Reviewerly/exHarmony huggingface dataset:
What Observables Are Needed for Precision Data-Enabled Learning of Inverse Operators?

Authors:
raw_author_name: Liangwu Yan
raw_affiliation_string: Department of Mechanical Engineering, University of Washington, Seattle, Seattle, Washington 98195
raw_author_name: Santosh Devasia
raw_affiliation_string: Department of Mechanical Engineering, University of Washington, Seattle, Seattle, Washington 98195

Abstract: Abstract The advent of easy access to large amount of data has sparked interest in directly developing the relationships between input and output of dynamic systems. A challenge is that in addition to the applied input and the measured output, the dynamics can also depend on hidden states that are not directly measured. In general, it is unclear what type of data, such as past input and or past output is needed, to learn inverse operators (that predict the input needed to track a desired output for control purposes) with a desired precision. The main contribution of this work is to show that, irrespective of the selected model, removing the hidden-state dependence and achieving a desired precision of inverse operators require (i) a sufficiently-long past history of the output and (ii) sufficiently-precise estimates of the output's instantaneous time derivatives that are necessary and sufficient for linear systems, and under some conditions, for nonlinear systems. This insight, about the required observables (output history and derivative) for removing the hidden-state dependence and achieving precision, is used to develop a data-enabled algorithm to learn the inverse operator for multi-input multi-output square systems. Simulation examples are used to illustrate that neural nets (with universal approximation property) can learn the inverse operator with sufficient precision only if the required observables, identified in this work, are included in training.
Computer Technologies as a Method of Forming Students' Information Skills in the Process of Learning

Authors:
raw_author_name: Oksana Shelever
raw_affiliation_string: Candidate of Pedagogical Sciences, Associate Professor, Associate Professor of the Department of Psychology, Uzhhorod National University
raw_author_name: Hanna Lopatina
raw_affiliation_string: PhD in Pedagogy, Associate Professor of the Department of Applied Psychology and Speech Therapy, Berdyansk State Pedagogical University
raw_author_name: Vadym Lisovyi
raw_affiliation_string: Candidate of Pedagogical Sciences, Associate Professor of the Department, Music Yuriy Fedkovych Chernivtsi National University
raw_author_name: Vira Polishchuk
raw_affiliation_string: Doctor of Pedagogical Sciences, Professor of the Department of Special and Inclusive Education, Volodymyr Hnatyuk Ternopil National Pedagogical University

Abstract: The article investigates the problem of formation of future teachers' readiness to innovative activity, the role of computer technologies as a method of formation of students' information skills in the learning process. Based on the stated and analyzed interpretations of the authors about the concept of "innovations in education", the research position to consider innovation as an important element of education support in a state of dynamic development, adequate to the demands of today's society as a phenomenon of collective or formed individual professional creative activity of a teacher was formulated. On the example of the course, in which a systematic theoretical, methodological and practice-oriented training of students is carried out, the experience of formation of readiness of future teachers to innovative activity is described, the leading forms of pedagogical activity are presented. According to the results of the study it is concluded that the formation of the future teacher's readiness for innovative activity contributes to the identification of the conceptual position of the specialty, the identification of development resources and innovation (including potential) and their critical evaluation, creating conditions for finding innovative solutions in education. This competence is an important part of the professionalism of the today's teacher. Also in the article the role of computer technologies as a method of formation of students' information skills in the learning process is studied; e-learning tools as a basic step of realization of digital pedagogical technologies, the structure of the author's system of preparation of university teacher to the application of e-learning tools in professional activity is given.
The Role of Current Computer Technologies in Developing Professional Competence of Music Teachers: A Model of a Personalized Educational Environment

Authors:
raw_author_name: Tetiana Rastruba
raw_affiliation_string: PhD in Pedagogy, Docent, Associate Professor of the Department of Vocal and Choral Skills, Nizhyn Mykola Gogol State University
raw_author_name: Khrystyna Pelekh
raw_affiliation_string: Senior Lecturer of the Department of Vocal and Choral, Choreographic and Visual Arts, Drohobych Ivan Franko State Pedagogical University
raw_author_name: Olena Koval
raw_affiliation_string: Candidate of Pedagogical Sciences (Ph.D.), Docent, Associate Professor of the Department of Music Pedagogy and Choreography, Nizhyn Mykola Gogol State University

