Private Learning Research Articles

COMPARATIVE ANALYSIS OF FEDERATED MACHINE LEARNING ALGORITHMS

In this paper, the authors propose a new machine learning paradigm, federated machine learning. This method produces accurate predictions without revealing private data. It requires less network traffic, reduces communication costs and enables private learning from device to device. Federated machine learning helps to build models and further the models are moved to the device. Applications are particularly prevalent in healthcare, finance, retail, etc., as regulations make it difficult to share sensitive information. Note that this method creates an opportunity to build models with huge amounts of data by combining multiple databases and devices. There are many algorithms available in this area of machine learning and new ones are constantly being created. Our paper presents a comparative analysis of algorithms: FedAdam, FedYogi and FedSparse. But we need to keep in mind that FedAvg is at the core of many federated machine learning algorithms. Data testing was conducted using the Flower and Kaggle platforms with the above algorithms. Federated machine learning technology is usable in smartphones and other devices where it can create accurate predictions without revealing raw personal data. In organizations, it can reduce network load and enable private learning between devices. Federated machine learning can help develop models for the Internet of Things that adapt to changes in the system while protecting user privacy. And it is also used to develop an AI model to meet the risk requirements of leaking client's personal data. The main aspects to consider are privacy and security of the data, the choice of the client to whom the algorithm itself will be directed to process the data, communication costs as well as its quality, and the platform for model aggregation.

중･고등학교 시기 사교육 및 자기주도학습 시간의 종단적인 변화에 따른 집단 분류 및 대학교 시기의 핵심역량에 대한 영향 분석

This study explored the impact of private learning and self-regulated learning time during middle and high school years on core competencies during the first university year, utilizing data from the 5th to 9th waves of the Korean Educational Longitudinal Study. A sample of 2,946 students entered universities in the 9th wave was analyzed. Employing a multivariate growth mixture model, students were classified into four types based on their longitudinal patterns of private learning and self-regulated learning time. Analysis of factors influencing the classification of each type revealed that students’ individual factors and family factors collectively influenced private learning and self-regulated learning time. Additionally, differences in core competencies during the first university year across each type indicated that students with higher self-regulated learning time during middle and high school exhibited higher levels of core competencies during university. This study is significant in that it analyzes students’ longitudinal patterns of learning time by considering both private and self-regulated learning time, and it highlights the importance of structuring learning time during middle and high school to cultivate high levels of core competencies.

Exploring music learning needs of working-age individuals in Thailand

This quantitative study delves into the music learning needs of working-age individuals in Thailand, focusing on those aged 21 to 59 years outside the music profession. The research, conducted in October 2023, collected responses from 503 participants through an online structured questionnaire. The findings revealed that “As a hobby/for relaxation” emerged as the primary motivation for enrolling in music classes, among currently enrolled, previously enrolled, and prospective participants. Desired learning contexts revealed “Onsite learning” with a “Very High” mean score for current ( x− = 4.89) and past enrollees ( x− = 4.58), while those aspiring to enroll rated this and “Hybrid Learning” in a “High” level. “Private learning” earned a “High” rating across groups, and “Small group learning” received a “High” rating for prospective and past enrollees. Exploring learning styles, participants consistently rated items at a High Level, encompassing “Involvement in learning choices,” “Prioritizing adaptation over theory,” “Using metaphor,” “Visual learning,” “Auditory learning,” and “Kinesthetic learning.” Barriers to music learning, including “Time management” and “Memory,” were notably high among past enrollees. These insights offer valuable guidance for tailoring music activities or lessons to the specific needs of adult learners.

