Collective Inference Research Articles

Pelaksanaan Quiz Beserta Umpan Balik Menggunakan Aplikasi Kahoot dalam Pembelajaran Seni Tari di SMA Negeri 4 Kerinci

This study aims to determine the implementation of Dance Arts learning activities as well as supporting and inhibiting factors in the implementation of Dance Arts learning activities by using the Kahoot Application to increase the learning motivation of Class XI MIPA students at SMA Negeri 4 Kerinci. This type of research is qualitative research with a descriptive method. The research instrument is the researcher himself and is assisted by supporting instruments such as stationery and cameras. Data were collected through literature studies, observations, interviews and documentation. The steps to analyze data are data collection, data reduction, data display and data inference. The results of the study show that the use of Kahoot application media in Dance Arts learning is aimed at increasing students' learning motivation. There is an additional value so that this motivates students to learn so that they can play the Kahoot application well. In addition, this game can also help students to strengthen their memory of the material that has been delivered by the teacher. By using the Kahoot App, students can actively participate in answering questions. The Kahoot app motivates and activates student learning as it can test their knowledge, repeat important concepts, create class discussions and student-to-student interactions. The supporting factor for the use of Kahoot application media in learning Dance Arts is the availability of adequate facilities and infrastructure such as Wi-Fi, lcd projectors, and so on. Then there is also the enthusiasm of students when playing games on the Kahoot application so that it gives a positive response and can facilitate the learning process. Meanwhile, the inhibiting factor for the use of Kahoot application media in Dance Arts learning is a poor signal connection that causes learning to be hampered.

Fuzzy rule based classifier model for evidence based clinical decision support systems

Clinicians benefit from the use of artificial intelligence and machine learning techniques applied to health data within health records, which identify commonalities between them. It enables them to get evidence-based support in recommending shared treatment paths for undiagnosed health records. The collective inference from these patterns, drawn from an array of health records, further enhances the capacity to mine essential features, supporting public health experts in their management of population health conditions. This paper presents a novel mapping tool model designed to analyze electronic health records and provide healthcare providers with evidence-based decision support. The work focuses on the analysis of health records from hospital databases, encompassing parameters extracted from routine health checkups. By scrutinizing patterns within examined health records, healthcare providers can seamlessly align with newer health records for diagnosis and treatment recommendations. Core to this approach is the integration of a fuzzy rule-based classifier system within the proposed system. This incorporation facilitates the processing of health records, extracting pertinent features to augment decision-making with the support of knowledge bases. The model architecture provides flexibility and customizability, enabling easy configuration of the system to accurately map new health records to the examined dataset. Additionally, the model utilizes a specially developed distance-measure technique tailored for the proposed fuzzy-based system. Results showcase satisfying performance and robust discriminant capability for accurate recommendations. The alignment of outcomes with expert evaluations underscores the model's efficacy and attainment of benchmarks.

Representasi Prosesi Pernikahan Dalam Puisi W.S. Rendra Berjudul "Undangan", "Nyanyian Para Malaikat", Dan "Kakawin Kawin".

marriage is the process of binding sacred promises between men and women. Marriage is a sacred event through which several steps pass. The steps in the wedding are referred to as the wedding procession. Human feelings can be represented through verses of poetry, stories in novels, lyrics in songs, and conversations in films. Literary works are able to express all human problems and feelings. The type of research used is qualitative descriptive research using the method of Comparative Literature. The data source used is a collection of poems by W.S Rendra includes poems 'invitation’,' Song Of The Angels’, and 'Kakawin Kawin' in the book four collections of poems. These three works were chosen because they have a connection to the wedding procession. Research techniques used are data collection, data analysis, and data inference. Based on the results of the study it can be concluded that there is a wedding procession represented through the poem ’invitation’,’Song Of The Angels’, and ’Kakawin Kawin’. In the poem 'invitation ’and’ Song Of The Angels', both set in a church, and are going through the process of marriage. Meanwhile, the poem 'Kakawin Kawin' is set in the House of the late bride. The difference lies in different situations but each poem represents the wedding procession differently.

