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It has been a long time quest of artificial intelligence to develop systems that can emulate human reasoning. Fundamental capabilities of such intelligent behavior are the abilities to understand causality and to predict. Those are essential for many artificial intelligence tasks that rely on human common-sense reasoning, such as decision making, planning, question-answering, inferring user intentions and responses. Much of the causal knowledge that helps humans understand the world is recorded in texts that express people's beliefs and intuitions. The World Wide Web encapsulates much of our human knowledge through news archives and encyclopedias. This knowledge can serve as the basis for performing true human-like prediction - with the ability to learn, understand language, and possess intuitions and general world knowledge. In this talk I will present Pundit - a learning system, which given an event, represented in natural language, predicts a possible future event it can cause. During its training, we constructed a semantically-structured causality graph of 30 million fact nodes connected by more than one billion edges, based on 150 year old news archive crawled from the web. We devised a machine learning algorithm that infers causality based on this graph. Using common-sense ontologies, it generalizes the events it observes, and thus able to reason about completely new events. We empirically evaluate our system on the 2010 news, and compare our predictions to human predictions. The results indicate that our system predicts similarly to the way humans do. ©2011 Microsoft Corporation. All rights reserved.

This article is from Journal of NeuroEngineering and Rehabilitation , volume 11 . Abstract Background: Research on the neurophysiological correlates of visuomotor integration and learning (VMIL) has largely focused on identifying learning-induced activity changes in cortical areas during motor execution. While such studies have generated valuable insights into the neural basis of VMIL, little is known about the processes that represent the current state of VMIL independently of motor execution. Here, we present empirical evidence that a subject’s performance in a 3D reaching task can be predicted on a trial-to-trial basis from pre-trial electroencephalographic (EEG) data. This evidence provides novel insights into the brain states that support successful VMIL. Methods: Six healthy subjects, attached to a seven degrees-of-freedom (DoF) robot with their right arm, practiced 3D reaching movements in a virtual space, while an EEG recorded their brain’s electromagnetic field. A random forest ensemble classifier was used to predict the next trial’s performance, as measured by the time needed to reach the goal, from pre-trial data using a leave-one-subject-out cross-validation procedure. Results: The learned models successfully generalized to novel subjects. An analysis of the brain regions, on which the models based their predictions, revealed areas matching prevalent motor learning models. In these brain areas, the α/μ frequency band (8–14 Hz) was found to be most relevant for performance prediction. Conclusions: VMIL induces changes in cortical processes that extend beyond motor execution, indicating a more complex role of these processes than previously assumed. Our results further suggest that the capability of subjects to modulate their α/μ bandpower in brain regions associated with motor learning may be related to performance in VMIL. Accordingly, training subjects in α/μ-modulation, e.g., by means of a brain-computer interface (BCI), may have a beneficial impact on VMIL.

Over the past decades, statisticians and machine-learning researchers have developed literally thousands of new tools for the reduction of high-dimensional data in order to identify the variables most responsible for a particular trait. These tools have applications in a plethora of settings, including data analysis in the fields of business, education, forensics, and biology (such as microarray, proteomics, brain imaging), to name a few. In the present work, we focus our investigation on the limitations and potential misuses of certain tools in the analysis of the benchmark colon cancer data (2,000 variables; Alon et al., 1999) and the prostate cancer data (6,033 variables; Efron, 2010, 2008). Our analysis demonstrates that models that produce 100% accuracy measures often select different sets of genes and cannot stand the scrutiny of parameter estimates and model stability. Furthermore, we created a host of simulation datasets and "artificial diseases" to evaluate the reliability of commonly used statistical and data mining tools. We found that certain widely used models can classify the data with 100% accuracy without using any of the variables responsible for the disease. With moderate sample size and suitable pre-screening, stochastic gradient boosting will be shown to be a superior model for gene selection and variable screening from high-dimensional datasets.

Over the past decades, statisticians and machine-learning researchers have developed literally thousands of new tools for the reduction of high-dimensional data in order to identify the variables most responsible for a particular trait. These tools have applications in a plethora of settings, including data analysis in the fields of business, education, forensics, and biology (such as microarray, proteomics, brain imaging), to name a few. In the present work, we focus our investigation on the limitations and potential misuses of certain tools in the analysis of the benchmark colon cancer data (2,000 variables; Alon et al., 1999) and the prostate cancer data (6,033 variables; Efron, 2010, 2008). Our analysis demonstrates that models that produce 100% accuracy measures often select different sets of genes and cannot stand the scrutiny of parameter estimates and model stability. Furthermore, we created a host of simulation datasets and "artificial diseases" to evaluate the reliability of commonly used statistical and data mining tools. We found that certain widely used models can classify the data with 100% accuracy without using any of the variables responsible for the disease. With moderate sample size and suitable pre-screening, stochastic gradient boosting will be shown to be a superior model for gene selection and variable screening from high-dimensional datasets.

Over the past decades, statisticians and machine-learning researchers have developed literally thousands of new tools for the reduction of high-dimensional data in order to identify the variables most responsible for a particular trait. These tools have applications in a plethora of settings, including data analysis in the fields of business, education, forensics, and biology (such as microarray, proteomics, brain imaging), to name a few. In the present work, we focus our investigation on the limitations and potential misuses of certain tools in the analysis of the benchmark colon cancer data (2,000 variables; Alon et al., 1999) and the prostate cancer data (6,033 variables; Efron, 2010, 2008). Our analysis demonstrates that models that produce 100% accuracy measures often select different sets of genes and cannot stand the scrutiny of parameter estimates and model stability. Furthermore, we created a host of simulation datasets and "artificial diseases" to evaluate the reliability of commonly used statistical and data mining tools. We found that certain widely used models can classify the data with 100% accuracy without using any of the variables responsible for the disease. With moderate sample size and suitable pre-screening, stochastic gradient boosting will be shown to be a superior model for gene selection and variable screening from high-dimensional datasets.

