Reviewerly/exHarmony huggingface dataset:
Construction of modern Chinese standard for computer language disfluency detection

Spoken text disfluency detection is an important component of speech computer recognition systems, and its goal is to effectively identify and remove spoken phenomena such as repetition, pauses, corrections and redundancy contained in AI speech recognition text data, thereby making spoken text data more concise and increasing the readability of its text data, and the technology helps to improve the correctness of computer language information processing tasks. However, in computer science or in linguistics, research on computer language disfluency detection techniques for modern Chinese word classes is almost in an academic gap. Based on a qualitative and quantitative analysis of 50,000 items of Chinese conference corpus, this study defines what is non-sentence elements in modern Chinesen and using the spoken language corpus, established a reference model for computer processing of disfluency detection components.
Privacy protection methods of power grid communication data aggregation based on federated learning

In recent years, as more and more countries attach importance to the next generation grid, a large amount of human and financial resources have been invested in the research and development of smart grid. In order to carry out real-time monitoring and intelligent control of the power grid, the control center of the smart power grid needs to continuously collect users' electricity consumption data, which, if leaked, will reveal individual users' electricity consumption rules and living habits. How to monitor regional power consumption without revealing how much individual users use has become a challenge. Therefore, the power grid communication data aggregation privacy protection method is designed based on federated learning. Firstly, the power grid communication data privacy types and encryption tools are analyzed. Secondly, the power grid communication data aggregation privacy protection scheme is designed based on federated learning, so as to realize the power grid communication data aggregation privacy protection. The results show that the privacy protection method designed for power grid communication data aggregation is effective and has certain application value.
Multimodal tag synset induction

Most previous works in synset induction generally ignore the visual data, which contains important semantic information. Instead, in this paper, we present an effective multi-modal solution for tag synsets induction task by leveraging the massive image tags pairs in image-centric social networks, such as Instagram and Pinterest. The proposed method consists of three stages: the first stage learns the textual and visual representations of tags, the second stage learns the distance between tags based on the learned representations in a supervised fashion, and the last stage performs clustering to construct the tag synsets based on the learned distance. In order to perform the tasks of these stages, we collect a suite of new datasets from social media: TagSet with over 415 million sentences for textual representation learning, ImageSet with nearly 16 million image tags pairs for visual representation learning and TagSynSet with a total of 5644 tags from 3680 synsets for distance learning and tag synset induction. Extensive experiments are conducted and the results on TagSynSet show that our proposed method is effective on the tag synset induction task.
Multispectral Benchmark Dataset and Baseline for Forklift Collision Avoidance

In this paper, multispectral pedestrian detection is mainly discussed, which can contribute to assigning human-aware properties to automated forklifts to prevent accidents, such as collisions, at an early stage. Since there was no multispectral pedestrian detection dataset in an intralogistics domain, we collected a dataset; the dataset employs a method that aligns image pairs with different domains, i.e. RGB and thermal, without the use of a cumbersome device such as a beam splitter, but rather by exploiting the disparity between RGB sensors and camera geometry. In addition, we propose a multispectral pedestrian detector called SSD 2.5D that can not only detect pedestrians but also estimate the distance between an automated forklift and workers. In extensive experiments, the performance of detection and centroid localization is validated with respect to evaluation metrics used in the driving car domain but with distinct categories, such as hazardous zone and warning zone, to make it more applicable to the intralogistics domain.
Recent advances in Ecological Aspect For prediction model based on ECG beat classification using neural network

