Reviewerly/exHarmony huggingface dataset:
MODELING OF LAUGHING MIMIC CAUGHT WITH MOTION CAPTURE TECHNOLOGY USING FUZZY LOGIC

This article explains the requirement of modeling using fuzzy logic and creates a blurred inference model so that the most realistic result can be reached when transferring a smile to a digital world. This allows for the most accurate representation of the smile to be generated. The term "motion capture technology" refers to the technology that is used in order to transmit the whole of human motion into the digital realm. On the other hand, numerical data that indicates accuracy is employed in the program language that is used in order to achieve the real outcomes of this technology. It has been shown that realism alone is insufficient for a computer to correctly understand a grin that was acquired by motion capture. In this research, the first thing that is discussed is fuzzy logic, followed by motion capture, digital motion capture technology, and finally human face muscles. The contributions that fuzzy logic theory makes to the improvement of issue solving are talked about. Then, beginning with the face muscles, the four primary muscles were designed using graphics so that they had a fuzzy appearance. In conclusion, a discussion of the contribution that the fuzzy logic theory has made to the solution follows.
The State of Open Data 2022

Open science is key to advancing discovery and furthering scientific progress for an accelerated and more effective research system. As proactive partners driving the transition to open science, Springer Nature and Figshare believe that research data should be open, accessible and reusable, to help speed up the pace of discovery and benefit society. As a member of UNESCO’s Global Open Science Partnership, Figshare works closely with other members of the open science community to inform and develop practices toward open data sharing. To better understand the current landscape, Springer Nature, Digital Science, and Figshare published the seventh annual State of Open Data report, based on over 5,000 responses from the global research community about motivations, challenges, perceptions, and behaviours toward open data. This poster will share the key findings from the report, including a focus on respondents’ preferences around open data policies, looking at mandates, requirements and grants, and the role that institutions, publishers and funders play.
Stochastic vs. BFGS Training in Neural Discrimination of RF-Modulation

Neuromorphic classification of RF-Modulation type is an on-going topic in SIGINT applications.Neural network training approaches are varied, each being suited to a certain application.For exemplification I show the results for BFGS (Broyden-Fletcher-Goldfarb-Shanno) optimization in discriminating AM vs FM modulation and of stochastic optimization for the challenging case of AM-LSB vs. AM-USB (upper / lower sideband) discrimination.Although slower than BFGS, the stochastic training of a neural network avoids better local minima, obtaining a stable neurocore.
Context-Aware Ubiquitous Learning Based on Case Methods and Team-Based Projects: Design and Validation

Since the development of information and communication technology ‘learning media’ have been recognized as increasingly important courses by educators to assist students training to be professional teachers to understand the process of designing, developing, utilizing, managing, and evaluating learning resources. The context-aware ubiquitous learning approach design based on case methods and team-based projects has been proposed to guide activities in the student learning process. Therefore, this research aims to describe the design and determine the feasibility or validity of context-aware ubiquitous learning based on case methods and a team-based project model using the research and development (R&amp;D) approach. The procedure consists of (1) model development and (2) product validation. It is important to note that the validation aspect involved five learning material/design experts, five ‘learning media’ experts, and 162 students from state and private universities in Indonesia after which the data obtained from experts and students were analyzed using the descriptive statistics. The results showed that the learning model designed is suitable for use in the student learning process.
Improving Natural Language Person Description Search from Videos with Language Model Fine-Tuning and Approximate Nearest Neighbor

Due to the ubiquitous nature of CCTV cameras that record continuously, there is a large amount of video data that are unstructured. Often, when these recordings have to be reviewed, it is to look for a specific person that fits a certain description. Currently, this is achieved by manual inspection of the videos, which is both time-consuming and labor-intensive. While person description search is not a new topic, in this work, we made two contributions. First, we improve upon the existing state-of-the-art by proposing unsupervised finetuning on the language model that forms a main part of the text branch of person description search models. This led to higher recall values on the standard dataset. The second contribution is that we engineered a complete pipeline from video files to fast searchable objects. Due to the use of an approximate nearest neighbor search and some model optimizations, a person description search can be performed such that the result is available immediately when deployed on a standard PC with no GPU, allowing an interactive search. We demonstrated the effectiveness of the system on new data and showed that most people in the videos can be successfully discovered by the search.
Challenges Faced by Learners with Multiple Disabilities (‘deafness and dumbness’) During Teaching and Learning in Regular Primary Schools: Case of Morogoro Municipality

