Reviewerly/exHarmony huggingface dataset:
Using mHealth for Primary Prevention of Dementia: A Proof-of-Concept Study on Usage Patterns, Appreciation, and Beliefs and Attitudes Regarding Prevention

Background: Health- and lifestyle factors account for a substantial part of all dementia cases, which opens the opportunity for primary prevention. However, the required behavioral change is complex and involves targeting multiple risk factors. mHealth interventions can potentially contribute to improving motivation in a low-cost and scalable way. Objective: To explore usage patterns, appreciation, and beliefs and attitudes regarding dementia risk reduction during the use of the MyBraincoach mobile app. Methods: Participants were community-dwelling middle-aged adults from the Netherlands and used either the standard (education) or extended (education+motivational triggers) app version for three months. Two panel studies were combined in this paper. Chi-square tests, t-tests and linear mixed models were used, adjusted for age, sex, and education. Results: Of all participants (n = 299, 50.2% male), 167 (55.9%) had installed the app. The most reported reason for non-use was technical problems (47%). Those who used the app were at baseline already more positive about dementia risk reduction than those who did not use the app. Of all users who completed the evaluation (n = 102), 78.4% (n = 80) stated that the app provided a positive approach towards brain health and 80.4% (n = 82) felt better informed. Younger (&lt;60y) and lower educated participants evaluated the app most positively. Conclusion: Usage of the app was low, but users showed more positive beliefs and attitudes regarding dementia risk reduction. Most users evaluated the app positively and stated to have gained knowledge on the topic. Improving the use of the app must keep high priority in future studies.
Pengembangan Media Pembelajaran Berbasis Augmented Reality (AR) pada Mata Pelajaran IPAS Kelas VIII SMP

Augmented Reality (AR) learning media is a new learning innovation that displays interesting 3D animation visuals and makes complex and abstract concepts more real so that it triggers students' interest and curiosity in learning, so it can be used as an alternative in learning problems. This study aims to develop learning media for IPAS Class VIII Junior High School in order to produce valid media that is practical and effective for use by teachers and students This type of research is a development research (Reseacrh an Development) with the 4D (Four-D) development model. The average score for media validity assessment using Likert scale percentage presentation with media assessment by validator I is 4.93 and the percentage of 98% is categorized as "very valid", validator II is 4.73 with a percentage of 94% categorized as "very valid". And the average score of the material validator is 4.79 with a percentage of 95% categorized as "very valid". The research was conducted in 4 stages, the first stage by conducting curriculum analysis, learner analysis, and concept analysis. Then the initial product development was carried out by making flowcharts and storyboards and then making learning media with the Aseemblr edu application. Furthermore, the validity test stage is carried out by media and material validators, the last one is conducting a practicality trial and effectiveness trial for VIII grade junior high school students.
Research on a Feature Enhancement Extraction Method for Underwater Targets Based on Deep Autoencoder Networks

The low-frequency line spectrum of the radiated noise signals of hydroacoustic targets contains features describing the intrinsic properties of the target that make the target susceptible to exposure. In order to extract the line spectral features of underwater acoustic targets, a method combining image processing and a deep autoencoder network (DAE) is proposed in this paper to enhance the low-frequency weak line spectrum of underwater targets in an extremely low signal-to-noise ratio environment based on the measured data of large underwater vehicles. A Gauss–Bernoulli restricted Boltzmann machine (G–BRBM) for real-value signal processing was designed and programmed by introducing a greedy algorithm. On this basis, the encoding and decoding mechanism of the DAE network was used to eliminate interference from environmental noise. The weak line spectrum features were effectively enhanced and extracted under an extremely low signal-to-noise ratio of 10–300 Hz, after which the reconstruction results of the line spectrum features were obtained. Data from large underwater vehicles detected by far-field sonar arrays were processed and the results show that the method proposed in this paper was able to adaptively enhance the line spectrum in a data-driven manner. The DAE method was able to achieve more than double the extractable line spectral density in the frequency band of 10–300 Hz. Compared with the traditional feature enhancement extraction method, the DAE method has certain advantages for the extraction of weak line spectra.
What Observables Are Needed for Precision Data-Enabled Learning of Inverse Operators?

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.
Analysis of Human activities on Smart Devices Using Riak-TS

In this paper we have implemented Riak TS which is a time series-based database.It is a key valuebased database and has time as important parameter.During the implementation of the project we have understood the installation process, loading the data and also analyzing the data using Riak TS.By doing complex querying we learnt how time plays a crucial role in understanding the data and analyzing them to visualize.
Computer Technologies as a Method of Forming Students' Information Skills in the Process of Learning

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

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

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

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

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.
Panduan Praktis untuk Meningkatkan Kinerja Keuangan UMKM: Implementasi Sistem Akuntansi Microsoft Access pada Lamoela Bar untuk Optimalisasi Laporan Keuangan

The financial management application implemented for Lamoela Bar has brought innovation in the management of micro, small, and medium-sized businesses (MSMEs) in the handicraft industry. With an initial focus on security through a login menu, the app provides restricted access to authorized users. Various app menus, including transaction types, sales, cash transactions, and general ledger, offer easy navigation and business activity recording. The transaction types allow recording of various transaction categories, while the sales menu facilitates efficient product sales recording. The cash transaction menu provides real-time monitoring of cash flow, and the general ledger helps organize transaction records into a ledger. With an intuitive design, this app not only provides efficiency in management but also offers an accurate overview of Lamoela Bar's financial performance. It is expected that the implementation of this app will increase productivity and support sustainable growth for this MSME in the dynamic business world.
Image Captioning: A Comprehensive Review

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

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

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

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