and external factors that cause difficulties in learning to read for students. The results of the study show that the factors causing difficulties in learning to read for sixth grade students at SDN Karangpawitan III are caused by internal factors (low intelligence, low student learning motivation, low student interest in reading, as well as social and emotional maturity and unstable student adjustment) and external factors (less varied teaching methods by teachers and lack of parental attention to students in the family environment). Therefore, it can be concluded that there are several factors causing difficulties in learning to read for students, namely internal and external factors.


PERBANDINGAN KINERJA METODE KLASIFIKASI K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINES PADA DATASET PARKINSON

Abstract: The government program in the form of social assistance (bansos) is part of the effort to improve the welfare of the community and ensure basic needs and improve the standard of living of the recipients. However, there are often cases of mistargeting of social assistance programs by the government. Improper data management and Data Terpadu Kesejahteraan Sosial (DTKS) which are not used as the cause of the distribution of social assistance are not well targeted. The data can be analyzed using the classification method to determine whether or not the family accepts the ban from the government. This study classifies the SUSENAS data by comparing K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). The advantage of the KNN method lies in the level of accuracy to solve problems with large data while the SVM method has better performance in various fields of application such as bioinformacs, handwriting recognition, text classification and so on. Based on training data and testing data comparison 85%:15% showed that KNN method had a better classification performance than the SVM method. The accuracy value of KNN method is 80,95% higher than the accuracy value of SVM method is 78,79%.


The Effects of Education Service Quality and University Image on Student Satisfaction and Switching Intention - Focusing on Tourism/Hotel Departments of Four-Year Course Local University -

Abstract: This study is intended to investigate the influences of education service quality and university images on students' satisfaction and switching intention for those who major in hotel·tourism at local universities. Through the empirical analysis, the levels of education service quality is identified to enhance the quality of education, to establish school images and to improve value cognition, so that hotel·tourism related majors can be improved and developed. For the accomplishment of the purpose of this study, a survey was conducted on hotel·tourism majors in local four-year-course universities, and the data collection was done from May 20 to June 18, 2023. For the response samples, total 154 copies were used, and multiple regression analysis was done with the use of SPSS.20. In the results of the analysis, three factors of human, facility and welfare services in the education service quality have a significant influence relation, but administrative and education services were dismissed. Therefore, the hypothesis was partially adopted. The university image had a significant impact on student satisfaction, and student satisfaction had a negative impact on switching intention. In addition, the human service was the only one among the education service quality that had a negative impact on switching intention, which led to partial adoption. Based on the above results, this study can be used for basic resources to establish methods to prevent switching intention by comprehending improvement factors of education service quality and by enhancing student satisfaction.


Aspiración y Expectativa: Un Estudio de Percepción Social Sobre Robot Imaginario

Abstract: Technological advancement has led to a series of transformations in human life that have deeply permeated the innermost circle of the social sphere. Among these innovations, the development of social robots stands out, whose influence on the relationship between humans and technology, particularly in the field of care, is notable. The objective of this study is to carry out an investigation into the aspirations and expectations related to the development of social robots, from the perspective of older people and children, in the context of care in three countries: Spain, Romania, and Italy. With the involvement of 41 elderly people and 44 children, this study aims to identify the elements that generate feelings of approval or disapproval in relation to social robots. Using a participatory approach, this study expands the understanding of the perceptions and aspirations of children and elderly people, in relation to their vision of what shape a social robot might take. The analysis highlights that there is a tendency to feminize the representation of the imagined robot, as a consequence of the association with attributes and roles traditionally considered feminine in society.


Kajian Literatur: Adopsi Artificial Intelligence (AI) dalam Bidang Jurnalistik

Abstract: The journalism has adopted technology at the next level, where Artificial Intelligence (AI) collaborates in the journalistic field. The aim of this research is to explore AI in the field of journalism and its perceived impact on journalistic products. Literature studies were carried out in international journals indexed by Scopus for 2015-2023. As a result, AI-based journalism is defined as an independent system for collecting, analyzing and processing data through programs created by humans both textually and visually; AI ethics in the form of collecting credible and trustworthy data that has been selected by the editorial team; advantages in the form of being able to overcome contemporary journalistic problems and challenges in the form of journalistic competence.


Informasi Anatomi MSCT Sinus Paranasal pada Suspek Sinusitis dengan Variasi Rekonstruksi Algorithma

Abstract: Background: The standard operating procedure with the bone window technique and bone reconstruction algorithm is referred to the MSCT protocol for paranasal sinuses in Hospital. However, the majority of radiologists select a protocol that implements the algorithm reconstruction, which is still trial and error without an organized protocol development study. There is a chance that the accuracy of the MSCT SPN and the quality of the picture data may become crucial problems. This study set out to assess and examine the algorithmic reconstruction method that can yield more accurate SPN anatomical data in sinusitis suspects.Methods: A quasi-experimental technique was taken in conducting the research. Three filters (bone, boneplus, and edge) of the reconstruction method were used to get thirty SPN images from ten patients. The images were assessed by the two expert radiologist.Results: The results of non-parametric was obtained based on statistical tests using the Friedman test ρ-value of anatomy, namely 0.00 less than 0.05. These results indicate that H0 is rejected and Ha is accepted, meaning that there are differences in anatomical information between variations of bone, boneplus, and edge reconstruction algorithms on the MSCT scan examination of the paranasal sinuses with sinusitis suspect. The Friedman test results using the mean rank values of each anatomy show that Boneplus is superior in terms of visualizing anatomy. The Friedman test's mean rank value of the entire anatomy yielded the result that the boneplus reconstruction algorithm is superior to the bone and edge reconstruction algorithm in displaying anatomical information on the MSCT scan of the paranasal sinuses with sinusitis suspect.Conclusions: Based on value mean rank For each Friedman test anatomy and the results of the frequency distribution, variations of the bone plus reconstruction algorithm are more optimal in displaying anatomical information on the MSCT Scan of the paranasal sinuses compared with the edge and bone reconstruction algorithms.