The purpose of this study was to investigate what challenges faced by learners with Multiple Disabilities (‘deafness and dumbness’) during the teaching and learning process in regular primary schools in Morogoro Municipality. This study was anticipated to deal with four specific research objectives; firstly; to assess respondents’ awareness of Special Needs Education (SNE) and Multiple Disabilities (‘deafness and dumbness’) in regular primary schools in Morogoro municipality, secondly; to identify challenges faced by learners with Multiple Disabilities (‘deafness and dumbness’) during teaching and learning in regular primary schools, thirdly; to exploring how these challenges affect their learning activities and finally; to obtaining possible solutions suggestion to the identified challenges for improving the learning process. A sample of 25 purposively randomly selected respondents included regular primary school teachers and learners with multiple (Deafness &amp; Dumbness). A Descriptive survey design was used to collect data from the respondents using interviews and observation that were administered to teachers without disabilities and learners with disabilities, particularly ‘deafness and dumbness’ in regular primary schools. Both qualitative and quantitative approaches were used to strip respondents’ individual views and straightforward percentages from the numerous responses for descriptive purposes. The IBM SPSS V22.0 version was used for data analysis to improve decision-making through numbers. The study indicated that there were several challenges faced by learners with disabilities, particularly ‘deafness and dumbness’ in multiple within the regular schools. The study recommended modifications and adaptations in curriculum, policy, and implementation to favour the learners with ‘deafness and dumbness’ within regular situations.
New Romanticism: It’s Time for New Realities

The essay elaborates on the fluidity of realities and different forms of shaping them. Realities can be created and thus must be imagined first and implemented second. Reality production happens on different scales: from the small design studio – where products and desires are created – to the big world stage, where political narratives shape global happenings. At the moment we are experiencing a global dispute about howand to what extend humanity should technologically interact with nature. Should humanity intensify the control of climatic issues through the use of technology or slow down and develop new modes of being in the world? Existing movements provide space for exploring new ways of being in the world such as posthumanism, ahumanism, degrowth practice and marginalized forms of knowledge production such as occulture. Those fields come from fundamentally different (research) traditions but overlap in their quest for new non-anthropocentric ontologies. The overlapping space of these movements is conceptualized as a form of New Romanticism which opposes mainstream reality production and includes the nonhuman in its worldview. Methods for shaping realities range from speculative design to (capitalist) magic. I intend to advocate for non-hegemonic ontological explorations in academic research through experimental speculative design methods. I have written in an explorative style in order to implement speculation as amethod. I first begin by looking at a tradition of Romanticism as an “irrational” opposing force to scientism and note how tendencies towards the Romantic have crept into the impetus behind other movements. Afterwards I explore different forms of shaping realities and narratives. Then I will propose new narratives for care for the age of the Anthropocene, followed by some initial ideas of how to implement New Romanticism through methods of Speculative Design. I will conclude with a short summary.
Large motion anime head animation using a cascade pose transform network

We study the problem of talking head animation from a single image where a target anime talking head is generated to mimic the change of facial expression and head movement of source anime characters. Most existing methods focus on generating talking heads from real humans. However, few efforts have been made to create anime talking head. Compared with human head generation, the key challenges of anime head generation are: how to align the pose and facial expression of the target head with that of the source head without explicit facial landmarks. To address this, we propose CPTNetV2, a cascaded pose transform network that unifies face pose transformation and head pose transformation. At the core of CPTNetV2 is the implicit encoding of facial changes and head movement by a pose vector. Given the pose vector, we introduce a mask generator to animate facial expression (e.g., close eyes and open mouth) and a grid generator to simulate head movement, followed by a fusion module to generate talking heads. To tackle large displacement and improve the quality of generation, we further design a details inpainting module with pose vector decomposition to reduce the receptive field of network required for pose transformation. In particular, we collect an anime talking head dataset AniHead-2K that includes around 2000 anime characters with different face/head poses. Extensive experiments on AniHead-2K demonstrate that CPTNetV2 can achieve arbitrary pose transformation conditioned on the target pose vector and outperforms other state-of-the-art methods. We also verify the effectiveness of each module through ablative studies. Additional results show that CPTNetV2 has good generalization and is applicable to generate anime talking head even based on human videos. The dataset will be made available at: https://github.com/zhangjiale487/AniHead-2K.
Aspect Based Opinion Mining &amp; Sentiment Analysis