We show that the task of question answering (QA) can significantly benefit from the transfer learning of models trained on a different large, fine-grained QA dataset. We achieve the state of the art in two well-studied QA datasets, WikiQA and SemEval-2016 (Task 3A), through a basic transfer learning technique from SQuAD. For WikiQA, our model outperforms the previous best model by more than 8%. We demonstrate that finer supervision provides better guidance for learning lexical and syntactic information than coarser supervision, through quantitative results and visual analysis. We also show that a similar transfer learning procedure achieves the state of the art on an entailment task.

Some machine learning applications involve training data that is sensitive, such as the medical histories of patients in a clinical trial. A model may inadvertently and implicitly store some of its training data; careful analysis of the model may therefore reveal sensitive information. To address this problem, we demonstrate a generally applicable approach to providing strong privacy guarantees for training data: Private Aggregation of Teacher Ensembles (PATE). The approach combines, in a black-box fashion, multiple models trained with disjoint datasets, such as records from different subsets of users. Because they rely directly on sensitive data, these models are not published, but instead used as "teachers" for a "student" model. The student learns to predict an output chosen by noisy voting among all of the teachers, and cannot directly access an individual teacher or the underlying data or parameters. The student's privacy properties can be understood both intuitively (since no single teacher and thus no single dataset dictates the student's training) and formally, in terms of differential privacy. These properties hold even if an adversary can not only query the student but also inspect its internal workings. Compared with previous work, the approach imposes only weak assumptions on how teachers are trained: it applies to any model, including non-convex models like DNNs. We achieve state-of-the-art privacy/utility trade-offs on MNIST and SVHN thanks to an improved privacy analysis and semi-supervised learning.

We describe a method for automatic word sense disambiguation using a text corpus and a machine-readable dictionary (MRD). The method is based on word similarity and context similarity measures. Words are considered similar if they appear in similar contexts; contexts are similar if they contain similar words. The circularity of this definition is resolved by an iterative, converging process, in which the system learns from the corpus a set of typical usages for each of the senses of the polysemous word listed in the MRD. A new instance of a polysemous word is assigned the sense associated with the typical usage most similar to its context. Experiments show that this method performs well, and can learn even from very sparse training data.

The present study introduces a method for improving the classification performance of imbalanced multiclass data streams from wireless body worn sensors. Data imbalance is an inherent problem in activity recognition caused by the irregular time distribution of activities, which are sequential and dependent on previous movements. We use conditional random fields (CRF), a graphical model for structured classification, to take advantage of dependencies between activities in a sequence. However, CRFs do not consider the negative effects of class imbalance during training. We propose a class-wise dynamically weighted CRF (dWCRF) where weights are automatically determined during training by maximizing the expected overall F-score. Our results based on three case studies from a healthcare application using a batteryless body worn sensor, demonstrate that our method, in general, improves overall and minority class F-score when compared to other CRF based classifiers and achieves similar or better overall and class-wise performance when compared to SVM based classifiers under conditions of limited training data. We also confirm the performance of our approach using an additional battery powered body worn sensor dataset, achieving similar results in cases of high class imbalance.

We consider the problem of estimating the class prior in an unlabeled dataset. Under the assumption that an additional labeled dataset is available, the class prior can be estimated by fitting a mixture of class-wise data distributions to the unlabeled data distribution. However, in practice, such an additional labeled dataset is often not available. In this paper, we show that, with additional samples coming only from the positive class, the class prior of the unlabeled dataset can be estimated correctly. Our key idea is to use properly penalized divergences for model fitting to cancel the error caused by the absence of negative samples. We further show that the use of the penalized $L_1$-distance gives a computationally efficient algorithm with an analytic solution. The consistency, stability, and estimation error are theoretically analyzed. Finally, we experimentally demonstrate the usefulness of the proposed method.

We consider the setting where a collection of time series, modeled as random processes, evolve in a causal manner, and one is interested in learning the graph governing the relationships of these processes. A special case of wide interest and applicability is the setting where the noise is Gaussian and relationships are Markov and linear. We study this setting with two additional features: firstly, each random process has a hidden (latent) state, which we use to model the internal memory possessed by the variables (similar to hidden Markov models). Secondly, each variable can depend on its latent memory state through a random lag (rather than a fixed lag), thus modeling memory recall with differing lags at distinct times. Under this setting, we develop an estimator and prove that under a genericity assumption, the parameters of the model can be learned consistently. We also propose a practical adaption of this estimator, which demonstrates significant performance gains in both synthetic and real-world datasets.

CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data. In this paper, we introduce an industrial strength solution with model named Large Scale Piece-wise Linear Model (LS-PLM). We formulate the learning problem with $L_1$ and $L_{2,1}$ regularizers, leading to a non-convex and non-smooth optimization problem. Then, we propose a novel algorithm to solve it efficiently, based on directional derivatives and quasi-Newton method. In addition, we design a distributed system which can run on hundreds of machines parallel and provides us with the industrial scalability. LS-PLM model can capture nonlinear patterns from massive sparse data, saving us from heavy feature engineering jobs. Since 2012, LS-PLM has become the main CTR prediction model in Alibaba's online display advertising system, serving hundreds of millions users every day.