&lt;p&gt;The ecological aspects such as environmental factors, socio-economic constraints, and demographic parameters are one of the key aspects of examining the health benefits of human subjects and are used as a ready reference in ecosystem modeling. Ecosystems are growing at an extraordinary speed around the world. Environmental factors are assumed to be responsible for over half of the global illness load. Environmental and ecological imbalances have a severe effect on public health, food security, global geopolitical and economic stability. The increased amount of environmental polluting factors eventually affects the health of humans and can cause diseases and disorders. Presently, there are various kinds of deadly diseases and disorders that are liable for affecting human health and impacting the eco framework of the whole world. Viruses such as corona, swine flu, omicron, and others are one of the best examples for the research community to understand the vulnerability of human health to these unpredictable causes. As per the report of the world health organization every year more than ten million people are affected by such ecological and environmental disbalance. The impact of environmental factors on the functioning of several organs in humans appears to be significant. There is a need to understand this ecological model with the health of human subjects. In this study, a cohort-based data set of ecological pollutants and physiological signals such as ECG and anthropogenic data of human subjects were extracted from Maharashtra from 2015 to 2021. As per neural network-based hazard ratio was calculated and observed to be deplorable among unhealthy and health categories of human subjects. The accumulative ecosystem is responsible for overburdening organs of living beings and policymakers must focus on the facts of study for modern management framework designs. Such initiatives could result in a more robust understanding of the human health implications of increasing environmental change, impacting decision-making in the environmental preservation and preventive health services.&lt;/p&gt;&#x0D;
Evolution of a cognitive architecture on the basis of a functional core

Abstract Industry 4.0 assumes the use of intelligent components integrated into a single system and capable of interacting with each other without human intervention. Autonomous cognitive agents are the technological basis for building such systems. One of the main problems of modern cognitive architectures is an excessively schematic approach to modeling the processes of cognitive activity. It does not allow the creation of a universal architecture that would be capable of reproducing higher nervous functions without using a predetermined set of perception patterns. This paper proposes an evolutionary approach to creating a cognitive architecture. The basis of this approach is the use of some functional core, which consistently generates the intellectual functions of an autonomous agent. We are considering a cognitive architecture that includes components, the interaction of which ensures the evolution of the agent. The discussion of the development of intelligence is carried out using the conceptual apparatus of semiotics. This allows us to consider the task of developing cognitive functions as a problem of establishing a connection between the Merkwelt and the Werkwelt through the creation of the Innenwelt. The problem of early postnatal ontogenesis is investigated on the basis of the theory of constructivism: we discuss the requirements for the functional core and its composition, as well as the mechanism that initiates the process of cognition. We have developed a numerical method for modeling cognitive activities present at an early stage of ontogenesis. The method was used for the creation of the Cognitive Sensory System.
English Text Readability and Reading Comprehension Performance of Grade 11 Senior High School Students

The study explored the significance of English text readability and reading comprehension proficiency of grade 11 senior high school students. English teachers validated the 45-item reading comprehension test and the reading comprehension intervention materials. Fifteen subjects of the study (who got the lowest pretest score) were selected from the class who got the lowest mean score in the pretest. It made use of the quasi experimental method where the subjects undergone reading comprehension lessons with English texts of varied readability level (high, fair, and low). Pretest and posttest scores are compared. Results indicated that there is a considerable increase in the mean score in the posttest (13.13) as compared to the pretest mean score (10.53). Further, there is significant difference between the pretest and posttest scores (p &lt; 0.05) and that English test readability has substantial effect on reading comprehension.
Vibration signal diagnosis and conditional health monitoring of motor used in biomedical applications using Internet of Things environment

Vibration, especially basic vibration, may cause loose contacts, open circuits, or other contact problems, which account for a large proportion of system failure causes. Due to the complexity of the chassis structure used in the biomedical motors, the vibration analysis of a single component cannot solve the vibration problems encountered in the work of the system. Therefore, it is necessary to use system simulation and experiments to monitor and enhance the health of the structure. Finite element method is used to carry out the dynamic analysis from the component to the system, and calculate the natural frequency, modal shape and harmonic response. Then, the effectiveness and accuracy of the analysis results are verified by experiments through simulation and experiments. The results show that the measures taken have effectively reduced the vibration of the chassis equipment and improved its safety for better health safety of patients. There is just a 2% variation between the natural frequencies acquired from the experiment and the simulation computation. In terms of natural frequency, the first 6-order natural frequencies all have errors of less than 10%, and the natural frequency error is comparable to that of fireproof board.
Visual authoring of virtual reality conversational scenarios for e-learning