Network effects on information acquisition by DeGroot updaters

AbstractIn today’s world, social networks have a significant impact on information processes, shaping individuals’ beliefs and influencing their decisions. This paper proposes a model to understand how boundedly rational (DeGroot) individuals behave when seeking information to make decisions in situations where both social communication and private learning take place. The model assumes that information is a local public good, and individuals must decide how much effort to invest in costly information sources to improve their knowledge of the state of the world. Depending on the network structure and agents’ positions, some individuals will invest in private learning, while others will free-ride on the social supply of information. The model shows that multiple equilibria can arise, and uniqueness is controlled by the lowest eigenvalue of a matrix determined by the network. The lowest eigenvalue roughly captures how two-sided a network is. Two-sided networks feature multiple equilibria. Under a utilitarian perspective, agents would be more informed than they are in equilibrium. Social welfare would be improved if influential agents increased their information acquisition levels.

The effect of COVID-19 pandemic on emotional wellbeing of education instructors: A perspective of Kenya’s private schools

The COVID-19 pandemic phenomenon generated inordinate strain and experiences across a wide range of sectors in Kenya, with the education segment introduced to its own set of unique challenges. Some instructors in private learning institutions joined the job seeking market once again as several schools indefinitely closed down. This often-needed reskilling and turned out to be frustrating as the job opportunities kept shrinking rapidly. This study looked at the effect of COVID-19 pandemic education disruption on emotional wellbeing of education instructors, with a focus on private schools’ teachers in Kenya. Data was collected from 367 respondents who completed a closed ended questionnaire and quantitative analysis was undertaken. The results revealed that there is a significant effect of COVID-19 pandemic education disruption on private school teachers’ emotional wellbeing. The prediction equation based on the unstandardized coefficients was statistically significant, F 1,365 = 46.035, p < 0.001 and accounted for approximately 11.2% of the variance of emotional wellbeing (R2 = 0.112, Adjusted R2 = 0.110). Therefore, hypothesis stated as COVID-19 pandemic education disruption has no significant effect on Kenyan private school teachers’ emotional wellbeing was not supported. The moderating effect of emotional and physical support did not significantly account for more variance with R2 Change = .009, p > .024. The findings supported the hypothesis that emotional and physical support does not moderate the effect of Covid-19 pandemic education disruption on Kenya private school teachers’ emotional wellbeing.

Differentially Private Guarantees for Analytics and Machine Learning on Graphs: A Survey of Results

We study the applications of differential privacy (DP) in the context of graph-structured data and discuss the formulations of DP applicable to the publication of graphs and their associated statistics as well as machine learning on graph-based data, including graph neural networks (GNNs). Interpreting DP guarantees in the context of graph-structured data can be challenging, as individual data points are interconnected (often non-linearly or sparsely). This connectivity complicates the computation of individual privacy loss in differentially private learning. The problem is exacerbated by an absence of a single, well-established formulation of DP in graph settings. This issue extends to the domain of GNNs, rendering private machine learning on graph-structured data a challenging task. A lack of prior systematisation work motivated us to study graph-based learning from a privacy perspective. In this work, we systematise different formulations of DP on graphs, discuss challenges and promising applications, including the GNN domain. We compare and separate works into graph analytics tasks and graph learning tasks with GNNs. We conclude our work with a discussion of open questions and potential directions for further research in this area.

Exit Dilemma: The Role of Private Learning on Firm Survival

We study the exit of duopolists from a stochastically declining market. Firms privately learn about the market conditions from observing the stochastic arrival of customers, while exit decisions are publicly observed. A larger firm is more likely to have customers and hence has better information about the market conditions. We provide sufficient conditions for either the smaller or the larger firm to be the first to exit in the unique equilibrium. Because of observational learning, exiting may be a firm's dominant action since continuing operation would bring too optimistic news to the rival, leading it to further postpone its exit. (JEL D21, D43, D83, L21, L25)

The effects of rivalry on scientific progress under public vs private learning

We offer a model of scientific progress in which uncertainty resolves over time. We show that rivalry leads to less experimentation, extending results for preemption games to experimentation with uncertain outcomes. We compare experimentation duration and welfare when experimental outcomes are publicly versus privately observable. We show that public learning can generate more experimentation and higher welfare when uncertainty about the feasibility of a breakthrough is large; breakthroughs are rare even when they are feasible; and experiments produce results infrequently. Our results shed light on recent criticism of the science system.

