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176 pages : 28 cm

176 pages : 28 cm

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.

random3, 'Andrew Park - Data Science for Beginners_ 4 Books in 1_ Python Programming, Data Analysis, Machine Learning. A Complete Overview to Master The Art of Data Science From Scratch Using Python for Busines'

Suicidal ideation (SI) poses a significant burden on individuals and society, necessitating effective interventions to address this growing crisis. While face-to-face cognitive behavioral therapy (CBT) has demonstrated its effectiveness in reducing SI, access to treatment remains limited due to various barriers. Digital interventions, particularly digital cognitive-behavioral therapy (iCBT), offer a promising solution by providing greater accessibility and flexibility. However, accurate prediction of treatment outcomes following iCBT remains challenging, and machine learning (ML) algorithms have been recommended to enhance prediction accuracy. We aim to leverage supervised ML models trained on pooled individual participant data (IPD) from multiple previous iCBT studies / RCTs, to predict treatment outcome (operationalized as reliable change in SI). We will employ various supervised ML algorithms, such as support vector machines, random forest or extreme gradient boosted regression trees. By employing a personalized and data-driven approach, this research seeks to assess the accuracy of outcome prediction following iCBT.

Rumination is associated with the onset, duration and severity of a depression. Being distracted from ruminative thoughts (“distraction hypothesis”) is discussed as a possible mechanism of action of the antidepressant effect of moderate to vigorous exercise, which is well-established (Heissel et al., 2023; Morres et al., 2019). In this project, we decode rumination from electroencephalography (EEG) data using machine learning algorithms. Decoded rumination and self-reports are used to predict possible changes in rumination through exercise. Decoded rumination provides a more objective measure of rumination, additional to and beyond self-reports, that might be less biased and shed light into the underlying neurophysiological correlates of rumination. In this project, we will investigate whether moderate-intensity exercise (ME) reduces rumination compared to a sedentary control condition (SED). ME will be performed as continuous exercise at 100-110% of the individual first lactate threshold. In the sedentary control condition, participants sit inactive in a chair. Each condition is performed for 30 minutes. Participants will complete a single factor (ME vs. SED) within-subject design in randomised order while EEG is measured. EEG is applied with 59 electrodes according to the 10-20 system. Additionally, data is measured from 4 EOG electrodes, 1 electrode at muscle risorius, 4 bipolar electrodes at muscle trapezius and 4 bipolar electrodes at sternocleidomastoid muscle. In a previous part of the project (https://doi.org/10.17605/OSF.IO/C5JF9), decoders (i.e., support-vector classification models) are trained to predict rumination (versus distraction) from EEG data during experimentally induced rumination or distraction. In the current project, the trained decoders predict the class (i.e., rumination vs. distraction) and class probability of rumination from continuous EEG data features (i.e., alpha and theta power across the 59 channels and a connectivity matrix between all channels) measured during the exercises. The class probability for rumination is analysed in 7.5 s data segments across the time course of ME or SED, respectively. Furthermore, self-reported rumination will be assessed before and after each condition using the Perseverative Thinking Questionnaire state (PTQ-S; Ehring et al., 2011) and during the conditions using visual analogue scales (VAS). We hypothesize that the mean change of self-reported rumination as well as the mean decoded probability of rumination is significantly lower in the ME condition compared to the SED condition. By implementing a novel, more objective measurement of rumination in combination with validated and well-established self-reports, this project will help to understand whether distraction mediates the antidepressant effect of exercise.