Transformation of data into knowledge through conceptual induction has been the focus of our research described in this paper. We have developed a Machine Learning System (MLS) to analyze the rocket engine simulation data. MLS can provide to its users fault analysis, characteristics, and conceptual descriptions of faults, and the relationships of attributes and sensors. All the results are critically important in identifying faults.

Neural network models of early sensory processing typically reduce the dimensionality of streaming input data. Such networks learn the principal subspace, in the sense of principal component analysis (PCA), by adjusting synaptic weights according to activity-dependent learning rules. When derived from a principled cost function these rules are nonlocal and hence biologically implausible. At the same time, biologically plausible local rules have been postulated rather than derived from a principled cost function. Here, to bridge this gap, we derive a biologically plausible network for subspace learning on streaming data by minimizing a principled cost function. In a departure from previous work, where cost was quantified by the representation, or reconstruction, error, we adopt a multidimensional scaling (MDS) cost function for streaming data. The resulting algorithm relies only on biologically plausible Hebbian and anti-Hebbian local learning rules. In a stochastic setting, synaptic weights converge to a stationary state which projects the input data onto the principal subspace. If the data are generated by a nonstationary distribution, the network can track the principal subspace. Thus, our result makes a step towards an algorithmic theory of neural computation.

People can recognize scenes across many different modalities beyond natural images. In this paper, we investigate how to learn cross-modal scene representations that transfer across modalities. To study this problem, we introduce a new cross-modal scene dataset. While convolutional neural networks can categorize cross-modal scenes well, they also learn an intermediate representation not aligned across modalities, which is undesirable for cross-modal transfer applications. We present methods to regularize cross-modal convolutional neural networks so that they have a shared representation that is agnostic of the modality. Our experiments suggest that our scene representation can help transfer representations across modalities for retrieval. Moreover, our visualizations suggest that units emerge in the shared representation that tend to activate on consistent concepts independently of the modality.

The primary objective of this paper is to revisit a widely held view that decision theory provides a unifying framework for comparing the frequentist and Bayesian approaches by bringing into focus their common features and neutralizing their differences using a common terminology like decision rules, action spaces, loss and risk functions, admissibility, etc. The paper calls into question this viewpoint and argues that the decision theoretic perspective misrepresents the frequentist viewpoint primarily because the notions of expected loss and admissibility are inappropriate for frequentist inference; they do not represent legitimate error probabilities that calibrate the reliability of inference procedures. In a nutshell, the decision theoreric framing is applicable to what R. A. Fisher called "acceptance sampling", where the decisions revolve around a loss function originating in information `other than the data'. Frequentist inference is germane to scientific inference where the objective is to learn from data about the 'true' data generating mechanism.

This discussion argues that additional information can be obtained from the Follow Through program in its current framework. Three ways of generating new knowledge from Follow Through are suggested: (1) secondary analysis of extant data, (2) compilation and analysis of followup data, and (3) completely new studies. Two ways of minimizing costs through the use of existing data on children who have already completed Follow Through programs are also mentioned. In addition, the discussion reviews and comments on some of the suggestions for Follow Through research studies that have been proposed in the 4 years since completion of the national evaluation. (RH)

Group size and treatment intensity are understudied topics in mathematics intervention research. This study examined whether the treatment intensity and overall intervention effects of an empirically-validated Tier 2 mathematics intervention varied between intervention groups with 2:1 and 5:1 student-teacher ratios. Student practice opportunities and the quality of explicit instruction served as treatment intensity metrics. A total of 465 kindergarten students with mathematics difficulties from 136 intervention groups participated. Results suggested comparable performances between the 2:1 and 5:1 intervention groups on six outcome measures. Observation data indicated that the intensity of student practice opportunities differed by group size. Students in the 5:1 groups received more opportunities to practice with their peers, while students in the 2:1 groups participated in more frequent and higher quality individualized practice opportunities. Implications in terms of delivering Tier 2 interventions in small-group formats and engaging at-risk learners in meaningful practice opportunities are discussed. [This paper was published in "Journal of Learning Disabilities" v52 n2 p168-180 Mar 2019 (EJ1203634). The published article was titled "Examining the Impact of Group Size on the Treatment Intensity of a Tier 2 Mathematics Intervention within a Systematic Framework of Replication."]

This study uses data from semi-structured interviews conducted with 44 early childhood education (ECE) staff and examines how knowledge dissemination processes operate in ECE centers, including how information from off-site trainings is diffused among staff. Our sample includes administrators, lead teachers, and assistant teachers serving children aged zero to five in a large ethnically-diverse urban district. We find that staff reported exchanging information through formal channels (e.g., scheduled staff meetings) and informal channels (e.g., extemporaneous meetings, advice-seeking interactions); our findings suggest that informal channels may be especially prevalent and consequential to ECE staff's professional learning. ECE professionals explained that they sought certain colleagues for information/advice primarily based on the colleague's expertise but also because of a colleague's job title and their familiarity with that colleague. Lastly, we find that nearly half of staff reported sharing information they received from off-site professional development with colleagues at their ECE center. [This paper was published in "Journal of Early Childhood Teacher Education" v43 n4 p554-567 2022.]