Opinion mining is a relatively new field that refers to the practice of collecting feedback in the form of online reviews and ratings left by users on various topics. Researchers are now able to monitor the states of consciousness of individuals in real-time because to this development. Just lately, a number of research papers for sentiment analysis were implemented, each of which was based on a unique categorization and ranking procedure. However, the amount of time necessary for the newline performing class has not decreased in any way. Sentiment Sensitivity newline word list SST was provided as a solution to the problem of function mismatch in the go-domain sentiment class across the source area and the target domain; however, achieving improved accuracy and identifying distributional similarities of words became less effective as time went on. Hidden Markov’s persistent development may be seen at the beginning. Cosine In order to achieve more effective and clean pre-processing, a method that is conceptually quite similar to HM-CPCS has been devised. The HM-CPCS methodology, which has recently been suggested, makes use of the POS tagger, a variant of which is based on the Hidden Markov algorithm. Evaluations are created using data from a wide variety of different domains. Similar to a newline, the tags that come before and after it compute the possibility of transitions and the existence of the term newline among the tags in order to increase capability. This is done in order to improve capability.
Development and Industrial Testing of a Method for Assessing the Deformation Properties of a Fixed Soil Massif

The are some actual problems in designing, construction and operation of buildings and structures based on subsident soils. The main points are: to search for the most relevant method and justification of the parameters of the soil base clamp; to ensure the relevance of physical and mechanical characteristics of the clumped soil mass and, finally, to keep on the quality. The development of an effective method for controlling the consolidation of subsidence soils of the foundation during new construction in regard to simultaneous determination of rational technological parameters of clamping at the control plot and stamp tests of the clamped body in order to determine the actual modulus of soil deformation.
Strategic positioning of media and information literacy

Purpose. The paper aims to point out the role of scientiﬁc research platforms as the main drivers of the integration of the concept of media and information literacy into public policies, that is, in the long term, into formal education.&#x0D; Approach/methodology/design. In order to successfully analyse the obstacles to establishing a clear correlation between the lack of systematic and long-term engagement of scientiﬁc research bases and the integration of media and information literacy into public policies, this paper has explored, analysed and conceptualized the approach and results of the Institute for Social Science Research, Faculty of Political Sciences, University of Sarajevo in research, development, advocacy and integration of media and information literacy into the education system in Bosnia and Herzegovina as a case study.&#x0D; Results. The research has attempted to illustrate how scientiﬁc research platforms can be used to inﬂuence (and create an optimal model for public intervention) public policies, rather than just serve as platforms for academic advancement and as project implementation tools.&#x0D; Social value. The scientiﬁc research platform on media and information literacy at the University of Sarajevo can be understood as an applied practice in an open scientiﬁc environment, and developed on the principles of the common good.
Students' Experiences and Usability Evaluation in Interactive Digital Interface Development Process

In this research, it was aimed to determine student experiences in the design process of interactive digital interfaces developed with Figma and to evaluate students’ works developed within the scope of the “Digital Interface Design” course by students and lecturer. The case study design was used in the research. While determining the participant group of the study conducted with 57 students, convenient sampling method, which is one of the non-random sampling types was used. Implementation activities lasted eight weeks. At the end of the implementation process, each of the student was expected to develop interactive mobile application or website interfaces. The qualitative data of the study were analyzed by content analysis method, while the quantitative data were analyzed descriptively. According to findings, most of the mobile application and website interfaces designed by students were evaluated as good usable, and a certain part as excellent usable.
Awareness of New Security Technology: An Analysis of Police Officials' Attitudes toward Predicted Security System