Abstract: The importance of the article lies in the fact that the profession of school music teacher today needs to create its own updated competence model of personal educational environment taking into account digital transformation and computerization of general education. The information competence of the music teacher, the issues of his professional development in working with music and computer technologies are taken into account. Analyzed the possibility of building a personal educational environment in the era of postmodern (including electronic) and their application in further professional activities. The author analyzes the possibility of introducing modern computer technology as a resource for the formation of musical needs of the teacher of the educational institution. In addition to professional competencies, some general requirements for music teachers today, which can be grouped into four groups: general professional, communicative, behavioral and analytical. The article presents a model of university music education in the postmodern era; ways of acquiring professional competence of a music teacher are considered; the stages of creating a model of personal educational environment of a music teacher in the postmodern era are described; the role of professional competence of a music teacher in the postmodern personal educational environment model is defined; the role of current music and computer technologies (MCT) in creating a model of personal educational environment of a music teacher was determined.
EASE: Energy Optimization through Adaptation – A Review of Runtime Energy-Aware Approximate Deep Learning Algorithms

Authors:
raw_author_name: Amin Aminifar
raw_affiliation_string: Heidelberg University
raw_author_name: Nima Taherinejad
raw_affiliation_string: Technische Universität Wien (TU Wien)
raw_author_name: Axel Jantsch
raw_affiliation_string: Technische Universität Wien (TU Wien)

Abstract: This survey provides an overview of the state-of-the-art in runtime adaptive Approximate Computing (AxC) for Deep Learning (DL) algorithms, highlighting the challenges and opportunities in the field. The survey covers a broad spectrum of applications, including medical applications, computer vision, and natural language processing. Various power-constrained platforms, such as System-on-Chips (SoCs), Application Specific Integrated Circuits (ASICs), and Field Programmable Gate Arrays (FPGAs), are explored for their utilization in implementing runtime adaptive AxC. The survey explores various techniques, such as dynamic quantization, adaptive pruning, and low-rank approximations, offering a detailed discussion of their advantages and disadvantages. Specifically, in some surveyed research works, the runtime approximation is achieved through the utilization of machine learning algorithms, with a notable emphasis on Reinforcement Learning (RL). These approaches aim to realize runtime conditions and exploit them appropriately. By providing insights into the advancements and trends in runtime adaptive AxC, this survey serves as a valuable resource for researchers and practitioners interested in this rapidly evolving area of computing. This survey conducts an in-depth investigation into the application, challenges, and scope of runtime adaptive AxC techniques, aiming to mitigate energy consumption while preserving acceptable levels of accuracy in DL models. Our primary focus lies on Convolutional Neural Networks (CNNs), with an emphasis on their application in diverse domains. In striving for comprehensiveness, the survey encompasses selected research works that extend beyond CNNs, including alternative DL models like Recurrent Neural Networks (RNNs). our scope of applications, focuses on CNNs; however, to make a comprehensive survey, we cover some surveyed research works that contain other DL models, such as RNNs. It also highlights the importance of considering specific application requirements and available resources when choosing the appropriate technique.
Backtracking CTE Pathways: Identifying and Investigating Pathways and Critical Junctures in Two-Year Information Technology Programs

Authors:
raw_author_name: Marcia Mardis
raw_affiliation_string: Florida A&M University - Florida State University

Abstract: Abstract This NSF Advanced Technological Education (ATE) research and development project aims to design and test a Backtracking Technique for understanding the pathways students take through college and into careers in science, technology, engineering, and mathematics (STEM) and career and technical education (CTE), with the focus of this project on information technology (IT). The project gathers data about current and former students who started in the same cohort, includes institutional research data (e.g., grades, demographics, course-taking) and merges these data with employment data from surveys and lived experiences obtained from interviews. These data are analyzed to identify potential pathways and critical junctions that may lead to student success or other outcomes. The research team is led by a doctoral granting institution and a community college, and includes four additional community colleges that collectively serve rural and urban student populations. In this paper we share the potential of the Backtracking Technique to generate contextualized career pathway data for institutions and create visualizations that can aid in institutional decision-making through a study pilot. The pilot is an initial effort to test the project's aims of integrating institutional data with phenomenological data to model student progression through post-secondary STEM programs. The analysis will identify and verify influencers that support or hinder student success. Quantitative data analyses will consist of descriptive and comparative methods, which will be verified and informed by open coding and thematic analysis of the qualitative data. We share how the systematic investigation of institutional and phenomenological data used in the Backtracking Technique has the potential to: (1) generate practical knowledge about academic/career pathways in information technology for use by stakeholders; (2) identify and examine relationships among these pathways, students experiences, and psychosocial factors; and (3) add to the analytical methods available to institutional research professionals to document, investigate, and visualize student pathway information using data dashboards. This ATE project has great potential to transform the technician preparation for the advanced technology fields that drive the nation's economy.
Cybersecurity for Everybody - A Multi-Tier Approach to Cyber Security Education, Training, and Awareness in the Undergraduate Curriculum

Authors:
raw_author_name: Nikunja Swain
raw_affiliation_string: South Carolina State University