Nilai Entrance Skin Dose Tiroid antara menggunakan Thyroid Shield dan tanpa Thyroid Shield pada Pemeriksaan CT Scan Kepala

Abstract: Background: CT scans have a much greater dose than conventional radiographic examinations. The thyroid organ is an organ that is sensitive to radiation which is located near the head, precisely in the inferior part of the head so that when a CT scan of the head is carried out, the thyroid organ will be exposed to radiation. The aim of this study was to determine the difference in thyroid skin dose entry value between using a thyroid shield and without a thyroid shield on a head CT scan.Methods: This study is a type of quantitative research with a pre-experimental design. The object of research is Entrance Skin Dose (ESE) of ThyroidOrgan on non-contrast head CT Scan examination. The research location is at the Radiology Department at the Regional General Hospital Prof. Dr. Margono Soekarjo Purwokerto. Sample data number is 50 people (25 samples of non-contrast head CT scans without thyroid shield, and 25 samples of non-contrast head CT scans with thyroid shield. Data was obtained by measuring the radiation dose using a dosimeter placed over the thyroid area during a non-contrast CT scan between using a thyroid shield and without using a thyroid shield. Data analysis was performed using statistical tests Results: The results of measuring the radiation dose to the samples showed that the average radiation dose to the skin surface of the thyroid organ on a non-contrast CT scan without using a thoracic shield was 884.96 µSv and using a thyroid shield was 428.64 µSv. This shows that the average radiation dose from the skin surface of the thyroid organ decreased by 456.32 µSv (51.56%).Conclusions: There is a significant difference in the amount of radiation dose to the surface of the skin of the thyroid organ in non-contrast head CT scans between using a thyroid shield and without using a shield with a dose reduction of 51.6% of the radiation dose without using a thyroid shield.


Implementasi Pemahaman Radiografer terhadap Exposure Index dalam Optimasi Prosedur Pemeriksaan Radiografi Computed Radiography

Abstract: Background: Increasing the exposure factor is often carried out by radiographers to reduce noise and maintain image quality in computer radiography (CR). Therefore, there is a need for awareness and understanding of the exposure index (EI) to implement optimization procedures in radiographic examinations. Methods: The research method is descriptive and analytical by providing questionnaires to radiographers in PA chest radiographic examinations. This research was carried out at hospitals A, B, and C in the Jakarta-Bogor area with the research carried out between August – October 2023. Statistical analysis used SPSS 27 with data coding previously. After that, the data is processed with descriptive statistics to determine the frequency, distribution, percentage, average, and standard deviation. Then, the test chi-square to determine the relationship between gender, age, education, and work experience in understanding CR optimization procedures. Next, to find out the relationship between age and weight and EI, a test was carried out by Pearson test to determine the level of relationship between the two variablesResults: was found that the level of awareness and understanding of radiographers regarding optimization procedures using EI is still low. Test results chi-square age-understanding 0.309, gender-understanding 0.772, education-understanding 0.036, experience-understanding 0.201. next are the test results Pearson for Hospital-A: age-EI 0.09, weight-EI -0.49, Hospital-B: age-EI -0.21, weight-EI 0.06, Hospital-C: age-EI -0.37, weight-EI 0.56. Conclusions: implementing EI in the optimization procedure, it needs to be confirmed by measuring the patient's radiation dose so that there are no errors in implementing the optimization.


Exploring the Potential Applications of Blockchain Technology in Accounting Practice: A Systematic Literature Review

Abstract: This study aims to examine the potential of blockchain technology in revolutionizing conventional accounting processes and mitigating persistent inefficiencies and problems. This study introduces the potential of blockchain technology to revolutionize conventional accounting processes and address persistent inefficiencies and challenges. By employing a rigorous search methodology, establishing specific criteria for selection, and conducting meticulous data analysis, this paper presents significant discoveries pertaining to the implementation of blockchain technology in the field of accounting. The results suggest an increasing fascination with the potential of blockchain technology to bring about a transformative impact on the accounting sector. The use of blockchain technology in the field of accounting encompasses several areas such as the recording of transactions, automation of accounting operations, and enhancement of auditing procedures. Application of this technology contributes to the improvement of transparency, integrity, and efficiency in financial reporting. However, for widespread adoption to occur, it is imperative to overcome many difficulties like standardisation, interoperability, scalability, legal compliance, and data protection. The study emphasizes the importance of investigating the possibilities of blockchain technology in the field of accounting to address conventional accounting obstacles. The findings of this study may be utilized by researchers to discover potential areas for further research. Additionally, practitioners can acquire valuable insights into the advantages and difficulties associated with implementing these findings.


Game Multimedia tentang Sistem Tata Surya Berbasis Android sebagai Media Edukasi pada Anak Usia 6-12 Tahun

Abstract: The solar system is one of the materials from natural science lessons taught in elementary school. However, many students have difficulty understanding and memorizing material about the solar system because of the many different characteristics of each planet. This happens because of the limited learning media provided by the teacher when introducing parts of the solar system. The delivery of material to students must be with a fun method so that students are not easily bored and easy to understand every side of learning. games have great potential in educating children about the solar system, of course, using games that are fun and fun will be more effective for children to absorb all the information contained in the games they play. The design of this game uses the Design Thimgking method, so it can be concluded that this design is a problem originating from a sense of empathy, the need for a solution to an existing problem, then continue to test for improvement. Then the game is created as the main media, then accompanied by other supporting media such as Posters, X-Banners, Game Cards, Stickers, Keychain, and Pins.


STRATEGI PENGGUNAAN FLYER DI MEDIA SOSIAL SEBAGAI SARANA UNTUK MENARIK MINAT BACA MASYARAKAT

Abstract: The purpose of this study is to describe the activities conducted by religious counselors and to assess the public's interest in flyer media for guidance or counseling. Religious counselors have the task of conducting counseling guidance, developing guidance, and professional development. Before conducting counseling guidance, religious counselors must prepare monographs and activity plans, as well as develop guidance materials and evaluation instruments. This research method uses a qualitative approach, with data collection techniques through observation, interviews, and documentary studies. The method used is descriptive. This study concludes: First, the tasks of religious counselors in conducting counseling guidance for the community begin with guidance preparation, counseling/information services, work plan preparation, guidance or counseling material preparation, guidance implementation, counseling assistance, religious problem mediation, guidance monitoring and evaluation, cross-sectoral cooperation activities, guidance or counseling model development, and guidance or counseling guideline preparation; Second, Flyer media can attract the public's/ congregants' interest in reading. In this case, flyer media is distributed through social media, known as e-flyers. Flyers distributed through social networks are faster, more massive, and more focused on the recipients, accompanied by a call to action to encourage readers to continue reading on the religious counselor's blog.