Differentially Private Learning with Per-Sample Adaptive Clipping

Privacy in AI remains a topic that draws attention from researchers and the general public in recent years. As one way to implement privacy-preserving AI, differentially private learning is a framework that enables AI models to use differential privacy (DP). To achieve DP in the learning process, existing algorithms typically limit the magnitude of gradients with a constant clipping, which requires carefully tuned due to its significant impact on model performance. As a solution to this issue, latest works NSGD and Auto-S innovatively propose to use normalization instead of clipping to avoid hyperparameter tuning. However, normalization-based approaches like NSGD and Auto-S rely on a monotonic weight function, which imposes excessive weight on small gradient samples and introduces extra deviation to the update. In this paper, we propose a Differentially Private Per-Sample Adaptive Clipping (DP-PSAC) algorithm based on a non-monotonic adaptive weight function, which guarantees privacy without the typical hyperparameter tuning process of using a constant clipping while significantly reducing the deviation between the update and true batch-averaged gradient. We provide a rigorous theoretical convergence analysis and show that with convergence rate at the same order, the proposed algorithm achieves a lower non-vanishing bound, which is maintained over training iterations, compared with NSGD/Auto-S. In addition, through extensive experimental evaluation, we show that DP-PSAC outperforms or matches the state-of-the-art methods on multiple main-stream vision and language tasks.

Learning and Management during and after the Pandemic

Abstract This article explores some pedagogical challenges and opportunities introduced by higher education's increased reliance on private learning management systems (LMS) during the COVID-19 pandemic. It theorizes LMS as an expression of neoliberalism and argues that critical literacy, as a method, should be done to (rather than simply through) LMS. Specifically, it examines two case studies of student interactions with the LMS during an asynchronous first-year writing course.

Pathways from information to the adoption of conservation agriculture practices in Malawi and Tanzania

Abstract To reduce agriculture's carbon, land and water footprint, the diffusion of conservation farming methods is one commonly cited proposition. Yet the process of translating available information on new conservation farming methods into farmers' practices is often a black box in many studies. This understanding is critical to inform strategies for scaling these complex, knowledge-intensive, but necessary practices for improving agriculture's resource and climate balance sheet. By implementing a series of mediation analysis using data from 700 households in Malawi and 930 households in Tanzania, this study examines how an improved understanding of conservation agriculture (CA) principles is an important mediator in the pathway from extension contact to the adoption of two of the CA practices examined. For the adoption of conservation tillage, the share of the mediated treatment effect was in the 31.5–34.4% range, while it was 31.6–46.9% for the adoption of soil cover (mulching). Our results suggest that unless learning from external sources strongly correlates with improved farmers' technical understanding of new farming practices, private learning by doing must be a critical adjunct to other avenues of learning. Beyond the basic promotional goals, improving farmers' technical know-how needs to be the centerpiece of holistic efforts in support of conservation farming and similar knowledge-intensive practices necessary for agriculture's sustinability goals.

The Ideology of Democratic Athens: Institutions, Orators and the Mythical Past by Matteo Barbato