The COVID-19 pandemic has led to face-to-face activities being developed in a virtual format that often offers a poor experience in areas such as education. Virtual Learning Environments have improved in recent years thanks to new technologies such as Virtual Reality or Chatbots. However, creating Virtual Learning Environments requires advanced programming knowledge, so this work is aimed to enable teachers to create these new environments easily. This work presents a set of extensions for App Inventor that facilitate the authoring of mobile learning apps that use Chatbots in a Virtual Reality environment, while simultaneously monitoring of student activity. This proposal is based on integrating block-based languages and Business Process Model and Notation diagrams. The developed extensions were successfully implemented in an educational app called Let's date!. A quantitative analysis of the use of these extensions in App Inventor was also carried out, resulting in a significant reduction in the number of blocks required. The proposed contribution has demonstrated its validity in creating virtual learning environments through visual programming and modelling, reducing development complexity.
LEARNING DIFFICULTY IN MATHEMATICS: a case study in a Federal Technological Education Institution

Much is discussed about learning difficulties and disorders, but only recently have studies focused on the dysfunctions of numerosity.Dealing with Mathematics learning difficulties is a complex topic, with research still at an early stage, even more so in the context of vocational and technological education (EPT).Therefore, dealing with this topic is essential to better understand this phenomenon and, thus, provide better learning conditions for students.This study aimed to psychopedagogically evaluate, through the case study methodology, the learning conditions of an integrated technical high school student with specific learning difficulties in Mathematics, in a federal educational institution.The student was referred to a psychopedagogical support teaching project.The evaluation was carried out in ten individual sessions, once a week, lasting one hour, in the opposite shift of regular classes.Anamnesis and analysis of school history in relation to Mathematics were carried out.Also, seven assessment instruments were applied, related to cognitive skills for learning Mathematics, mathematical anxiety and dyscalculia.As a result, a picture very close to mathematical anxiety was found, as well as obstacles to mathematical reasoning due to accentuated difficulties in cognitive functions related to mathematical learning, which are reflected in obstacles in the application of mathematical procedures, leading the student to serious difficulties.to solve, in particular, mathematical problems.In view of the results, we can see the importance of psychopedagogical support, in favor of a truly inclusive education.
Strengthening Broadband through Library Engagement

In the Pittsburgh region, the Every1online project, led by the non-profit Meta Mesh, bridges communities with no-cost Internet service. Recognizing that digital literacy is a core component of digital inclusion, the authors worked with Meta Mesh to consider the digital literacy needs of community members and to be responsive to these needs through resources provided to individuals as their Internet connection is established. To build an understanding of these digital literacy needs, the research team surveyed librarians to discover the questions that patrons bring to the library. In this paper, we propose that libraries both provide essential social infrastructure and, as evidenced by this case study, serve as important intermediaries to critical infrastructure. We introduce the concept of a critical infrastructure intermediary, an organization or individual that helps people to find and use critical infrastructure and that helps to strengthen that infrastructure through a feedback loop.
Parasocial Interactions and Relationships among Emerging Adults in Online Communities

Emerging adulthood (age 19-29) can be an exploratory, yet tumultuous time of life. Online methods of connecting with others can be especially effective during this often-fluctuating life stage since the point of entry to engage online is generally low. Among the most popular social media sites, YouTube has had the highest traffic on a video streaming site almost since its inception. YouTube enables content creators to express themselves and connect with a follower base, and for followers to receive advice, entertainment, and even relate personally to content creators. Content creators are often unable to communicate directly, leading followers to experience one-sided connections. Such one-sided connections, or “parasocial interactions,” (PSI) have historically occurred toward film and television figures but have also occurred toward YouTube presenters. When PSI extend beyond consuming content to perceiving the existence of a relational bond with a creator, they form a “parasocial relationship” (PSR). With over three million subscribers, the Nerdfighteria community evolved from a YouTube vlog channel called the Vlogbrothers, founded by Hank and John Green in 2007. This work focuses on understanding the Nerdfighteria community through the conceptual framework of the development of PSI and PSR through video streaming and an ecosystem of platforms used by online communities. Major contributions include (1) Extending the themes of PSR development through the lens of emerging adulthood, (2) Understanding design heuristics supporting the development of PSR online, (3) Providing methodological insights for the use of graphic elicitation in remote qualitative data collection for participant agency and equity.
Revisiting Instructional Approaches in Response to Emerging Cataloging Standards