Machine-Learning-Assisted Descriptors Identification for Indoor Formaldehyde Oxidation Catalysts.

The development of highly efficient catalysts for formaldehyde (HCHO) oxidation is of significant interest for the improvement of indoor air quality. Up to 400 works relating to the catalytic oxidation of HCHO have been published to date; however, their analysis for collective inference through conventional literature search is still a challenging task. A machine learning (ML) framework was presented to predict catalyst performance from experimental descriptors based on an HCHO oxidation catalysts database. MnOx, CeO2, Co3O4, TiO2, FeOx, ZrO2, Al2O3, SiO2, and carbon-based catalysts with different promoters were compiled from the literature. Notably, 20 descriptors including reaction catalyst composition, reaction conditions, and catalyst physical properties were collected for data mining (2263 data points). Furthermore, the eXtreme Gradient Boosting algorithm was employed, which successfully predicted the conversion efficiency of HCHO with an R-square value of 0.81. Shapley additive analysis suggested Pt/MnO2 and Ag/Ce-Co3O4 exhibited excellent catalytic performance of HCHO oxidation based on the analysis of the entire database. Validated by experimental tests and theoretical simulations, the key descriptor identified by ML, i.e., the first promoter, was further described as metal-support interactions. This study highlights ML as a useful tool for database establishment and the catalyst rational design strategy based on the importance of analysis between experimental descriptors and the performance of complex catalytic systems.

Collective relational inference for learning heterogeneous interactions

Interacting systems are ubiquitous in nature and engineering, ranging from particle dynamics in physics to functionally connected brain regions. Revealing interaction laws is of fundamental importance but also particularly challenging due to underlying configurational complexities. These challenges become exacerbated for heterogeneous systems that are prevalent in reality, where multiple interaction types coexist simultaneously and relational inference is required. Here, we propose a probabilistic method for relational inference, which possesses two distinctive characteristics compared to existing methods. First, it infers the interaction types of different edges collectively by explicitly encoding the correlation among incoming interactions with a joint distribution, and second, it allows handling systems with variable topological structure over time. We evaluate the proposed methodology across several benchmark datasets and demonstrate that it outperforms existing methods in accurately inferring interaction types. The developed methodology constitutes a key element for understanding interacting systems and may find application in graph structure learning.

KOLEK AS A TRADITIONAL BOAT OF NATUNA PEOPLE


 
 
 
 Natuna society in the maritime region has a close relationship with traditional technology in the form of kolek boats. As a material object, kolek is considered an important part of fishing activities since ancient times. The description of kolek is currently still lacking and even very minimally known. This article aims to describe kolek as a traditional technology of Natuna society. Likewise, some traditional boats in Natuna. The formal object of this research is cultural anthropology, while the material object is kolek boats. This descriptive qualitative research uses data collection techniques of observation, direct interview, and literature study. There are three stages of data analysis, namely data collection, data interpretation, and data inference. The traditional technology of kolek boats is not only a means of fishing at sea, but many cultural values. Kolek also holds traditional knowledge that must be preserved.
 
 
 

An inferential analysis of stainless steel in additive manufacturing using bibliometric indicators

In this work, we analyse previous research in the subject, spanning an extensive range of publications, to give an exhaustive bibliometric analysis of the literature concerned with the influence of stainless steel on additive manufacturing (AM). A bibliometric study hasn't been done by many academics, despite the fact that AM is one of the most significant industrial revolutions. Through the investigation of the metadata of all publications included in the bibliographical database Web of Science, this article seeks to examine the scientific output on AM of stainless steel based on 376 publications from 2010 to 2023. This study's five-stage process, which includes research study design, data collection, data analysis, data visualization, and data inference, identifies the most relevant sources, authors, and nations active and influential via the use of bibliometric indicators. To highlight research streams, co-citation of authors and co-occurrence analysis on all keywords were carried out. The associated rising research areas were discovered by keyword analysis. Using VOSviewer software, bibliographic data was graphically analysed. According to this analysis, the journals “International Journal of Advanced Manufacturing Technology“, “Additive Manufacturing”, “Materials“, “Materials Science and Engineering A-Structural Materials Properties”, “Rapid Prototyping Journal,“ and “Materials & Design” are the most effective and significant in AM of stainless-steel research. Also, linear regression analysis was used to estimate the number of publications in the upcoming years. The findings and recommendations of this study may be used as forecasts for future research in the field of AM in stainless steel.