THEORETICAL AND PRACTICAL PRINCIPLES OF FORMING THE SKILLS OF STUDENTS TO WORK ON THEMSELVES IN MUSIC LESSONS

Abstract: This article provides a theoretical and practical basis for developing students' self-study skills in music lessons. Music culture classes focus on the importance of activities for students to work on a regular basis, and on how students can improve their musical knowledge through music literacy, listening to music, and singing as a group.


MODERN APPROACHES TO PROMOTING YOUTH CULTURE

Abstract: The article examines the conditions and topical issues of comprehensive and cultural improvement of the culture of modern youth by further improving the educational process in the family and educational institutions.


Effective Teaching by the Association of College and University Educators (ACUE) in Undergraduate Mathematics Class

Abstract: Teaching effectively in college is not only key for student’ satisfaction in classroom but also for performance. This research, as a pilot study, has analyzed the survey and course evaluation results from an undergraduate Mathematics class, where The Association of College and University Educators’ (ACUE) effective teaching practices are applied. Results have shown the positive effects from ACUE practices. At the same time, differences in student performance in the same courses from different semesters have been discussed. With the same course content and learning outcomes, teaching with ACUE practices has resulted in better performance throughout the semester. The research compared course evaluations from the same courses in different semesters when including and not including ACUE practices. Results show that students are more satisfied with the courses using ACUE practices. Researcher has made suggestion that ACUE practices should be recommended for use by faculty members in universities and colleges to improve student performance and course satisfaction.


Bibliometric Analysis of Research Trends in Problem Based Learning and Problem Solving Ability (2002-2022)

Abstract: This study aims to analyze research trends in problem-based learning and problem-solving ability through bibliometric analysis in the Dimensions.ai databases from 2002 to 2022. The method used is bibliometric analysis with stages following the flow chart of Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). The results of the research show that the number of problem-based learning publications and problem-solving ability has continued to increase since 2018. The number of publications on problem-based learning and problem-solving ability ranks first in the field of education. While the most published journal is the Journal of Physics Conference Series. The first rank in the number of publications reviewed by the researcher, I Made Arnawa. Based on the keywords problem based learning and problem solving ability the two are not connected. Thus, the topic of problem-based learning and problem-solving ability can be used as novelty for research and will become a trend in the future. Articles that can be used as references in research that reviews problem-based learning and problem-solving ability are from the Journal of Physics Conferences Series. Meanwhile, the author who can be used as a reference is I Made Arnawa.


PENGARUH MODEL PEMBELAJARAN DISCOVERY LEARNING TERHADAP HASIL BELAJAR SISWA KELAS VII SMP NEGERI 5 MANIAMOLO

Abstract: Based on the background of the problem, student activity seems to be lacking in the learning process, especially student activity in asking the teacher regarding the material being discussed, students do not understand the material presented, there is a reluctance of students to ask the teacher because they are embarrassed even though they are given the opportunity, but students only listen to the lecture. and do the exercises given. This research aims to determine the influence of the discovery learning model on the learning outcomes of class VII students at SMP Negeri 5 Maniamolo. This type of research is quantitative, descriptive in nature. The population of this research is class VII students, while the sample of this research is students of class VII-A and class VII-B of SMP Negeri 5 Maniamolo. This research data was analyzed using normality tests and hypothesis testing using the SPSS (Statistical product and service solution) application. The results of the two classes, namely the control class and the experimental class, can be said to be more students who have completed learning using the discovery learning model compared to the learning outcomes of students using the conventional learning model and while participating in learning activities using the discovery learning model it appears that in the initial test, students still did not understand the material presented, whereas in the final test after completing the learning process the students really understood what was conveyed by the teacher and were able to get through the assignments given with quite good learning results. Researcher suggestions (1) teachers should use the discovery learning learning model in learning activities because it makes students diligent and think more critically in learning and the attraction gained by studying social studies. (2) Future researchers should carry out this research with a broader scope.


Pelatihan Penggunaan Cairan Klorin Untuk Menurunkan Angka Mikroorganisme Pada Kaset Radiografi

Abstract: Radiography has an important role in diagnosing a patient's disease, but the equipment used has a considerable risk as a place for the development of nosocomial bacteria which can endanger the patient and radiographer. Cassettes, one of the tools in radiography, have the potential to become a medium for disease transmission and based on previous research it has been proven that there are microorganisms and fungi on the cassettes, so the cassettes need to be cleaned regularly. One of the duties of a radiographer in the field of radiology services is the management of radiology equipment facilities and infrastructure. Cleaning the cassette with 70% alcohol can reduce the number of microorganisms on its surface. The methods in this activity are lectures, simulations, and practice cleaning cassettes with chlorine. The activities were carried out at the Laboratory of the Department of Radiodiagnosis and Radiotherapy Technique at the Semarang Health Polytechnic Ministry with radiographers from hospitals and clinical laboratories in the city of Semarang. The activity begins with the registration, opening, delivering material to the participants, and the simulation of resource persons. Evaluation is done by filling in the pretest and posttest. The evaluation results showed an increase in participants' knowledge of 63% about how to clean cassettes using liquid chlorine to reduce the number of microorganisms on radiographic cassettes. With this training, radiographers are expected to be able to apply cleaning radiographic cassettes with liquid chlorine 0,5% in their respective institutions.