This study uses data from semi-structured interviews conducted with 44 early childhood education (ECE) staff and examines how knowledge dissemination processes operate in ECE centers, including how information from off-site trainings is diffused among staff. Our sample includes administrators, lead teachers, and assistant teachers serving children aged zero to five in a large ethnically-diverse urban district. We find that staff reported exchanging information through formal channels (e.g., scheduled staff meetings) and informal channels (e.g., extemporaneous meetings, advice-seeking interactions); our findings suggest that informal channels may be especially prevalent and consequential to ECE staff's professional learning. ECE professionals explained that they sought certain colleagues for information/advice primarily based on the colleague's expertise but also because of a colleague's job title and their familiarity with that colleague. Lastly, we find that nearly half of staff reported sharing information they received from off-site professional development with colleagues at their ECE center. [This paper was published in "Journal of Early Childhood Teacher Education" v43 n4 p554-567 2022.]

Forecasting solar still performance from conventional weather data variation by machine learning method 作者： 高文杰 1 作者单位： 1. 华中科技大学 提交时间： 2022-05-30 摘要: Solar stills are considered an effective method to solve the scarcity of drinkable water. However, it is still missing a way to forecast its production. Herein, it is proposed that a convenient forecasting model which just needs to input the conventional weather forecasting data. The model is established by using machine learning methods of random forest and optimized by Bayesian algorithm. The required data to train the model is obtained from daily measurements lasting 9 months. To validate the accuracy model, the determination coefficients of two types of solar stills are calculated as 0.935 and 0.929, respectively, which are much higher than the value of both multiple linear regression (0.767) and the traditional models (0.829 and 0.847). Moreover, by appling the model, it is predicted that the freshwater production of four cities in China. The predicted production is approved to be reliable by a high value of correlation (0.868) between the predicted production and the solar insolation. With the help of the forecasting model, it would greatly promote the global application of solar stills. Solar still Production forecasting Forecasting model Weather data Random forest 来自： 孙森山 分类： 能源科学 >> 能源（综合） 引用： ChinaXiv:202205.00175 (或此版本 ChinaXiv:202205.00175V1 ) doi:10.12074/202205.00175 CSTR:32003.36.ChinaXiv.202205.00175.V1 推荐引用方式： 高文杰.(2022).Forecasting solar still performance from conventional weather data variation by machine learning method.中国科学院科技论文预发布平台.[ChinaXiv:202205.00175] 版本历史 [V1] 2022-05-30 15:52:33 ChinaXiv:202205.00175V1 下载全文

Little is known about when and whether people infer hidden causes on the basis of temporal data. This study aims to explore whether and how people use temporal information to infer causal structure, where the options include the the possibility of a common hidden cause. Building on a previous experiment (pre-registered; Valentin et al., 2021), the aim of this experiment is to replicate the previous findings and extend the experimental design with three additional between-participant conditions that differ in terms of the cover story and context information given to the participants, in order to cover different domains and assess their influence on people's inferences. Valentin, S., Bramley, N. R., & Lucas, C. G. (2021, March 18). Learning Hidden Causal Structure From Temporal Data: Experiment 1. Retrieved from osf.io/e5d23

We propose a method for using synthetic data to help learning classifiers. Synthetic data, even is generated based on real data, normally results in a shift from the distribution of real data in feature space. To bridge the gap between the real and synthetic data, and jointly learn from synthetic and real data, this paper proposes a Multichannel Autoencoder(MCAE). We show that by suing MCAE, it is possible to learn a better feature representation for classification. To evaluate the proposed approach, we conduct experiments on two types of datasets. Experimental results on two datasets validate the efficiency of our MCAE model and our methodology of generating synthetic data.

This paper addresses the problem of learning a Nash equilibrium in $\gamma$-discounted multiplayer general-sum Markov Games (MG). A key component of this model is the possibility for the players to either collaborate or team apart to increase their rewards. Building an artificial player for general-sum MGs implies to learn more complex strategies which are impossible to obtain by using techniques developed for two-player zero-sum MGs. In this paper, we introduce a new definition of $\epsilon$-Nash equilibrium in MGs which grasps the strategy's quality for multiplayer games. We prove that minimizing the norm of two Bellman-like residuals implies the convergence to such an $\epsilon$-Nash equilibrium. Then, we show that minimizing an empirical estimate of the $L_p$ norm of these Bellman-like residuals allows learning for general-sum games within the batch setting. Finally, we introduce a neural network architecture named NashNetwork that successfully learns a Nash equilibrium in a generic multiplayer general-sum turn-based MG.

This paper addresses the problem of learning a Nash equilibrium in $\gamma$-discounted multiplayer general-sum Markov Games (MG). A key component of this model is the possibility for the players to either collaborate or team apart to increase their rewards. Building an artificial player for general-sum MGs implies to learn more complex strategies which are impossible to obtain by using techniques developed for two-player zero-sum MGs. In this paper, we introduce a new definition of $\epsilon$-Nash equilibrium in MGs which grasps the strategy's quality for multiplayer games. We prove that minimizing the norm of two Bellman-like residuals implies the convergence to such an $\epsilon$-Nash equilibrium. Then, we show that minimizing an empirical estimate of the $L_p$ norm of these Bellman-like residuals allows learning for general-sum games within the batch setting. Finally, we introduce a neural network architecture named NashNetwork that successfully learns a Nash equilibrium in a generic multiplayer general-sum turn-based MG.