Implementation of Strategic Management towards Competitive School Development (Multisite Study at Smp Negeri 1 and Smp Negeri 6 Banjarmasin)

This study aims to describe how the implementation of strategic management towards competitive schools. This research uses a case research approach and is also called descriptive research. Data collection by observation, interviews and documentation. Data analysis uses data analysis, data collection, data condensation, triangulation, data presentation and data inference or verification. The results obtained that a strategic environmental analysis was carried out at SMP Negeri 1 and SMP Negeri 6 Banjarmasin to identify the strengths and weaknesses possessed as well as the opportunities and challenges faced by the school. The strategic formulation in SMP Negeri 1 and SMP Negeri 6 Banjarmasin begins by clarifying the vision and mission to be achieved and determining long and short term goals. The School Development Team will formulate a strategy by identifying Human Resources, funds, and available facilities and infrastructure, then designing a strategy. The principal's policy supports strategic formulation. Strategically implemented on the program with clear procedural and financial support. Strategic evaluation and control are carried out by assessing the overall strategy that has been implemented in the program. The strategic plan made becomes a benchmark for success. School Development Team can make decisions based on the results of strategic evaluation and control. The conclusion of this study shows that strategic implementation is carried out in SMP Negeri 1 and SMP Negeri 6 Banjarmasin in the development of eight National Education Standards. Education units are expected to maintain existing strategic management and improve strategies that are more innovative, emphasize the quality of education, and are oriented towards fund efficiency.

Human Action Recognition Using Key-Frame Attention-Based LSTM Networks

Human action recognition is a classical problem in computer vision and machine learning, and the task of effectively and efficiently recognising human actions is a concern for researchers. In this paper, we propose a key-frame-based approach to human action recognition. First, we designed a key-frame attention-based LSTM network (KF-LSTM) using the attention mechanism, which can be combined with LSTM to effectively recognise human action sequences by assigning different weight scale values to give more attention to key frames. In addition, we designed a new key-frame extraction method by combining an automatic segmentation model based on the autoregressive moving average (ARMA) algorithm and the K-means clustering algorithm. This method effectively avoids the possibility of inter-frame confusion in the temporal sequence of key frames of different actions and ensures that the subsequent human action recognition task proceeds smoothly. The dataset used in the experiments was acquired with an IMU sensor-based motion capture device, and we separately extracted the motion features of each joint using a manual method and then performed collective inference.

Compositional constraints and selection forces dictate codon usage in human bocavirus