Analysis of Quantum Machine Learning Algorithms in Noisy Channels for Classification Tasks in the IoT Extreme Environment

Abstract: By 2050, there will be a 50% rise in energy demand, and existing natural and renewable resources will be under extreme scrutiny. Optimizing current power generation and transmission to reduce energy consumption, cost, and other factors is equally vital to upgrading methods for effectively harvesting renewable energy. However, it gets more challenging for conventional computers to perform optimization as the number of factors affecting power generation and transmission rises. Extreme environmental cases will consequently lead to the imperfect functioning of IoT systems. By utilizing quantum-mechanical properties, such as superposition and entanglement, quantum computers can computationally outperform classical computers while consuming much less energy. In this paper, we investigate various quantum machine learning algorithms on two datasets (TWTDUS and SDWTT18) related to IoT extreme environment and study the effect of a noisy quantum environment. We observe that for the TWTDUS dataset, the variational UU† with analytical clustering methods achieves the highest accuracy of 98.10%. Similarly, for the SDWTT18 dataset, the UU† method with k-Means clustering achieves an accuracy of 94.43%. The results show that the accuracy of the proposed quantum algorithms outperforms the existing classical methods and can be utilized to forecast output power generation daily by measuring the metrics required in energy sector decision-making situations. This will be useful to save energy and costs in an IoT-extreme environment, where energy organizations must decide instantly whether to start or stop generating units.


Specific Emitter Identification Using Adaptive Signal Feature Embedded Knowledge Graph

Abstract: Specific emitter identification (SEI) plays an important role in secure Industrial Internet of Things (IIoT). In recent years, many SEI methods based on machine learning (ML) and deep learning (DL) have been proposed due to their great performance. However, DL-based SEI methods are accompanied by huge computation overhead, which is not suitable for IIoT applications. In addition, the existing ML-based SEI methods rely on feature extraction and a heavy and redundant classifier, which do not ensure optimal feature combination and efficient computation. To solve the above problem, we propose an improved DL-based SEI method using signal feature embedded knowledge graph (KG) composed of universal features. To the best of our knowledge, this is the first attempt to apply KG for SEI technology. Specifically, we explore an adaptive feature combination (AFC) strategy through attention mechanism to realize an efficient SEI classifier. The simulation results show that the proposed KG-AFC algorithm outperforms existing SEI methods in identification performance and computation overhead. At the same time, under the optimal compression rate, the average accuracy of the proposed SEI algorithm is higher than 99.2% and can effectively reduce complexity. The code and the dataset can be downloaded from https://github.com/Lollipophua/KG-AFC.


UAV Base Station Network Transmission-Based Reverse Auction Mechanism for Digital Twin Utility Maximization

Abstract: Digital twin (DT) technology uses Internet of Things (IoT) devices to collect real-world data and build a virtual world in the DT cloud. However, many IoT devices collect data in harsh natural environments, and these data cannot be transmitted through fixed base stations. Thus, many DT services adopt dynamic data transmission methods, such as transmission through unmanned aerial vehicle base stations (UAV-BSs). However, UAV-BS approaches have many communication constraints, such as limitations on the transmission bandwidth, data throughput, and number of channels. In addition, when integrating a large amount of data submitted by IoT devices, DT service providers need a corresponding mechanism to select the most valuable device data, which can be described by a winner decision problem with the goal of maximizing utility. In this paper, we consider the problem of maximizing the utility of a DT model under UAV-BS network transmission, transform it into a mixed integer programming model with communication and computing constraints, and adopt a reverse auction mechanism to solve it. Specifically, we design an optimal reverse auction mechanism based on optimal allocation and Vickrey–Clarke–Groves (VCG) theory. Additionally, a reverse auction mechanism with polynomial execution time is designed based on monotonic allocation, network maximum flow and critical value theory. These two mechanisms are proven to satisfy individual rationality and truthfulness. Experimental results indicate the favorable performance of the designed mechanisms.


MG-GCN: Multi-Granularity Graph Convolutional Neural Network for Multi-Label Classification in Multi-Label Information System

Abstract: Recently, many researchers have applied Graph Convolutional Neural Networks (GCN) to multi-label learning tasks by establishing relations among labels. However, these researchers have only used GCN as an auxiliary module and have lacked exploration of object relations, which has limited the inference capability of GCN. Applying GCN to multi-label classification in information systems poses significant challenges to address because information systems are considered as a graph with only nodes and no edges. This article proposes a multi-granularity graph convolutional neural network (MG-GCN) based on object relations to enhance the versatility of GCN and demonstrate its reasoning ability. The MG-GCN efficiently solves complex multi-label classification problems in information systems. Firstly, based on the multi-granularity thinking, the attribute set is divided into several attribute subsets based on granularity. Secondly, a graph of differences representing the relationship between objects is established under each attribute subset. Finally, based on the attention mechanism and multiple difference matrices of objects, MG-GCN is proposed and applied to multi-label classification within information systems. The effectiveness and significance of performance of MG-GCN has been verified through ranking-based indicators and hypothesis tests, respectively.


A Cross-Level Interaction Network Based on Scale-Aware Augmentation for Camouflaged Object Detection

Abstract: Camouflaged object detection (COD), with the task of separating the camouflaged object from its color/texture similar background, has been widely used in the fields of medical diagnosis and military reconnaissance. However, the COD task is still a challenging problem due to two main difficulties: large scale-variation for different camouflaged objects, and extreme similarity between the camouflaged object and its background. To address these problems, a cross-level interaction network based on scale-aware augmentation (CINet) for the COD task is proposed. Specifically, a scale-aware augmentation module (SAM) is firstly designed to perceive the scales information of the camouflaged object by calculating an optimal receptive field. Furthermore, a cross-level interaction module (CLIM) is proposed to facilitate the interaction of scale information at all levels, and the context of the feature maps is enriched accordingly. Finally, with the purpose of fully utilizing these features, we design a dual-branch feature decoder (DFD) to strengthen the connection between the predictions at each level. Extensive experiments performed on four COD datasets, e.g., CHAMELEON, CAMO, COD10K, and NC4K, demonstrate the superiority of the proposed CINet compared with 21 existing state-of-the-art methods.