This paper addresses the problem of learning a Nash equilibrium in $\gamma$-discounted multiplayer general-sum Markov Games (MG). A key component of this model is the possibility for the players to either collaborate or team apart to increase their rewards. Building an artificial player for general-sum MGs implies to learn more complex strategies which are impossible to obtain by using techniques developed for two-player zero-sum MGs. In this paper, we introduce a new definition of $\epsilon$-Nash equilibrium in MGs which grasps the strategy's quality for multiplayer games. We prove that minimizing the norm of two Bellman-like residuals implies the convergence to such an $\epsilon$-Nash equilibrium. Then, we show that minimizing an empirical estimate of the $L_p$ norm of these Bellman-like residuals allows learning for general-sum games within the batch setting. Finally, we introduce a neural network architecture named NashNetwork that successfully learns a Nash equilibrium in a generic multiplayer general-sum turn-based MG.

This study aims to find out the student responses in enhancing new vocabulary through subtitled English Movies. And the research question is what are students' responses in enhancing new vocabulary through subtitled English movies? To achieve this objective, the study employed a quantitative method. The data were obtained from the questionnaire. The questionnaire was distributed to the Universitas Advent Indonesia. The data were then descriptively analyzed. The result of this study indicated that most of the students responded positively through subtitled English movies in enhancing new vocabulary. These research findings are expected to contribute to the efforts in the teaching and learning area, particularly in increasing students' vocabulary.

This archived entry captures the professional journey of Bhardwaj K Mohare , a graduate of the Postgraduate Program in Data Science and Analytics at Imarticus Learning . Motivated by a deep interest in data and its real-world impact, Bhardwaj set out to acquire the skills necessary to build a future in analytics and technology. Through a rigorous and industry-aligned curriculum, he gained hands-on experience in key tools and platforms such as Python , Power BI , Tableau , SQL , and Machine Learning . The program offered practical learning through live projects, expert mentorship, and real-time problem solving. One of the highlights of the program was its 100% job assurance , including comprehensive career support—resume workshops, mock interviews, and placement assistance—that helped Bhardwaj secure a role in the data science domain. His story stands as an important example of how structured, outcome-focused education can drive career change and open new professional opportunities in a data-driven world.

This brief is part of a broad landscape analysis focused on policy and practice issues related to the recognition of prior learning. The landscape analysis focuses on issues arising in the practice of the recognition of prior learning, policies that encourage or limit its adoption, and key research needs and future directions for the field. This brief draws on data from a survey administered to 1,184 current undergraduate students and from interviews with six college students. The survey asked the undergraduate students about their prior learning experiences, access to and use of prior learning assessment programs, and their perspectives about their experiences. The interview protocol asked the six college students about their background, their previous work and college experience, their future career and education goals, and their experience with and perspectives on prior learning assessment (PLA). Key highlights of this brief include: (1) Students gain college-level learning outside of the classroom in a variety of ways. Adult learners in this study were more likely to report work experience, completing a certificate or professional license, or enlisting in the military; students under the age of 25 were more likely to report having taken an AP/IB course or volunteer experience; (2) Students cited conversations with individuals (such as high school counselors, counselors or academic advisors on the college campus, other students, or family members) as the main sources of knowledge about PLA, not written material on college catalog; (3) Students cited time and cost savings as two benefits to their college career from PLA. Adult learners also cited benefits to their careers; and (4) Students cited lack of information about PLA as a top barrier to accessing PLA. Adult learners cited money and time required with more frequency than younger students. Younger students cited credit limitations (such as number of credits eligible, how/if a student can apply the credits to their program of study) at a higher rate than older students.

In summer 2021, as exams could not take place, GCSE, AS and A level grades in England were awarded by teachers, in accordance with relatively broad official guidance. This guidance stressed that grades had to be based on evidence of candidate work, though what this was, how much was needed or where/when it should come from were not tightly specified. This was to deal with variations in teaching and learning across centres as a consequence of the variable impact of the COVID-19 pandemic. The quality of these teacher assessed grades (TAGs) was assured by awarding organisations by sampling a selection of the evidence used. This report looks at samples for GCSE Mathematics and English Language, to try to get an understanding of what this evidence looked like at different centres, how it varied, and how different centres combined evidence to come up with final grades. The data inspected was hugely varied in terms of the detail centres offered on what evidence was used to determine grades and how it was brought together. The report concludes that, while the TAGs process provided assessment outcomes to candidates in what was a difficult situation and that these grades were on the whole accepted by stakeholders and wider society (at least compared to the situation in 2020), there are questions about comparability of standards between centres because of the level of variation found. The report ends with four recommendations for improving possible future teacher assessment processes to enhance consistency, efficiency and comparability of standards. [The title on the report cover differs from the suggested citation. Title on cover: "What Do We Know about the Evidence Sources Teachers Used to Determine Teacher Assessed Grades? "]

Objectives: This report presents national estimates of the prevalence of diagnosed attention deficit hyperactivity disorder (ADHD) and learning disability (LD) in U.S. children 6-17 years of age and describes the prevalence of these conditions for children with selected characteristics. The use of educational and health care services and the prevalence of other health conditions are contrasted for children with ADHD without LD, LD without ADHD, both conditions, and neither condition. Methods: Estimates are based on data from the National Health Interview Survey (NHIS), an ongoing national household survey of the civilian non-institutionalized population of the United States. The analysis focuses on 23,051 children 6-17 years of age in the child sample of the 2004, 2005, and 2006 NHIS. Results: About 5% of children had ADHD without LD, 5% had LD without ADHD, and 4% had both conditions. Boys were more likely than girls to have each of the diagnoses (ADHD without LD, LD without ADHD, and both conditions). Children 12-17 years of age were more likely than children 6-11 years of age to have each of the diagnoses. Hispanic children were less likely than non-Hispanic white and non-Hispanic black children to have ADHD (with and without LD). Children with Medicaid coverage were more likely than uninsured children and privately insured children to have each of the diagnoses. Children with each of the diagnoses were more likely than children with neither ADHD nor LD to have other health conditions. Children with ADHD were more likely than children without ADHD to have contact with a mental health professional, use prescription medication, and have frequent health care visits. Children with LD were more likely than children without LD to use special education services. (Contains 9 figures and 4 tables.)