ObjectivesHuman bocavirus (HBoV), of the genus Bocaparvovirus, is a small, non-enveloped linear single-stranded DNA virus of the Parvoviridae family. The virus is known to cause severe life-threatening respiratory tract infections in pediatric patients. Considering its deleterious impacts on respiratory, gastrointestinal, and hematological health, we prompted to investigate codon usage patterns, parity, neutrality, Nc-GC3 analysis, gene expression, Intrinsic codon bias index (ICDI), Codon bias index (CBI), Relative codon deoptimization index (RCDI), Translational selection (P2), and measure independent of length and composition (MILC) to investigate the role of evolutionary constraints such as selection and mutation and molecular patterns present in HBoV. Methods805 HBoV sequences were investigated using various software and statistical tools. ResultsThe present study demonstrated the predominant governance of selection forces over mutational forces in determining codon usage. The compositional parameters were pivotal in deciding the dinucleotide occurrence and frequently used codons. Since the HBoV genomes were A-and T-nucleotide-rich, A/T ending codons and ApAdinucleotides were overrepresented. Despite the A/T richness, at the non-neutral positions of the codon, the G/C nucleotide content was found to be the highest, again underscoring the selective forces that drive a high percentage of G/C nucleotide. At the non-neutral codon positions, with an increase in GC nucleotide, codon bias also increased, while at the third position of the codon, with an increase in GC content, it was decreased. Overall, there is a low codon bias in HBoV. A total of nine A/T ending codons were overrepresented, while nineteen G/C ending codons were underrepresented. Interestingly, instead of CTG, a commonly overrepresented codon, AGA was the most overrepresented codon. AGA and CGG encoding for arginine showed average maximum and minimum RSCU values in the HBoV genome. ConclusionsThe collective inference from the neutrality plot, Nc-GC3 curve, and P2 analysis indicated the prevalence of selection force over mutation force. With that, our study offers a novel perspective on the different molecular patterns present in HBoV, and the results might be implicated in designing efficacious therapeutic modalities against HBoV.

Comparison Analysis of EVA and ROA Methods in Assessing The Financial Performance of The Company

This study aims to determine the company's financial performance by using a comparative analysis of the Economic Value Added (EVA) and Return On Assets (ROA) methods.
 This study employed pre-field research techniques and on-site fieldwork to conduct descriptive qualitative research. Secondary data, data gathering methods utilizing report studies and literature analyses, and data analysis methods utilizing data collection, data reduction, data presentation, and data inference techniques are all used as the data sources. To verify the accuracy of the data, the author uses three sources and three different techniques. 
 The results showed that EVA INDF in the 2014-2016 period produced an EVA value < 0, which could be interpreted as unfavorable, ICBP, MYOR, and ROTI in the 2014-2018 period, on average, produced an EVA value > 0 which could be construed as positive. Except for ROTI in the last three years which resulted in an EVA < 0. Meanwhile, ROA INDF, ICBP, MYOR, and ROTI in 2014-2018 produced an average ROA value above the industry standard or can be said to be good. Except for INDF in 2015, 2017, and 2018, the average ROA value is below industry standards.
 This study concludes that EVA analysis is better because if the average value of EVA is> 0, then the company can provide added value to investors. At the same time, the ROA analysis produces an average ROA value below the industry standard.

Impact of The Existence of A Coffee Store on Family Economic Education and Interest in Entrepreneurship(Case Study in Sudimoro Malang Region )

The number of coffee shops in the Sudimoro area makes the Sudimoro area one of the centers for coffee shops in the city of Malang. Based on initial observations, it is known that the existence of a coffee shop in the Sudimoro area has an influence on the family's economic education and interest in entrepreneurship in the community around Sudimoro. This research uses a qualitative approach with the type of case study research. Data collection techniques in this study using observation and interviews. Techniques in data analysis are data collection, data reduction, data presentation, and data inference. The results of the study of the impact of the existence of coffee shops in the Sudimoro area of Malang on family economic education, namely parents familiarize children with saving, teach productive behavior, see future opportunities, be open about family financial conditions and involve family members in family financial planning, and get used to having In addition, the existence of a coffee shop in the Sudimoro Malang area has an impact on interest in entrepreneurship, namely families in the Sudimoro Malang area know knowledge about entrepreneurship, have an interest in entrepreneurship, have the intention and motivation in entrepreneurship.

Construction of Landscape Ecological Planning Evaluation Model Based on Sensor Network