Reviewed by: The Ideology of Democratic Athens: Institutions, Orators and the Mythical Past by Matteo Barbato Christine Plastow (bio) Matteo Barbato, The Ideology of Democratic Athens: Institutions, Orators and the Mythical Past. Edinburgh, GB: Edinburgh University Press, 2020. 252 pp. ISBN: 978-1-4744-6642-4. Barbato's book offers a new analysis of Athenian ideology through the application of a New Institutionalist approach to the city's democratic institutions as demonstrated by their use of stories from the mythical past. He argues for a middle ground between Marxist and culturalist understandings, characterising Athenian ideology as value-neutral, flexible, normative, constructive, and bidirectional. This is illustrated through an analysis of the varied presentations of four myths across several institutional contexts: particularly the epitaphioi logoi, but also tragedy, Assembly and forensic speeches, and private genres. An introduction lays out the structure of the book and summarises previous approaches to Athenian ideology; there is a particular focus on the Marxist approach of Nicole Loraux and others, and the culturalist approach illustrated by the work of Josiah Ober. The second chapter explores Athenian knowledge of mythology, identifying the theatre as its main source but also noting the importance of religious festivals such as the Panathenaia, public institutional contexts, and private learning. The third chapter establishes Barbato's theoretical approach, drawing on New Institutionalism to argue that the different democratic institutions of Athens had their own discursive frameworks and that discourse within them was necessarily structured by these: the need to create an imagined community in the funeral speeches; the requirement to argue in favour of justice in the law courts; the principle of advantage for the Athenians in the Assembly, and both justice and advantage in the Council; and the ability to play with the ideological frameworks of other institutions and the need to appeal to a diverse audience at the dramatic festivals. The subsequent four chapters examine the use of four stories from the Athenian mythic past in these institutional contexts and in private genres: the concept of Athenian autochthony, the sheltering of the Heraclidae, the Amazonomachy, and the assistance provided to Adrastus against Thebes. A short conclusion summarises the book's arguments and contextualises its contributions to the study of Athenian ideology, democracy more broadly, and interactions between Classics and political science. [End Page 88] There is much to commend Barbato's book. His analysis of Athenian ideology highlights two important points that are not prominent in the work on the subject to date. First, he emphasises that Athenian ideology was not fixed but fluid and dynamic, and that the presentation of ideological material necessarily differed based on the context in which it was delivered. This is an important point to grasp to understand the Athenians' apparent tendency to contradict themselves from source to source. Barbato successfully illustrates the appropriateness of different perspectives in different institutional contexts. For example, his nuanced analysis of the various versions of the myth of Adrastus presented in Lysias' funeral oration, Euripides' Suppliant Women, and Assembly speeches convincingly shows how the emphasis on or exclusion of certain narrative features—such as the hybris of the Thebans—can be manipulated to evoke aspects of the democratic ideology suitable to the setting. Second, he is right to draw attention to the fact that ideological material not only describes the audience's viewpoint but also moulds it by demonstrating a norm to which they are expected to conform, touching implicitly on an important point regarding the cognitive effects of rhetoric. Indeed, this methodology in combination with a cognitive approach could produce a particularly strong reading: for instance, how was the ideological result affected by the movement of audience members between the institutions and their memory of the different versions they had heard in other arenas? Barbato is working within a particular school of thought in the study of Athenian oratory that proposes that strict expectations of acceptability were in place in the various contexts of public speaking. Indeed, in his conclusion he summarises that the institutionalist reading of fixed discursive parameters in the institutions "corroborates the view that Aristotle's subdivision of the discipline into three genres was based on the observation of actual oratorical practice" (219). While the...

Методы подготовки руководителей с применением когнитивных карт и ситуационного моделирования

Introduction. The methods of managing the process of professional training of managers based on the concept of consistent development of a set of professionally significant qualities by conducting simulation games and exercises with students are considered. At the same time, the formal model of the complete training process of the head is presented in the form of the sum of efforts spent on the implementation of private learning processes that allow students to form a cumulative ability to carry out managerial activities. Accordingly, the training of the head is considered successful, provided that the results of passing all stages of training have an acceptable value. Dynamic modeling of the development of situations using actual modeling scenarios based on real situations, the use of semantically significant terms and concepts familiar to managers from the subject area of management of organizational structures, allows you to bring the model situation as close as possible to the real activity of the manager, due to which it is expected to receive high efficiency of their training. Tasks of the work. Development of methods based on modeling situations characteristic of the activities of managers, and conducting simulation games with students, exercises based on the training software and hardware complex in order to improve the practical training of managers. At the same time, it is proposed to implement intelligent management of the educational process by using cognitive maps, and to use models of organizational systems functioning at the regional level for information support. Methods. The dynamic modeling method based on cognitive maps is applied. Predicate calculus methods, situational management methods, including situational modeling and situational analysis are also applied. Results and discussion. The considered methods increase the practical orientation of training, projecting the intended activity of students on the educational process, in connection with which there is a correction of the content of training sessions in the direction of increasing the expediency of the training. To implement the proposed methods in the educational process, it is proposed to use specialized training software and hardware complexes, in connection with which their composition and main functions are considered. Conclusions. The presented methods make it possible to improve the organization and implementation of the educational process on the basis of a situational approach aimed at improving the effectiveness of training managers and strengthening its practical orientation. Keywords: leadership training; education management; cognitive maps; professionally significant qualities; training complexes; situational approach.