Abstract: Abstract Almost every career encompasses some form of security and today's students must be introduced to various aspects of security to be effective in their career and daily lives. South Carolina State University (SC State University) has an ongoing Target Infusion Proposal grant from National Science Foundation (NSF) to address the problem of the lack of awareness and participation in cyber security since 2019. The project vision is to create a successful model of institution wide reform for undergraduate cybersecurity education at SC State University using instruction, internships, and seminars. The student should be able to evaluate, make decisions, and take responsible actions in the context of cyber security. This project uses a multi-tier approach to increase capacity in cyber security education, training, and awareness in the undergraduate curriculum The objective of this poster presentation is to share our experiences with various project activities. The findings presented in this paper may be used by interested parties involved in STEM curriculum.
Image Captioning: A Comprehensive Review

Authors:
raw_author_name: Yash Vardhan Mishra

Abstract: Abstract: The Image Caption Generator is an intriguing project that bridges the domains of computer vision and natural language processing, aiming to automatically generate descriptive text for images. In the last decade, considerable progress has been made, yet key challenges in optimising Convolutional Neural Networks (CNNs) for precise image feature extraction and refining Long Short-Term Memory (LSTM) networks for coherent text generation persist. Challenges include overfitting, limited context understanding, and difficulties in modelling long-range dependencies. Solutions have emerged in the form of attention mechanisms, which help focus on relevant image regions, and advanced LSTM architectures like the use of Gated Recurrent Units (GRUs) to improve sequential modelling. These innovations have significantly enhanced the overall performance, leading to more accurate and contextually relevant image captions, ultimately advancing the synergy between computer vision and natural language processing in the realm of image captioning
Arithmetic Feature Interaction Is Necessary for Deep Tabular Learning

Authors:
raw_author_name: Cheng, Yi
raw_author_name: Hu, Renjun
raw_author_name: Ying, Haochao
raw_author_name: Shi, Xing
raw_author_name: Wu, Jian
raw_author_name: Lin, Wei

Abstract: Until recently, the question of the effective inductive bias of deep models on tabular data has remained unanswered. This paper investigates the hypothesis that arithmetic feature interaction is necessary for deep tabular learning. To test this point, we create a synthetic tabular dataset with a mild feature interaction assumption and examine a modified transformer architecture enabling arithmetical feature interactions, referred to as AMFormer. Results show that AMFormer outperforms strong counterparts in fine-grained tabular data modeling, data efficiency in training, and generalization. This is attributed to its parallel additive and multiplicative attention operators and prompt-based optimization, which facilitate the separation of tabular samples in an extended space with arithmetically-engineered features. Our extensive experiments on real-world data also validate the consistent effectiveness, efficiency, and rationale of AMFormer, suggesting it has established a strong inductive bias for deep learning on tabular data. Code is available at https://github.com/aigc-apps/AMFormer.
Verifiable evaluations of machine learning models using zkSNARKs

Authors:
raw_author_name: South, Tobin
raw_author_name: Camuto, Alexander
raw_author_name: Jain, Shrey
raw_author_name: Nguyen, Shayla
raw_author_name: Mahari, Robert
raw_author_name: Paquin, Christian
raw_author_name: Morton, Jason
raw_author_name: Pentland, Alex 'Sandy'

Abstract: In a world of increasing closed-source commercial machine learning models, model evaluations from developers must be taken at face value. These benchmark results, whether over task accuracy, bias evaluations, or safety checks, are traditionally impossible to verify by a model end-user without the costly or impossible process of re-performing the benchmark on black-box model outputs. This work presents a method of verifiable model evaluation using model inference through zkSNARKs. The resulting zero-knowledge computational proofs of model outputs over datasets can be packaged into verifiable evaluation attestations showing that models with fixed private weights achieve stated performance or fairness metrics over public inputs. These verifiable attestations can be performed on any standard neural network model with varying compute requirements. For the first time, we demonstrate this across a sample of real-world models and highlight key challenges and design solutions. This presents a new transparency paradigm in the verifiable evaluation of private models.
The THREE CENTERS OF EDUCATION AS INSTITUTIONS FOR THE DEVELOPMENT OF LEARNING THEORY

Authors:
raw_author_name: Al-Muthmainnah -
raw_affiliation_string: Sekolah Tinggi Keguruan dan Ilmu Pendidikan Dampal Selatan

Abstract: This research is aiming at elaborating the three educational centers which is a concept that gradually and integrated assumes educational responsibility for children. These three centers include education in the family environment, education in the school environment, and education in the community environment. The three centers responsible for education are required to work together comprehensively. This research uses a literature review approach combined with the researcher's reflection method. The literature method utilizes research articles published in various current scientific journals, while the reflection method is based on the knowledge and experience of researchers in recognizing, identifying and responding to student phenomena in various educational institutions. The family environment, which is the first environment and is considered the foundation of a child's education and the beginning of the education that will be pursued later, also plays a major role in personality formation. The school environment is the second environment that children face. Education in the school environment has a foundation, objectives, content, methods and other requirements which are arranged systematically in the form of a curriculum. Therefore, schools are considered the foundation of knowledge. Meanwhile, in society, children will be aware of a number of educational and teaching theories that have been taught in previous educational institutions. Then they will actualize and involve themselves in the community environment.&#x0D; Keywords: three centers of education, development, learning theory
WRITING COMPARISON/CONTRAST PARAGRAPH WITH BLOCK STYLE