Analytics-driven complaint prioritisation via deep learning and multicriteria decision-making

Abstract: Complaint analysis is an essential business analytics application because complaints have a strong influence on customer satisfaction (CSAT). However, the process of categorising and prioritising complaints manually can be extremely time consuming for large companies. In this paper, we propose a framework for automatic complaint labelling and prioritisation using text analytics and operational research techniques. The labelling step of the training set is performed using a simple weighting approach from the multiple-criteria decision-making (MCDM) literature, while transformer-based deep learning (DL) techniques are used for text classification. We define two priority classes, namely, urgent complaints and other claims, and develop a system for automatic complaint categorisation. Our experimental results show that excellent predictive performance can be achieved with state-of-the-art text classification models. In particular, BETO, a bidirectional encoder representations from transformers (BERT) model trained on a large Spanish corpus, reaches an accuracy (ACCU) and area under the curve (AUC) of 92.1% and 0.9785, respectively. This positive result translates into a successful complaint prioritisation scheme, which improves CSAT and reduces the churn rate.


Causal maps in the analysis and unsupervised assessment of the development of expert knowledge: Quantification of the learning effects for knowledge management purposes

Abstract: This study proposes an application of cognitive maps in the representation of cognitive structures of the experts and assessment of their development/modification as a result of a (computer or expert system-assisted) learning process. It strives to identify information needed for the guidance of the process of creation and management of expert knowledge by formal modeling tools. Changes in experts’ cognitive structures are assumed to stem from individual and collaborative (group-level) learning. The novel approach to assessing the outcomes of learning reflected as changes in the cognitive structures of experts or groups of experts, modeled by cognitive maps, does not assume any correct or desired outcome of the learning process to be known in advance. Instead, it identifies and analyses the changes in (or robustness of) the constituents of the cognitive maps from different points of view and allows for quantifying and visualizing the actual effect of the learning. The proposed methodology can identify changes in cognitive diversity, causal structures in terms of causal relations and concepts, and the perceived importance of strategic issues over the learning period. It can also detect which cause–effect relationships have appeared/disappeared considering the pre-/post-mapping design. Thus it provides an exploratory account on the changes in the cognitive structures of the expert(s) as a result of learning. The applicability of the proposed methods is illustrated in the assessment of the learning outcomes of a group of 71 graduate students who participated in an eight-week business simulation task. The results of the empirical analysis confirm the viability of the proposed methodology and indicate that the students’ understanding of the utilized concepts and associated relationships in the decision-making process improved throughout the learning activity, ultimately showing that the course learning has considerably improved students’ perception and knowledge. Based on the results, it can be concluded that the proposed approach has the potential to be effective in assessing learning outcomes in a teaching-learning activities.


Cross-level collaborative context-aware framework for medical image segmentation

Abstract: Efficient and accurate medical image segmentation is necessary for pathological evaluation and disease diagnosis in clinical practice. In recent years, the U-shaped encoder–decoder structure has achieved good performance in various medical image segmentation tasks. It is a neural network model similar to the letter U, which can gradually reduce the spatial dimension of the feature map to capture high-level semantic information in the feature encoding stage, and can make up for the lack of target detail information in the feature decoding stage to decouple the final segmentation results. However, the U-shaped encoder–decoder structure still suffers from semantic asymmetry and global semantic dilution problems, which are exacerbated during decoding, due to the limitation of the framework's fixation. In this paper, we propose a cross-level collaborative context-aware framework (C3-Net) to address the aforementioned issues in medical image segmentation. The main contributions of this research include: (i) To address the inherent problems in the U-shaped structure, we propose the C3-Net to explore the differences between cross-level contextual information effectively; (ii) To achieve detailed information preservation and semantic information enhancement, we present a Channel Scale-aware Context Enhancement (CSCE) module to enhance low-level contextual features from the global and local scales of the channel dimension; (iii) A Spatial Pyramid Context Alignment (SPCA) module is designed to extract and align pyramid features, thus obtaining accurate global contextual features at the network's top; (iv) We propose a Cross-Level Collaborative Context Refinement (CCCR) module to enable efficient collaboration between cross-level contextual features to simultaneously achieve semantic alignment and global semantic enhancement. The outcomes of implementing the proposed method on three publicly available datasets (Synapse, ACDC, and GlaS) and one private dataset (VMICH) show that the proposed method outperforms all competing approaches on four datasets. Our C3-Net achieves new state-of-the-art performance on four image segmentation tasks with Dice scores of 85.26%, 92.10%, 87.20%, and 87.61%, respectively.


Single image deraining using scale constraint iterative update network

Abstract: Rain streaks, which can be viewed as a type of noise, have the potential to destroy the structural integrity of objects within an image, leading to a dramatic degradation in the performance of advanced vision algorithms. Therefore, it is important to develop an effective deraining algorithm. There are three challenges in the current deraining task: First, it is difficult to separate rain streaks from the image background because of the complexity of rain types. Second, most of the existing methods use encoder–decoder-based structures, but the models are designed to be too bulky and inflexible to accommodate complex rain streaks. Third, many models perform the same operation for all rain types, which can reduce the generalizability of the model. To address these challenges, we propose a scale constraint iterative update network which divides the network structure into feature extraction block, multi-scale constraint block, and iterative update block. In the feature extraction block, the deep mixed attention is used to extract complex rain streak features, and the mixed convolution block is used to extract the background details. The multi-scale constraint block aggregates contextual features across different scales, while the Global GateUnit captures long-range dependencies. We followed a coarse-to-fine pipeline. In the iterative update block, the feature maps are updated based on features from different scales, gradually refining the image. The final optimized result is obtained by performing learnable up-sampling. During the whole iteration process, the parameters are shared to reduce the complexity of the model while maintaining the performance. Additionally, We also designed a dynamic inference mechanism, which dynamically selects the number of iterations for different types of rain streaks, effectively enhancing the generalization of the model. We conducted extensive experiments on synthetic and real-world rain datasets, as well as a raindrop dataset. The results of our experiments demonstrate that our method outperforms existing deraining methods in terms of effectiveness and generalization ability.