The application of wireless sensor network (WSN) technology promotes the modernization of forestry. WSN application technology in forest areas is an important research topic for the sustainable development of forestry in China and is also a research hotspot for forestry ecological monitoring at present. The application of wireless sensor networks in forest areas, first of all, solves the problem of limited energy supply and low-latency data transmission in the forest environment. Due to the large area of the forest environment and uneven tree density, dynamic changes in forest height, easy to block the signal, and other characteristics, the forest environment is prone to node energy depletion fast, the network life cycle is short, and data transmission delays large dilemma. Second, the application of wireless sensor networks is usually centered on maximum data acquisition, but the contradiction between high data acquisition rate and limited energy supply is inevitable, so it is necessary to construct a maximum data acquisition rate model with limited energy supply as a constraint to guarantee the optimal acquisition conditions for wireless sensor networks. In this paper, from the application of wireless sensor network technology in forest environment, the research on the application of wireless sensor network in forestry is carried out around the analysis of energy self-collection permanent function of sensor nodes, sensor node transmission routing strategy, data collection, fusion, and fuzzy inference decision-making fire danger warning process, so as to provide a solution to the overall problem of forest fire warning based on rechargeable wireless sensor network. In this paper, we analyze the dynamic replenishment of energy in rechargeable wireless sensor networks and propose an energy-based transmission control protocol that effectively improves data transmission efficiency. In the rechargeable wireless sensor network, the network E2E (end-to-end) average delay time is calculated based on the number of nodes on the data transmission link. The research idea of this paper starts from the application technology of wireless sensor network in the forest environment, and the design of the energy self-collection permanent function of sensor nodes, sensor node transmission routing strategy, data collection, fusion, and fuzzy inference decision fire danger warning process are realized vertically.

DZIKIR RATIB AL-HADDAD AS AN EFFORT TO STRENGTHEN RELIGIOUS CHARACTER EDUCATION

This research was conducted because people are increasingly losing their character and identity as an excellent Indonesian nation, and the low public awareness of religious activities. For this reason, this study aims to analyze and examine the remembrance of Ratib Al-Haddad to strengthen religious character education. The approach used in this research is a descriptive qualitative type of case study. Data collection uses observation and in-depth interviews and is supported by documentation. The research location is in Kaling Village, Tasikmadu District, Karanganyar Regency. Data analysis techniques used are data collection, data reduction, data presentation, and data inference. The results showed that efforts to strengthen religious character education in Kaling Village provided the community an understanding of religious character through remembrance of Ratib Al-Haddad activities led by Kyai. Strengthening character education through remembering Ratib Al-Haddad includes moral knowing, feeling, and doing. This research implies that all parties need support to achieve a common goal, namely creating a harmonious religious character.

Collective Variational Inference for Personalized and Generative Physiological Modeling: A Case Study on Hemorrhage Resuscitation.

Individual physiological experiments typically provide useful but incomplete information about a studied physiological process. As a result, inferring the unknown parameters of a physiological model from experimental data is often challenging. The objective of this paper is to propose and illustrate the efficacy of a collective variational inference (C-VI) method, intended to reconcile low-information and heterogeneous data from a collection of experiments to produce robust personalized and generative physiological models. To derive the C-VI method, we utilize a probabilistic graphical model to impose structure on the available physiological data, and algorithmically characterize the graphical model using variational Bayesian inference techniques. To illustrate the efficacy of the C-VI method, we apply it to a case study on the mathematical modeling of hemorrhage resuscitation. In the context of hemorrhage resuscitation modeling, the C-VI method could reconcile heterogeneous combinations of hematocrit, cardiac output, and blood pressure data across multiple experiments to obtain (i) robust personalized models along with associated measures of uncertainty and signal quality, and (ii) a generative model capable of reproducing the physiological behavior of the population. The C-VI method facilitates the personalized and generative modeling of physiological processes in the presence of low-information and heterogeneous data. The resulting models provide a solid basis for the development and testing of interpretable physiological monitoring, decision-support, and closed-loop control algorithms.