Extracting the underlying low-dimensional space where high-dimensional signals often reside has long been at the center of numerous algorithms in the signal processing and machine learning literature during the past few decades. At the same time, working with incomplete (partly observed) large scale datasets has recently been commonplace for diverse reasons. This so called {\it big data era} we are currently living calls for devising online subspace learning algorithms that can suitably handle incomplete data. Their envisaged objective is to {\it recursively} estimate the unknown subspace by processing streaming data sequentially, thus reducing computational complexity, while obviating the need for storing the whole dataset in memory. In this paper, an online variational Bayes subspace learning algorithm from partial observations is presented. To account for the unawareness of the true rank of the subspace, commonly met in practice, low-rankness is explicitly imposed on the sought subspace data matrix by exploiting sparse Bayesian learning principles. Moreover, sparsity, {\it simultaneously} to low-rankness, is favored on the subspace matrix by the sophisticated hierarchical Bayesian scheme that is adopted. In doing so, the proposed algorithm becomes adept in dealing with applications whereby the underlying subspace may be also sparse, as, e.g., in sparse dictionary learning problems. As shown, the new subspace tracking scheme outperforms its state-of-the-art counterparts in terms of estimation accuracy, in a variety of experiments conducted on simulated and real data.

The annotated bibliography on Sex Education contains approximately 135 abstracts and associated indexing information for documents or journal articles published from 1962 to 1975 and selected from the computer files of the Council for Exceptional Children's Information Services and the Education Resources Information Center (ERIC). It is explained that titles were chosen in response to user requests and analysis of current trends in the field. The bibliography is divided into the following sections: Handicapped Children, Aurally Handicapped, Disadvantaged, Emotionally Disturbed, Mentally Handicapped, Multiply Handicapped, Physically Handicapped, Visually Handicapped, and Learning Disabled. Abstracts include bibliographic data (identification or order number, publication date, author, title, source or publisher, and availability); descriptors indicating the subject matter covered; and a summary of the document's contents. Also provided are instructions for using the bibliography, a list of journals from which articles were abstracted, and an order form for ordering microfiche or paper copies of the documents through the ERIC Document Reproduction Service. (PM)

The UNESCO Institute for Lifelong Learning (UIL) has just published its fifth policy brief, entitled "Making Large-Scale Literacy Campaigns and Programmes Work." The brief provides policymakers with a set of recommendations based on an analysis of adult literacy campaigns and programmes that took place around the world between 2000 and 2014. Despite a resurgence in the popularity of literacy campaigns as a means of mobilizing political will, resources and people, the analysis finds that most large-scale campaigns failed to achieve their overly ambitious targets. The policy brief's key message is therefore that the literacy challenge needs to be addressed from a lifelong learning perspective. This will help policymakers to achieve the literacy target of the new global education agenda, Education 2030. Taking into account the complexity of the literacy task ahead, the policy brief recommends linking literacy campaigns to social change and mobilization; ensuring adequate investment; integrating literacy into holistic learning systems; making systematic use of technology; and improving the quality of literacy data.

One of the key commitments of the Community College Research Initiatives (CCRI's) STEM Transfer Partnership (STP) program is to transform STEM transfer pathways and improve outcomes for students from low-income backgrounds using student input. Student input is an essential element in institutional transformation for student success but the process of cultivating student input involves creative rethinking of data collection strategies. This data note documents the collaborative work of community college and university partnerships to collect student input and translate that data into improvements in the STEM transfer pathway. The authors find that these partnerships are developing contextually responsive, multifaceted strategies for incorporating student input that prioritize student engagement and clarifying information systems.

Workforce development organizations often find it challenging to assess how former program participants are faring in the labor market, since they need to rely on participants' willingness to report and verify their job placements after they either leave or complete their programs. The 2013 Unemployment Insurance Data Sharing Bill (S5773A) amended the New York State Labor Law to make it easier for government agencies to obtain state unemployment insurance (UI) wage data for program monitoring, improvement, and evaluation purposes. In 2016, the Change Capital Fund (CCF) and the New York City Mayor's Office for Economic Opportunity (NYC Opportunity) identified the law as a chance to invest in a demonstration with four community development organizations that were already CCF grantees, aiming to expand their ability to collect and use data to improve their programs in coordination with the city government. This report describes that two-year demonstration and is meant to serve as a guide for other New York municipalities and community organizations that may consider requesting access to state UI wage data. It illustrates some of the challenges and opportunities involved in accessing UI data on program participants and offers some practical lessons for organizations in New York State.

Identifying appropriate data representation is critical to many decision making problems. In this project, we focus on learning kernel-based data representation from noisy data and uncertain information. Unlike conventional approaches that represent objects by vectors, kernel representation defines a pairwise similarity between two objects, and is convenient for representing complex objects like graphs. Although many studies are devoted to learning kernel representation, none of them addresses the challenge of learning kernel representation from noisy data and uncertain information. The proposed research aims to address this challenging problem by developing (i) a kernel learning framework that are robust to data noise and information uncertainty, and (ii) efficient algorithms to solve the related optimization problems. The proposed algorithms will be evaluated in the object recognition domain. The impact of the proposed research to the US Army is significant. To counter against future threats to the safety and security of our society, we need to enhance our capabilities to detect, locate, and track such threats by extracting and representing data from noisy observation and uncertain information. The proposed research seeks to significantly advance, both theoretically and computationally, the representation and modeling of information from noisy and uncertain sources.