Level of Satisfaction Towards Five Dimension of Service Quality in Private Learning Institutions in Perak

Level of Satisfaction Towards Five Dimension of Service Quality in Private Learning Institutions in Perak

Describing the Ecology of Parenting Based on Preschool Mothers’ Social Relationships in Korea: An Ecological Theory Approach

Mothers' social networks are important to their children's health but still remains poorly understood in Korea. The purpose of the study was to explore the elements of social relationships in Korean preschool mothers to describe their parenting ecology. Data were collected from interviews with 32 mothers according to the Social Network Interview questionnaire and from their seven-day diaries describing their daily social relationships. Eleven ecological elements were identified. The social relationships of these mothers involved seven types of people: relatives, mothers of their children's friends, kindergarten and learning center teachers, domestic workers providing parenting support, husbands, friends, and children's friends. The mothers' social relationships were also associated with four location types: kindergartens, private learning centers, health services centers, and private entertainment centers. The strongest element of the ecology of parenting in terms of frequency was relatives, followed by the mothers of their children's friends and kindergarten teachers; for the location, the strongest was kindergartens, followed by private learning centers and health services centers. In conclusion, the strongest elements in parenting ecology should be evaluated during the assessment of children's growth and development, and incorporated in the assessment tools used.

Mitigating disparate impact on model accuracy in differentially private learning

The techniques based on the theory of differential privacy (DP) have become a standard building block in the machine learning community. The DP training mechanisms offer strong guarantees that an adversary cannot determine with high confidence about the training data based on analyzing the released model, let alone any details of the instances. However, DP may disproportionately affect the underrepresented and relatively complicated classes, which means that the reduction in utility (i.e., model’s accuracy) is unequal for each class. Existing work neglects the adverse impact of DP or omits the influence of hyperparameters on the private learning procedure. This paper proposes a fair differential privacy algorithm (FairDP) to mitigate the disparate impact on each class’s model accuracy. We cast the learning procedure as a bilevel programming problem, which could integrate differential privacy with fairness. FairDP establishes a self-adaptive DP mechanism and dynamically adjusts instance influence in each class depending on the theoretical bias-variance bound with privacy guarantees simultaneously. Our experimental evaluation shows the effectiveness of FairDP in mitigating the disparate impact on model accuracy among the classes on several benchmark datasets and scenarios ranging from text to vision and achieving state-of-the-art accuracy and fairness.

Emergency Online Learning: The Effects of Interactional, Motivational, Self-Regulatory, and Situational Factors on Learning Outcomes and Continuation Intentions

This study investigated the effects of interactional, motivational, self-regulatory, and situational factors on university students’ online learning outcomes and continuation intentions during the COVID-19 pandemic. Data were collected from 255 students taking a business course at a university in southern China. Hierarchical multiple regression analyses revealed that while family financial hardship caused by COVID-19 was a marginally significant negative predictor of students’ learning outcomes, learner–content interaction; instructors’ provision of e-resources, course planning, and organisation; and students’ intrinsic goal orientation and meta-cognitive self-regulation were significant positive predictors with the latter two sets of predictors mediating the effects of learner–instructor and learner–learner interactions, respectively. Multinominal logistic regression analyses showed that learner–instructor interaction, learner–content interaction, and private learning space were significant positive predictors of students’ intentions to continue with online learning, but learner–learner interaction was a significant negative predictor. These findings point to the differential effects of various types of interactional and situational factors on learning outcomes and continuation intentions, and the instructor- and learner-level factors that mediate the effects of learner–instructor and learner–learner interactions on learning outcomes. They contribute to our understandings of emergency online learning and provide implications for facilitating it.