Today, new policing technologies are an alternative to crime prevention as well as rapid suppression of crime. In addition, in terms of operation, the police are actively using new security technologies to present an efficient strategy. In particular, with the advent of the 4th Industrial Revolution, expectations for predictive security among new security technologies have increased. However, the outputs of the forecast plan are related to discretion and it is a very important question to follow the results given this discretion. This is because the output of the predictive security limits subjective discretion in police execution behavior, which contributes to standardization of execution behavior. Therefore, research on the operator and the executor (police officer) who executes the product needs to be preceded by a study on the efficiency of the new security technology system. Therefore, this study directly collected the prerequisites for the new security technology through a questionnaire. In addition, the purpose of the study was to investigate the perception of police officers, who are operators and enforcers who use security technology. Through this, I would like to clarify what are the prerequisites for the operational orientation of the police organization necessary to utilize the results of the new security technology. This study surveyed 140 police officers. As for the specific research contents, a regression analysis was conducted on whether the orientation of the criminal justice system, system stability, and the possibility of standardizing discretion affect attitudes. As a result of the study, influencing the attitude of police officers toward predictive security had a significant effect on the attitude of police officers than on other directions.
Research on Area of Uncertainty of Underwater Moving Target Based on Stochastic Maneuvering Motion Model

Considering the influence of measurement error on target state estimation, there is an uncertain dispersion region for target position estimate, that is, the area of uncertainty (AOU, area of uncertainty). In underwater target tracking, the state estimation is point estimation without AOU estimation and its accuracy is poor in the early stage because of large measurement errors. Fast tracking with higher accuracy and AOU estimation are of great significance to time-sensitive target tracking. To improve the state estimation accuracy in the early stage, and estimate the AOU, a method of AOU estimation of underwater moving target is presented based on a stochastic maneuvering motion (SMM, stochastic maneuvering motion) model. The stochastic maneuvering motion model is established based on the Langevin equation to reflect the movement characteristics of an underwater moving target. Then, the target state is estimated with a noise adaptive Kalman filter by constructing the measurement equation and state equation according to measurement error characteristic and stochastic maneuvering model. Based on the physical significance of the error covariance matrix from the Kalman filter, the parameters of AOU are deduced. Simulation results of underwater target tracking and AOU estimation are presented to demonstrate the relative performance of the proposed algorithm compared with the adaptive Kalman filter. It is clearly shown from the results that SMM tracking algorithm achieves higher accuracy of state estimation in the initial stage of tracking, and the predicted AOU is consistent with the actual distribution of underwater moving targets while yielding more concentrated distribution, which reveals that estimated AOU can be precisely represented by the confidence ellipses. The presented approach and obtained results may be useful in time-sensitive target threat analysis and weapon strike applications.
ESL Learners’ Perception on Twitter, Online Forum, and Blog for Collaborative Learning

Social Media sites can be used by learners, as a platform for information gathering and interaction during collaborative learning activities.However, there is little research on tertiary learners' experience in using social media for collaborative learning.Positive experiences while using social media applications may contribute to better learning outcomes.Thus, this study aims to investigate ESL learners' experience and perception of using Twitter, online forum, and blog for collaborative learning.The study employs a quantitative data collection method involving seventy-seven ESL learners from an intact class at a public university.An online questionnaire survey was used to gather the learners' experiences and perceptions.It referred to the Technology Acceptance Model (TAM) regarding its perceived usefulness and perceived ease of use.Overall, the results showed learners had a positive experience while using the three social media platforms.Blog was also considered the most useful and easy to use for collaborative learning.More insights and information for the implementation of collaborative learning while using social media can promote active learning that fits the current generation in live with the demand for the Industrial Revolution 4.0.
Future Direction and Conclusion

In recent years, graph neural networks become powerful graph analysis paradigm and have made great progress. As a young and promising research field, in the future, we need to pay more attention to their practical problem (e.g., heavy label reliance, safety, and ethical issues) and broader applications. Thus, in this chapter, we illustrate a series of advanced topics for future research directions, including self-supervised learning on graphs, robustness, explainability, fairness, and the promising applications in biochemistry and physics areas. Finally, we summarize the progress of GNNs in this book.
Introduction

Relational structures are ubiquitous and abundant in the real world such as social relations, transaction relations, biological relations, and so on. Graphs or networks are usually used to model relational structures. And researches of graphs have attracted extensive attentions recently, the most important of which is graph representation learning, i.e., learning node embedding representations for downstream tasks. In this chapter, we first introduce some basic concepts and definitions in graph representation learning, and discuss the development of the advanced graph representation learning methods, i.e., graph neural networks. We also emphasize several frontier aspects of graph neural networks mentioned in the book and further conclude the organization of the book in this chapter.