Authors:
raw_author_name: Impiani Zagoto
raw_affiliation_string: Universitas Nias Raya

Abstract: A comparison/contrast paragraph can be organized into two ways known point-by-point, and block model. The most of fifth semester students of English department at Nias Raya University, however, prefer to organize their writing with block model. The reasons for choosing the model were the purposes of this research. This research was carried out by using qualitative descriptive. The data were obtained through unstructured questionnaire. The result of this research showed that there are three reasons of the students in using block model in organizing the comparison/contrast paragraph; the first is easier to be organized; the next is good for the shorter paragraph; and the last is the similarities or differences between the compared subjects is understood easily. Hence, block organization for a comparison/contrast paragraph is the easiest way for the students to construct their paragraph to be interested writing rather than other style. Therefore, this information can be a consideration for the lecturer in teaching the type of the paragraph.
Fluxonium qubits in a flip-chip package

Authors:
raw_author_name: Aaron Somoroff
raw_affiliation_string: SEEQC, Inc., Elmsford, New York 10523, USA
raw_author_name: Raymond A. Mencia
raw_affiliation_string: Department of Physics, Joint Quantum Institute, and Quantum Materials Center, University of Maryland, College Park, Maryland 20742, USA
raw_author_name: Maxim G. Vavilov
raw_affiliation_string: Department of Physics and Wisconsin Quantum Institute, University of Wisconsin - Madison, Madison, Wisconsin 53706, USA
raw_author_name: Vladimir E. Manucharyan
raw_affiliation_string: Department of Physics, Joint Quantum Institute, and Quantum Materials Center, University of Maryland, College Park, Maryland 20742, USA; École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
raw_author_name: Oleg A. Mukhanov
raw_affiliation_string: SEEQC, Inc., Elmsford, New York 10523, USA

Abstract: The strong anharmonicity and high coherence times inherent to fluxonium superconducting circuits are beneficial for quantum information processing. In addition to requiring high-quality physical qubits, a quantum processor needs to be assembled in a manner that minimizes crosstalk and decoherence. In this paper, we report on fluxonium qubits packaged in a flip-chip architecture, where a classical control and readout chip is bump bonded to the quantum chip, forming a multichip module. The modular approach allows for improved connectivity between the qubits and control and readout elements, and separate fabrication processes. We characterize the coherence properties of the individual fluxonium qubits, demonstrate high-fidelity single-qubit gates with 6-ns microwave pulses (without DRAG), and identify the main decoherence mechanisms to improve on the reported results.
Auto-Encoding Variational Bayes

Authors:
raw_author_name: Jingxuan Liu
raw_affiliation_string: International Elite Engineering School, East China University of Science and Technology
raw_author_name: Yiqi Wu
raw_affiliation_string: International College of Engineering, Changsha University of Science and Technology
raw_author_name: Yige Xu
raw_affiliation_string: College of Electronic Science and Engineering, Jilin University
raw_author_name: Zhanhao Zhang
raw_affiliation_string: School of Mechanical Transport and Engineering, Chongqing University

Abstract: This paper employs the Auto-Encoding Variational Bayes (AEVB) estimator based on Stochastic Gradient Variational Bayes (SGVB), designed to optimize recognition models for challenging posterior distributions and large-scale datasets. It has been applied to the mnist dataset and extended to form a Dynamic Bayesian Network (DBN) in the context of time series. The paper delves into Bayesian inference, variational methods, and the fusion of Variational Autoencoders (VAEs) and variational techniques. Emphasis is placed on reparameterization for achieving efficient optimization. AEVB employs VAEs as an approximation for intricate posterior distributions.
Development of Learning Blocket Media for Vocabulary at A2 Level

Authors:
raw_author_name: Jeanita Monica
raw_affiliation_string: Universitas Negeri Medan
raw_author_name: Herlina Jasa Putri Harahap
raw_affiliation_string: Universitas Negeri Medan

Abstract: The research aims to determine the process of making learning media using Blooket media for vocabulary learning and to describe the results of making learning media using Blooket for vocabulary learning. This research is a compilation study. This research uses the Richey and Klein model. There are three steps in this research, which are divided into three steps: (1) planning, (2) production, and (3) evaluation. The research results show that learning materials on the topic of family history were created using data collected from existing data sources during the research. Blooket's media about family history is packaged well, practical, interesting and effective so that students can easily understand the material and use it as a learning tool.