Extending repair in peer interaction: A conversation analytic study.

Peer interaction constitutes a focal site for understanding learning orientations and autonomous learning behaviors. Based on 10 h of video-recorded data collected from small-size conversation-for-learning classes, this study, through the lens of Conversation Analysis, analyzes instances in which L2 learners spontaneously exploit learning opportunities from the on-task public talk and make them relevant for private learning in sequential private peer interaction. The analysis of extended negation-for-meaning practices in peer interaction displays how L2 learners orient to public repair for their learning opportunities in an immediate manner and in so doing, how different participation framework is being utilized to maximize their learning outcomes. As these extended repair practices are entirely managed by learners themselves, they yield both efficient and inefficient learning outcomes. Findings reveal that learners frequently resort to their peers to recycle the focal trouble words for learning opportunities, shifting their participating role from the on looking audience to active learners. By reporting the rather under-researched post-repair negotiation-for-meaning sequence in peer interactions, the study highlights the relevance between on-task classroom activities and private learning, contributing to understanding private learning behaviors in the language classroom and learning as a co-constructed activity locally situated in peer interaction.

TEKNIK KOMUNIKASI DAN TEKNIK PEMBELAJARAN ANAK BERKEBUTUHAN KHUSUS PADA MASA PANDEMI COVID 19

This study aims to describe communication techniques and learning techniques for Children with Special Needs during the Covid19 pandemic at KTK BSS Giwangan Yogyakarta. The research used descriptive qualitative research. The qualitative approach was chosen because this study used social interaction to obtain data from natural data sources. The data source of this research is Children with Special Needs (ABK) or Special Needs at KTK BSS Giwangan Yogyakarta in kindergarten and elementary school levels. From this research, it was found that in KTK BSS Giwangan there are three types of children with special needs, namely speech impaired, autism and learning difficulties. Communication techniques used are: 1) Using private learning methods (one teacher one child); 2) Make eye contact with the child you are talking to; 3) Cultivating a sense of empathy in conversation; 4) Discuss specific and clear topics and 5) Be patient in waiting for answers from children. Different learning techniques from other children. The learning technique used is the learning technique used for the speech impaired using the Indonesian Sign Language System (SIBI) and the Indonesian Sign Language (BISINDO). For children with Autism constraints using 1) Modeling; 2) Latent Learning 3) Give positive praise and 4) Divide all activities step by step. Learning Difficulties using 1) Multisensory Method; 2) Linguistic Method and 3) Glass Analysis

TEKNIK KOMUNIKASI DAN TEKNIK PEMBELAJARAN ANAK BERKEBUTUHAN KHUSUS PADA MASA PANDEMI COVID 19

This study aims to describe communication techniques and learning techniques for Children with Special Needs during the Covid19 pandemic at KTK BSS Giwangan Yogyakarta. The research used descriptive qualitative research. The qualitative approach was chosen because this study used social interaction to obtain data from natural data sources. The data source of this research is Children with Special Needs (ABK) or Special Needs at KTK BSS Giwangan Yogyakarta in kindergarten and elementary school levels. From this research, it was found that in KTK BSS Giwangan there are three types of children with special needs, namely speech impaired, autism and learning difficulties. Communication techniques used are: 1) Using private learning methods (one teacher one child); 2) Make eye contact with the child you are talking to; 3) Cultivating a sense of empathy in conversation; 4) Discuss specific and clear topics and 5) Be patient in waiting for answers from children. Different learning techniques from other children. The learning technique used is the learning technique used for the speech impaired using the Indonesian Sign Language System (SIBI) and the Indonesian Sign Language (BISINDO). For children with Autism constraints using 1) Modeling; 2) Latent Learning 3) Give positive praise and 4) Divide all activities step by step. Learning Difficulties using 1) Multisensory Method; 2) Linguistic Method and 3) Glass Analysis