A nonlinear African vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy

Abstract: As a new intelligent optimization algorithm, the African vultures optimization algorithm (AVOA) has been widely used in various fields today. However, when solving complex multimodal problems, the AVOA still has some shortcomings, such as low searching accuracy, deficiency on the search capability and tendency to fall into local optimum. In order to alleviate the main shortcomings of the AVOA, a nonlinear African vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy (HWEAVOA) is proposed. Firstly, the Henon chaotic mapping theory and elite population strategy are proposed to improve the randomness and diversity of the vulture's initial population; Furthermore, the nonlinear adaptive incremental inertial weight factor is introduced in the location update phase to rationally balance the exploration and exploitation abilities, and avoid individual falling into a local optimum; The reverse learning competition strategy is designed to expand the discovery fields for the optimal solution and strengthen the ability to jump out of the local optimal solution. HWEAVOA and other advanced comparison algorithms are used to solve classical and CEC2022 test functions. Compared with other algorithms, the convergence curves of the HWEAVOA drop faster and the line bodies are smoother. These experimental results show the proposed HWEAVOA is ranked first in all test functions, which is superior to the comparison algorithms in convergence speed, optimization ability, and solution stability. Meanwhile, HWEAVOA has reached the general level in the algorithm complexity, and its overall performance is competitive in the swarm intelligence algorithms.


Human–machine Teaming with Small Unmanned Aerial Systems in a MAPE-K Environment

Abstract: The Human Machine Teaming (HMT) paradigm focuses on supporting partnerships between humans and autonomous machines. HMT describes requirements for transparency, augmented cognition, and coordination that enable far richer partnerships than those found in typical human-on-the-loop and human-in-the-loop systems. Autonomous, self-adaptive systems in domains such as autonomous driving, robotics, and Cyber-Physical Systems, are often implemented using the MAPE-K feedback loop as the primary reference model. However, while MAPE-K enables fully autonomous behavior, it does not explicitly address the interactions that occur between humans and autonomous machines as intended by HMT. In this article, we, therefore, present the MAPE-K HMT framework, which utilizes runtime models to augment the monitoring, analysis, planning, and execution phases of the MAPE-K loop to support HMT despite the different operational cadences of humans and machines. We draw on examples from our own emergency response system of interactive, autonomous, small unmanned aerial systems to illustrate the application of MAPE-K HMT in both a simulated and physical environment, and we discuss how the various HMT models are connected and can be integrated into a MAPE-K solution.


Fractional-Order Echo State Network Backstepping Control of Fractional-Order Nonlinear Systems

Abstract: Classical backstepping control of fractional-order nonlinear systems (FONSs) needs to calculate fractional derivatives of virtual control inputs recursively, which usually results in the “explosion of complexity” problem. This article proposes a command-filtering adaptive control scheme based on fractional-order echo state networks (FOESNs) for strict-feedback uncertain FONSs. Finite-time command filters are introduced to estimate instead of calculating the complicated fractional-order derivatives of virtual control inputs, which not only has the advantages of the conventional command-filters, but also guarantees the finite-time convergent property. An FOESN with stronger approximation ability is presented to approach uncertainties. Compared with conventional adaptive neural network or fuzzy control, the proposed method with similar computational complexity has more design freedom and better approximation effect. The effectiveness of the proposed control framework is verified by two simulation examples.


Updatable Siamese tracker with two-stage one-shot learning

Abstract: Offline-trained Siamese networks have realized very promising tracking precision and efficiency. However, the performance is still limited by the drawbacks in online update. Traditional strategies cannot tackle the irregular variations of object and the sampling noise, so it is quite risky to adopt them to update Siamese trackers. In this paper, we present a two-stage one-shot learner by exploring the learning scheme of Siamese network, which reveals there are two key issues during online update, i.e., feature fusion and feature comparison. Based on this finding, we propose an updatable Siamese tracker by introducing two independent transformers (SiamTOL). Concretely, a Cross-aware transformer is designed to combine the features of the initial and the dynamic templates, while a Decoder-favored transformer is exploited to compare the fusing template and the search region. By combining these transformers, our tracker is able to adequately model the feature dependencies between multi-frame object samples. Extensive experimental results on several popular benchmarks well manifest that the proposed approach achieves the leading performance, and outperforms other state-of-the-art trackers.


Overview of modulation techniques for spatially structured-light 3D imaging

Abstract: The modulation and projection of patterns are the cornerstones of spatially structured-light three-dimensional (3D) imaging. However, an overview of this field is lacking. This paper comprehensively reviews the modulation techniques for spatially structured-light 3D imaging. First, the frameworks and the state-of-the-art status of all the mainstream methods are introduced. Then, side-by-side comparisons are performed from different perspectives to illustrate their respective characteristics. In particular, experiments to compare the shape reconstruction quality of different methods were conducted. Beyond academic research, the applications and off-the-shelf products in the industry are reviewed. Finally, our views on the demands and trends in the field are presented.


Product consumptions meet reviews: Inferring consumer preferences by an explainable machine learning approach

Abstract: Inferring consumers' preferences provides a better understanding of their purchase behavior, which is very important for business success, e.g., recommendation systems and targeted advertising. In this paper, we propose an explainable machine learning approach, namely Multi-view Latent Dirichlet Allocation (MVLDA), to infer and interpret consumer preferences. In the proposed model, we assume that there exists a downstream relationship between consumers' motivations and their purchase behaviors. We model this downstream relationship by linking two types of topics (i.e., textual topics for motivations and product-related topics for purchase behaviors), to quantify and explain consumers' choices. We validate our modeling framework using a real-world dataset collected from the online retailer Amazon. The experimental results show that the proposed model identifies a set of high-quality textual topics but also interprets its effect on consumer choices based on product-related topics. In addition, we demonstrate that the proposed model quantifies the consumer's preferences. The proposed model yields interesting insights about user preferences, and provides several important managerial implications, e.g., e-commerce platforms, brand managers, and marketers.