"The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors."

Two minute video that uses a simple light bulb example to demonstrate both how it is possible to have causation without correlation and also why machine learning - no matter how much data you have - can never learn the causal relationship.

The manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi-)automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic ontology relations. It combines corpus-based techniques with reasoning on Semantic Web data. Corpus-based methods apply vector space similarity of verbs co-occurring with labeled and unlabeled relations to calculate relation label suggestions from a set of candidates. A meta ontology in combination with Semantic Web sources such as DBpedia and OpenCyc allows reasoning to improve the suggested labels. An extensive formal evaluation demonstrates the superior accuracy of the presented hybrid approach.

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This article is from Bioinformatics , volume 28 . Abstract Motivation: Recent advances in brain imaging and high-throughput genotyping techniques enable new approaches to study the influence of genetic and anatomical variations on brain functions and disorders. Traditional association studies typically perform independent and pairwise analysis among neuroimaging measures, cognitive scores and disease status, and ignore the important underlying interacting relationships between these units.Results: To overcome this limitation, in this article, we propose a new sparse multimodal multitask learning method to reveal complex relationships from gene to brain to symptom. Our main contributions are three-fold: (i) introducing combined structured sparsity regularizations into multimodal multitask learning to integrate multidimensional heterogeneous imaging genetics data and identify multimodal biomarkers; (ii) utilizing a joint classification and regression learning model to identify disease-sensitive and cognition-relevant biomarkers; (iii) deriving a new efficient optimization algorithm to solve our non-smooth objective function and providing rigorous theoretical analysis on the global optimum convergency. Using the imaging genetics data from the Alzheimer's Disease Neuroimaging Initiative database, the effectiveness of the proposed method is demonstrated by clearly improved performance on predicting both cognitive scores and disease status. The identified multimodal biomarkers could predict not only disease status but also cognitive function to help elucidate the biological pathway from gene to brain structure and function, and to cognition and disease.Availability: Software is publicly available at: http://ranger.uta.edu/%7eheng/multimodal/Contact:[email protected]; [email protected]

This report presents a review of studies and articles on employer-sponsored training in Canada. The authors reviewed documentation that used data from the Workplace and Employee Survey (WES) and offer a synthesis of the current state of knowledge. The report looks alternately at issues pertaining to determinants of training from the employer and employee perspectives. From each perspective, summarized results are presented regarding returns on training, their variability across industries, occupations and other characteristics, key barriers to training, and the types of training and supports that are provided by the employer. Knowledge gaps are identified. An appendix includes Synthesis Tables of the Results. (Contains 2 footnotes and 3 tables.) [This report was prepared for the Learning Policy Directorate, Strategic Policy and Research, Human Resources and Social Development Canada and is available in French under the title: "La formation parrainee par les employeurs au Canada: Synthese de la documentation a l'aide de donnees extraites de l'Enquete sur le milieu de travail et les employes."]

This is a summative report based on Year Three data from the MSP Grant Project entitled, "Content Development for Investigations" (CoDE:I). The purpose of the MSP grant program was to develop standards-based elementary mathematics teachers by giving teachers the tools to teach with a new standards-based mathematics curriculum, "Investigations in Number, Data, and Space" ("Investigations"). The participants were teachers in two school systems located near a large metropolitan city in the southeastern United States. System One is a large, urban school system and System Two is a smaller suburban school system in a neighboring city. The two school systems conducted professional development separately and on different days throughout the grant program, but the overall content and focus of the professional development remained consistent. The professional development facilitators worked with both groups of teachers. Since the MSP project is not a longitudinal design, teachers participants exit from the program at the end of the academic year. The focus of this report is to examine the impacts of the professional development on teacher beliefs, practices, mathematics content knowledge, and student learning outcomes.

From observational data alone, a causal DAG is only identifiable up to Markov equivalence. Interventional data generally improves identifiability; however, the gain of an intervention strongly depends on the intervention target, that is, the intervened variables. We present active learning (that is, optimal experimental design) strategies calculating optimal interventions for two different learning goals. The first one is a greedy approach using single-vertex interventions that maximizes the number of edges that can be oriented after each intervention. The second one yields in polynomial time a minimum set of targets of arbitrary size that guarantees full identifiability. This second approach proves a conjecture of Eberhardt (2008) indicating the number of unbounded intervention targets which is sufficient and in the worst case necessary for full identifiability. In a simulation study, we compare our two active learning approaches to random interventions and an existing approach, and analyze the influence of estimation errors on the overall performance of active learning.

From observational data alone, a causal DAG is only identifiable up to Markov equivalence. Interventional data generally improves identifiability; however, the gain of an intervention strongly depends on the intervention target, that is, the intervened variables. We present active learning (that is, optimal experimental design) strategies calculating optimal interventions for two different learning goals. The first one is a greedy approach using single-vertex interventions that maximizes the number of edges that can be oriented after each intervention. The second one yields in polynomial time a minimum set of targets of arbitrary size that guarantees full identifiability. This second approach proves a conjecture of Eberhardt (2008) indicating the number of unbounded intervention targets which is sufficient and in the worst case necessary for full identifiability. In a simulation study, we compare our two active learning approaches to random interventions and an existing approach, and analyze the influence of estimation errors on the overall performance of active learning.