Online Spatial Crowdsensing With Expertise-Aware Truth Inference and Task Allocation

Emerging crowdsensing paradigm enables a large number of sensing applications, where much attention is drawn to the fundamental problems of data collection and truth inference. Existing works have devised manifold techniques to discover truth from collected noisy data, but they frequently ignore various expertise of workers and dynamic information of crowdsensing system, thus leading to error-prone estimated truth and unqualified sensing data. In this paper, we design an online location-aware crowdsensing system to accurately estimate truth and efficiently assign tasks. Specifically, we unify diverse types of numerical and categorical tasks based on probabilistic graphical model, and then propose unsupervised learning methods which can dynamically infer ground truth and various worker expertise at the same time. Furthermore, we develop online task allocation schemes to gradually gather high quality data considering location awareness and inferred worker expertise. In particular, we convert the complicated task allocation into the additive form of probability improvement and entropy reduction, thereby solving the allocation problem via linearly selecting worker-task pair with low computation complexity. We finally carry out extensive evaluations using two datasets collected by our smartphones, where results demonstrate the superiority of our algorithms over the state-of-the-art approaches.

Ethics in Psychiatric Research.

Ethics in Psychiatric Research.

Hemodynamic Monitoring via Model-Based Extended Kalman Filtering: Hemorrhage Resuscitation and Sedation Case Study

This letter investigates the potential of model-based extended Kalman filtering (EKF) for hemodynamic monitoring in a hemorrhage resuscitation-sedation case study. To the best of our knowledge, it may be the first model-based state estimation study conducted in the context of hemodynamic monitoring. Built upon a grey-box mathematical model with parametric uncertainty as process noise, the EKF can estimate cardiac output (CO) and total peripheral resistance (TPR) continuously from mean arterial pressure (AP) measurements against inter-individual physiological and pharmacological variability. Its unique practical strengths include: it does not require AP waveform as in existing AP-based pulse-contour CO (PCCO) monitors; and it can estimate CO and TPR with explicit account for the effect of sedative drugs. The efficacy of the EKF-based hemodynamic monitoring was evaluated based on a large number of plausible virtual patients generated using a collective inference algorithm, which demonstrated that it has significant advantage over open-loop pure prediction, and that its accuracy is comparable to PCCO.

Measuring Brand Favorability Using Large-Scale Social Media Data

For decades, brand managers have monitored brand health with the use of consumer surveys, which have been refined to address issues related to sampling bias, response bias, leading questions, etc. However, with the advance of Web 2.0 and the internet, consumers have turned to social media to express their opinions on a variety of topics and, subsequently, have generated an extremely large amount of interaction data with brands. Analyzing these publicly available data to measure brand health has attracted great research attention. In this study, we focus on developing a method to measure brand favorability while accounting for the measure biases exhibited by social media posters. Specifically, we propose a probabilistic graphical model–based collective inference framework and implement a block-based Markov chain Monte Carlo sampling technique to obtain an adjusted brand favorability measure that is correlated with traditional survey-based measures used by brands. To demonstrate the effectiveness of our model, we evaluate it using more than 3,300 brands and about 205 million unique users that interact with those brands collected through Facebook. Our model performs very well, providing brand managers with a new method to more accurately measure consumer opinions toward the brand using social media data.

Analysis of Students' Learning Difficulties in Learning Mathematics at Elementary Schools

This study aims to know the difficulty in learning mathematics experienced by fourth-grade students of SDN 169 Pekanbaru regarding understanding concepts. This study uses descriptive qualitative research with primary data sources from teachers and students of class IV SDN 169 Pekanbaru and secondary data sources obtained from documents and archives supporting learning in class IV to analyze student learning difficulties in mathematics in class IV SDN 169 Pekanbaru. The data collection techniques were obtained through interviews, observation and documentation techniques. To obtain the validity of objective data in qualitative research, the researcher will use source triangulation and technical triangulation. This study uses data analysis techniques Miles and Huberman, namely data collection, data reduction, data presentation and data inference. The results showed students' difficulties in using concepts, difficulties in using principles, and difficulties solving verbal problems. The factors that cause students to experience learning difficulties in learning mathematics in class IV SDN 169 Pekanbaru include internal and external factors. The factors that come from within students (internal) include physical factors, psychological factors (intelligence, attention, interest, talent, motivation), and fatigue. In contrast, the factors that come from outside the student (external) include family factors, school factors (learning methods, learning tools, teaching methods), and community factors.