MMDS: A secure and verifiable multimedia data search scheme for cloud-assisted edge computing

Abstract: With the rapid development and popularity of Internet of Things (IoT) technology, IoT devices can generate MultiMedia Big Data (MMBD) as multimedia devices. MMBD is often encrypted and stored on an untrusted cloud server. Searchable encryption is an effective way which can finish a controllable keyword search of ciphertext. However, there are two limitations to these existing works. On the one hand, it is hard to independently protect the data privacy of each IoT device. On the other hand, when faced with an untrusted cloud server, it is difficult to implement more flexible fine-grained verification for search results. To overcome these limitations, we propose a secure and verifiable MultiMedia Data Search (MMDS) scheme for cloud-assisted edge computing. To protect the data of IoT devices independently, we designed a secure, flexible, and efficient keyword search mechanism based on bilinear pairings. To achieve a more flexible and practical search result verification mechanism, we design a fine-grained verification algorithm combining blockchain and hashing techniques. We have conducted performance evaluations and security proof and analysis for MMDS scheme. Finally, we applied MMDS scheme to solve real problems in intelligent multimedia systems. Security proof and analysis prove the security of MMDS. Performance analyses and evaluations further confirm that MMDS is efficient and feasible for practical applications.


Self-distillation and self-supervision for partial label learning

Abstract: As a main branch of weakly supervised learning paradigm, partial label learning (PLL) copes with the situation where each sample corresponds to ambiguous candidate labels containing the unknown true label. The primary difficulty of PLL lies in label ambiguities, most existing researches focus on individual instance knowledge while ignore the importance of cross-sample knowledge. To circumvent this difficulty, an innovative multi-task framework is proposed in this work to integrate self-supervision and self-distillation to tackle PLL problem. Specifically, in the self-distillation task, cross-sample knowledge in the same batch is utilized to refine ensembled soft targets to supervise the distillation operation without using multiple networks. The auxiliary self-supervised task of recognizing rotation transformations of images provides more supervisory signal for feature learning. Overall, training supervision is constructed not only from the input data itself but also from other instances within the same batch. Empirical results on benchmark datasets reveal that this method is effective in learning from partially labeled data.


A hybrid framework using explainable AI (XAI) in cyber-risk management for defence and recovery against phishing attacks

Abstract: Phishing and social engineering contribute to various cyber incidents such as data breaches and ransomware attacks, financial frauds, and denial of service attacks. Often, phishers discuss these attack vectors in dark forums. Further, the probability of phishing attacks and the subsequent loss suffered by the firm are highly correlated. In this context, we propose a hybrid framework using explainable AI techniques to assess cyber-risks generated from correlated phishing attacks. The first phase computes the probability of expert phishers within a community of similar attackers with varying expertise. The second phase calculates the probability of phishing attacks upon a firm even after it has invested in IT security and adopted regulatory steps. The third phase categorises phishing and genuine URLs using various machine-learning-based classifiers. Next, it estimates the joint distribution of phishing attacks using an exponential-beta distribution and quantifies the expected loss using Archimedean Copula. Finally, we offer recommendations for firms through the computation of optimal investments in cyber-insurance versus IT security. First, based on the risk attitude of a firm, it can use this explainable-AI (XAI) framework to optimally invest in building security into its enterprise architecture and plan for cyber-risk mitigation strategies. Second, we identify a long-tail phenomenon demonstrated by the losses suffered during most cyber-attacks, which are not one-off incidents and are correlated. Third, contrary to the belief that cyber-insurance markets are ineffective, it can guide financial firms to design realistic cyber-insurance products.


Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels

Abstract: Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized private datasets where the labeling effort is entrusted to the clients. While most existing FL approaches assume high-quality labels are readily available on users’ devices, in reality, label noise can naturally occur in FL and is closely related to clients’ characteristics. Due to scarcity of available data and significant label noise variations among clients in FL, existing state-of-the-art centralized approaches exhibit unsatisfactory performance, whereas prior FL studies rely on excessive on-device computational schemes or additional clean data available on the server. We propose FedLN , a framework to deal with label noise across different FL training stages, namely FL initialization, on-device model training, and server model aggregation, able to accommodate the diverse computational capabilities of devices in an FL system. Specifically, FedLN computes per-client noise level estimation in a single federated round and improves the models’ performance by either correcting or mitigating the effect of noisy samples. Our evaluation on various publicly available vision and audio datasets demonstrates a 22% improvement on average compared to other existing methods for a label noise level of 60%. We further validate the efficiency of FedLN in human-annotated real-world noisy datasets and report a 4.8% increase on average in models’ recognition performance, highlighting that FedLN can be useful for improving FL services provided to everyday users.


An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments

Abstract: As a fundamental component of autonomous driving systems, motion planning has garnered significant attention from both academia and industry. This paper focuses on efficient and spatial-temporal optimal trajectory optimization in unstructured environments using compact convex approximations of vehicle shapes. Conventional approaches typically model the task as an optimal control problem by discretizing the motion process in state configuration space. However, this often results in a tradeoff between optimality and efficiency since generating high-quality motion trajectories often requires high-precision discretization of the dynamic process, which imposes a substantial computational burden. To address this issue, we leverage the differential flatness property of car-like robots to simplify the trajectory representation and analytically formulate the spatial-temporal joint optimization problem with flat outputs in a compact manner, while ensuring the feasibility of nonholonomic dynamics. Moreover, we achieve efficient obstacle avoidance with a collision-free driving corridor for unmodelled obstacles and signed distance approximations for dynamic moving objects. We present comprehensive benchmarks with State-of-the-Art methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes for the research community.


Anomaly detection in telemetry data using a jointly optimal one-class support vector machine with dictionary learning

Abstract: Anomaly detection based on telemetry data is a major issue in satellite health monitoring, given that it can identify unusual or unexpected events to avoid serious accidents and ensure the safety and reliability of operations. This study proposes a jointly optimal one-class support vector machine with dictionary learning (JODL-OC) framework for multivariate anomaly detection in telemetry. Dictionary learning (DL) extracts the correlation and local dynamics of time series into a sparse matrix, and the one-class support vector machine (OCSVM) detects anomalies in the transformed sparse space. The optimisation objective of DL is modified by introducing a novel regularisation term called soft-boundary OCSVM, so that the JODL-OC seamlessly integrates DL, sparse representation, and classifier training into a unified model to jointly learn the features and decision boundary. Furthermore, we propose an iterative approach to solve the optimisation function and obtain the optimal JODL-OC framework for anomaly detection. A case analysis based on satellite antenna telemetry data reveals that the proposed method improves the detection precision, F1-score, FPR and FAR compared to other existing anomaly detection methods, particularly to illustrate the superior performance in correlation anomalies.