Stress in humans is associated with a variety of mental and physical health outcomes as well as functioning and performance in the work setting. Measuring stress in a continuous and non-intrusive way by using wearable technology and smartphones, can potentially serve as an early warning system for long- and short-term effects on health and performance. The increasing ubiquity of mobile phones has made it easier than ever to collect voice data in naturalistic, real-world settings. While most prior research on stress detection from voice has been conducted in lab settings, this study takes a novel approach by analyzing voice recordings collected in real-life conditions. Specifically, we conduct a secondary analysis of data from a previous study that tested the usability and feasibility of collecting daily stress measurements via semi-automated WhatsApp conversations. That study demonstrated the practicality of using mobile messaging to gather self-reported stress data and voice messages at the end of participants’ workdays over the course of two weeks. Similarly, recent advances in computational power and ML techniques have opened new possibilities for analyzing more complex data in mental health research. By leveraging these tools, we aim to evaluate the predictive performance of different modeling approaches and assess the potential of voice-based stress detection in real-world settings—contributing to the development of scalable, passive mental health monitoring systems. Therefore, the current study has two main objectives: 1) To examine whether there is a relationship between voice features recorded under natural circumstances and self-reported stress levels in a population of working adults and 2) to compare the performance of traditional regression models and machine learning (ML) approaches in predicting stress from voice data.

Sequencing items in adaptive learning systems typically relies on a large pool of interactive question items that are analyzed into a hierarchy of skills, also known as Knowledge Components (KCs). Educational data mining techniques can be used to analyze students response data in order to optimize the mapping of items to KCs, with similarity-based clustering as one of the two main approaches for this type of analysis. However, current similarity-based methods make the implicit assumption that students' performance on items that belong to the same KC should be similar. This assumption holds if the latent trait (mastery of the underlying skill) is relatively fixed during students' activity, as in the context of testing, which is the primary context in which these methods were developed and applied. However, in adaptive learning systems that aim for learning, and address subject matters such as K-6 Math that consist of multiple sub-skills, this assumption does not hold. In this paper we propose a new item-similarity measure, termed Kappa Learning (KL), which aims to address this gap. KL identifies similarity between items under the assumption of learning, namely, that learners' mastery of the underlying skills changes as they progress through the items. We evaluate KL on data from a K-6 Math Intelligent Tutoring System, with experts' tagging as ground truth, and on simulated data. Our results show that clustering that is based on KL outperforms clustering that is based on commonly used similarity measures (Cohen's Kappa, Yule, and Pearson), and that KL is also superior in the task of discovering the number of KCs. [For the full proceedings, see ED599096.]

The inability of teachers to timely cover the scheme of work, students' truancy and persistent lateness to school, poor attitude towards teaching and learning, activities of cultism and frequent conflicts among secondary school students in Enugu State prompted this study to determine the administrators' managerial competencies for sustainable human resource management in secondary education in Enugu State. Two specific purposes were formulated and two research questions guided the study. The study adopted a descriptive survey research design. The study population comprised all the 291 secondary school principals in Enugu State. Simple random sampling technique was used to sample 146 principals for the study. The instrument used for data collection was a 19 items researchers' developed instrument titled; "Administrators' Managerial Competencies for Sustainable Human Resource Management Questionnaire" (AMCSHRMQ). The face validation of the instrument was established by three experts, two from the Department of Educational Management and Policy and one from the Department of Educational Foundations (Measurement and Evaluation Unit), Nnamdi Azikiwe University, Awka. The Cronbach alpha was used to determine the reliability of the instrument. Coefficient value of 0.71 was obtained. Data were analyzed using mean and standard deviation. Findings of the study revealed among others that secondary school administrators lack managerial competencies for students' human resource management by not providing counseling services to students regarding their learning process, providing incentives for students to increase their motivation to learn among others. Based on the finding, it was recommended among others that student human resource management should be incorporated and emphasized in the training guide for educational administrators in order to make them develop more suitable students centered policies in their various schools. Conclusion was drawn based on the findings.

The inability of teachers to timely cover the scheme of work, students' truancy and persistent lateness to school, poor attitude towards teaching and learning, activities of cultism and frequent conflicts among secondary school students in Enugu State prompted this study to determine the administrators' managerial competencies for sustainable human resource management in secondary education in Enugu State. Two specific purposes were formulated and two research questions guided the study. The study adopted a descriptive survey research design. The study population comprised all the 291 secondary school principals in Enugu State. Simple random sampling technique was used to sample 146 principals for the study. The instrument used for data collection was a 19 items researchers' developed instrument titled; "Administrators' Managerial Competencies for Sustainable Human Resource Management Questionnaire" (AMCSHRMQ). The face validation of the instrument was established by three experts, two from the Department of Educational Management and Policy and one from the Department of Educational Foundations (Measurement and Evaluation Unit), Nnamdi Azikiwe University, Awka. The Cronbach alpha was used to determine the reliability of the instrument. Coefficient value of 0.71 was obtained. Data were analyzed using mean and standard deviation. Findings of the study revealed among others that secondary school administrators lack managerial competencies for students' human resource management by not providing counseling services to students regarding their learning process, providing incentives for students to increase their motivation to learn among others. Based on the finding, it was recommended among others that student human resource management should be incorporated and emphasized in the training guide for educational administrators in order to make them develop more suitable students centered policies in their various schools. Conclusion was drawn based on the findings.