The ever-shifting landscape of cataloging standards over the last decade has kept library and information science (LIS) educators on their toes, and continuing developments only promise to maintain this trend. Examples include the publication of the International Federation of Library Association &amp; Institution’s (IFLA’s) conceptual model Library Reference Model (LRM) in 2017, the release of the new and heavily revised version of the cataloging content standard Resource Description and Access (RDA) in 2020, and Library of Congress’ upcoming Bibliographic Framework (BIBFRAME) standard for encoding and publishing library data. These new standards have altered the way in which cataloging work is conceived and discussed, radically changed the interfaces used for accessing cataloging documentation, and are now spurring the creation of new software and tools for cataloging work, including Library of Congress’ new Marva metadata editor. At the same time, the increasing inclusion of linked data projects in libraries, archives, and other cultural heritage institutions are requiring new skills, practices, and workflows that cataloging and metadata librarians must be prepared for. With many of these standards and initiatives not fully implemented in the majority of libraries, cataloging educators face a dilemma in deciding how best to prepare their students to successfully navigate this time of change, where old and new standards and practices intersect.&#x0D; The Technical Services Education SIG session will include a panel of three educators (two of whom are current cataloging practitioners) with unique perspectives on strategies for teaching toward emerging cataloging standards. After brief presentations by each of the panelists, audience members will be encouraged to ask questions and offer their own experiences and ideas concerning this area of LIS education.&#x0D; This conversation aligns well with the ALISE conference theme of “Go Back and Get It: From One Narrative to Many” as cataloging practice and education must be in constant dialogue about the preparation of LIS students for the lifespan of their careers. How can LIS educators prepare students for an environment in which different institutions are facing drastically different plans and timelines for the implementation of new standards? How can teaching practices be adjusted to best leverage current best practices alongside new strategies? And how can study of past, present, and emerging cataloging standards and practices provide a solid foundation on which LIS students can build throughout their careers? This panel will offer opportunities for LIS educators to reach back to knowledge and experiences concerning previous standards transitions, share best practices for addressing the current, dynamic environment, and look toward the future of cataloging and metadata education and preparation.
Building Capacity for Library Participation in Open Civic Data Work with the Civic Data Education Series

This paper reports on the development and first phase evaluation of the Civic Data Education Series, a modular instructional series that builds civic data literacy and supports library workers’ participation in their civic data ecosystems. The series, funded by the IMLS, is intended to be integrated into LIS coursework and used for professional development training in libraries. The modules encourage information professionals to work alongside community members as they use civic data to uncover structures of oppression, tell their own stories, and address information needs. Through the series and its integration in LIS courses and library training, we aim to prepare library workers to be data intermediaries who contribute to equitable civic data creation, stewardship, and sharing.
LibParlor Online Learning

Learning how to conduct research in library and information science is not always a requirement in LIS graduate programs. The result of uneven training impacts librarianship and the ways in which certain voices and research are privileged in our scholarship. This work in progress poster will share the results of an IMLS grant funded national needs assessment wherein we learned about the experiences, educational needs, and institutional constraints of MLIS students and academic library workers pursuing research through a survey and focus groups. The poster will share insights on the experiences of academic library workers and their needs for institutional, educational, and financial support for training and advocacy in LIS research. We will share information about what LIS researchers feel they benefited from their graduate coursework in research methods, as well as ideas for improvement for these courses.
Deep Matrix Factorization Based on Convolutional Neural Networks for Image Inpainting

In this work, we formulate the image in-painting as a matrix completion problem. Traditional matrix completion methods are generally based on linear models, assuming that the matrix is low rank. When the original matrix is large scale and the observed elements are few, they will easily lead to over-fitting and their performance will also decrease significantly. Recently, researchers have tried to apply deep learning and nonlinear techniques to solve matrix completion. However, most of the existing deep learning-based methods restore each column or row of the matrix independently, which loses the global structure information of the matrix and therefore does not achieve the expected results in the image in-painting. In this paper, we propose a deep matrix factorization completion network (DMFCNet) for image in-painting by combining deep learning and a traditional matrix completion model. The main idea of DMFCNet is to map iterative updates of variables from a traditional matrix completion model into a fixed depth neural network. The potential relationships between observed matrix data are learned in a trainable end-to-end manner, which leads to a high-performance and easy-to-deploy nonlinear solution. Experimental results show that DMFCNet can provide higher matrix completion accuracy than the state-of-the-art matrix completion methods in a shorter running time.