A deep model towards accurate boundary location and strong generalization for medical image segmentation

Abstract: Accurate medical image segmentation plays a crucial role in computer-assisted diagnosis and monitoring. However, due to the complexity of medical images and the limitations of image acquisition, most of the current segmentation models still face the challenges of imprecise boundary positioning and poor generalization ability, which greatly affect their application in clinical practices. To address these issues, we propose a new deep learning-based medical image semantic segmentation network (BLG-Net). To improve the accuracy of object boundary location, a Local–global module (Logo) is proposed. It uses the combination of a self-attention mechanism and a cross-attention mechanism to model intra-sample and inter-sample long-range dependencies from local and global perspectives, respectively, to help recover the spatial structures. To further enhance the generalization ability of the BLG-Net, we develop a generalization enhancement module (GEM), which can adaptively adjust the classifier weights of the network according to the features of the test images extracted during the test process to alleviate the distribution shift issues. Besides, we design a multi-granularity loss (MGL) function, which can guide the network to learn the object region and its boundary details at the pixel, patch, and map levels, respectively. Extensive experiments on five medical image segmentation tasks demonstrate the improvements of our method. We further verify the model’s generalization ability to unseen data on two other popular datasets with different modalities.


A new approach of contrast enhancement for Medical Images based on entropy curve

Abstract: X-ray, MRI, and CT scan images etc, are widely used for the detection of various prevalent diseases. The efficient identification of the disease depends very much on the quality of the image. Sometimes due to environmental conditions such as insufficient and nonuniform illumination, shacking and movement of objects or the imaging device, and some other fault in the device, the resultant image may have low contrast and the presence of noise or artifacts. In such situation, a contrast enhancement algorithm that can efficiently improve the contrast without increasing noise and artifacts can play an important role. In this article, we propose a simple yet efficient method for these types of images by using the image’s entropy curve and homomorphic filtering. It enhances the contrast of medical images without increasing the content of noise and artifacts. Also, the performance of the proposed method is evaluated by a complete qualitative and quantitative analysis.


Efficient Long-Short Temporal Attention network for unsupervised Video Object Segmentation

Abstract: Unsupervised Video Object Segmentation (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge. However, previous methods do not fully use spatial–temporal context and fail to tackle this challenging task in real-time. This motivates us to develop an efficient Long-Short Temporal Attention network (termed LSTA) for unsupervised VOS task from a holistic view. Specifically, LSTA consists of two dominant modules, i.e., Long Temporal Memory and Short Temporal Attention. The former captures the long-term global pixel relations of the past frames and the current frame, which models constantly present objects by encoding appearance pattern. Meanwhile, the latter reveals the short-term local pixel relations of one nearby frame and the current frame, which models moving objects by encoding motion pattern. To speedup the inference, the efficient projection and the locality-based sliding window are adopted to achieve nearly linear time complexity for the two light modules, respectively. Extensive empirical studies on several benchmarks have demonstrated promising performances of the proposed method with high efficiency.


NeatIBP 1.0, a package generating small-size integration-by-parts relations for Feynman integrals

Abstract: In this work, we present the package NeatIBP, which automatically generates small-size integration-by-parts (IBP) identities for Feynman integrals. Based on the syzygy and module intersection techniques, the generated IBP identities' propagator degree is controlled and thus the size of the system of IBP identities is shorter than that generated by the standard Laporta algorithm. This package is powered by the computer algebra systems Mathematica and Singular, and the library SpaSM. It is parallelized on the level of Feynman integral sectors. The generated small-size IBP identities can subsequently be used for either finite field reduction or analytic reduction. We demonstrate the capabilities of this package on several multi-loop IBP examples. Program Title: NeatIBP CPC Library link to program files: https://doi.org/10.17632/ms85fpfm7b.1 Developer's repository link: https://github.com/yzhphy/NeatIBP Licensing provisions: GPLv3 Programming language: Mathematica, Singular, C Nature of problem: The difficulty of the IBP reduction of Feynman integrals stems from the large size of the IBP system. Solution method: We apply the module intersection method to generate IBP relations by algebraic geometry methods, and compare the smaller IBP system we generate with that from Laporta's algorithm. NeatIBP generates the module intersection in Singular [1] and selects relevant and independent IBP relations via an algorithm relying on the library SpaSM [2]. The workflow is parallelized by a task manager written in Mathematica [3]. W. Decker, G.-M. Greuel, G. Pfister, and H. Schönemann, Singular 4-3-1 – A computer algebra system for polynomial computations, http://www.singular.uni-kl.de, 2022. The SpaSM group, SpaSM: a Sparse direct Solver Modulo p, http://github.com/cbouilla/spasm, 2017. Wolfram Research, Mathematica 13.2, https://www.wolfram.com/mathematica/.


Light-field 3D image parallel encryption based on the state transition diagram of Maximum Length Cellular Automata

Abstract: In our work, we proposed a parallel encryption scheme based on the state transition diagram of Maximum Length Cellular Automata (MLCA) to protect 3D images. Compared with the previous random mode decomposition (RMD) encryption scheme and DNA sequences and chaotic systems scheme, our scheme effectively reduces the time consumption, especially for the RME scheme, by up to 5 times. Meanwhile, leveraging MLCA's abundant rules, high parallelism, and straightforward hardware implementation, our encryption scheme exhibits flexible key selection, parallel processing capabilities, and easy hardware implementation. The results of key space analysis, differential attack analysis, and statistical analysis verify the high-security level achieved by our proposed parallel encryption scheme.