This article is from BMC Bioinformatics , volume 11 . Abstract Background: Extracting protein-protein interactions from biomedical literature is an important task in biomedical text mining. Supervised machine learning methods have been used with great success in this task but they tend to suffer from data sparseness because of their restriction to obtain knowledge from limited amount of labelled data. In this work, we study the use of unlabeled biomedical texts to enhance the performance of supervised learning for this task. We use feature coupling generalization (FCG) – a recently proposed semi-supervised learning strategy – to learn an enriched representation of local contexts in sentences from 47 million unlabeled examples and investigate the performance of the new features on AIMED corpus. Results: The new features generated by FCG achieve a 60.1 F-score and produce significant improvement over supervised baselines. The experimental analysis shows that FCG can utilize well the sparse features which have little effect in supervised learning. The new features perform better in non-linear classifiers than linear ones. We combine the new features with local lexical features, obtaining an F-score of 63.5 on AIMED corpus, which is comparable with the current state-of-the-art results. We also find that simple Boolean lexical features derived only from local contexts are able to achieve competitive results against most syntactic feature/kernel based methods. Conclusions: FCG creates a lot of opportunities for designing new features, since a lot of sparse features ignored by supervised learning can be utilized well. Interestingly, our results also demonstrate that the state-of-the art performance can be achieved without using any syntactic information in this task.

This pre-registration contains the plan for carrying out my master thesis, which is based on my research proposal.

This pre-registration contains the plan for carrying out my master thesis, which is based on my research proposal.

Mining in data is an important step for knowledge discovery, which leads to extract new patterns from datasets. It is a widespread methodology that has the capability to help ministries, companies, and experts for diving into the data to find important insights and patterns to help them take suitable decisions. The farmers and marketers of the date product in the production regions lack to discover the most important characteristics of dates types from the economically, healthy, and the type of consumers point of view to achieve the highest profits by choosing the best types and the most consumed. The research objective is to extract interesting patterns from the dates’ product dataset, using Machine Learning, based on association rules generation. This, in turn, will support the farmers, and marketers to discover new features related to the production, consumption, and marketing processes. This research used a real dataset collected from KSA, Qassim region, which is the first region of cultivation of palm, that produces the best types of dates in the Arab region. The data preprocessed and analyzed by the Apriori algorithm. The results show important features and insights related to the health benefits of dates, production, its consumption, consumers types, and marketing. Consequently, these results can be employed, for instance, to encourage individuals to consume dates for their nutritional value and their important health benefits. Furthermore, the results encourage producers to focus on the production of preferable types and to improve the marketing policies of the other types.

Mining in data is an important step for knowledge discovery, which leads to extract new patterns from datasets. It is a widespread methodology that has the capability to help ministries, companies, and experts for diving into the data to find important insights and patterns to help them take suitable decisions. The farmers and marketers of the date product in the production regions lack to discover the most important characteristics of dates types from the economically, healthy, and the type of consumers point of view to achieve the highest profits by choosing the best types and the most consumed. The research objective is to extract interesting patterns from the dates’ product dataset, using Machine Learning, based on association rules generation. This, in turn, will support the farmers, and marketers to discover new features related to the production, consumption, and marketing processes. This research used a real dataset collected from KSA, Qassim region, which is the first region of cultivation of palm, that produces the best types of dates in the Arab region. The data preprocessed and analyzed by the Apriori algorithm. The results show important features and insights related to the health benefits of dates, production, its consumption, consumers types, and marketing. Consequently, these results can be employed, for instance, to encourage individuals to consume dates for their nutritional value and their important health benefits. Furthermore, the results encourage producers to focus on the production of preferable types and to improve the marketing policies of the other types.

Mining in data is an important step for knowledge discovery, which leads to extract new patterns from datasets. It is a widespread methodology that has the capability to help ministries, companies, and experts for diving into the data to find important insights and patterns to help them take suitable decisions. The farmers and marketers of the date product in the production regions lack to discover the most important characteristics of dates types from the economically, healthy, and the type of consumers point of view to achieve the highest profits by choosing the best types and the most consumed. The research objective is to extract interesting patterns from the dates’ product dataset, using Machine Learning, based on association rules generation. This, in turn, will support the farmers, and marketers to discover new features related to the production, consumption, and marketing processes. This research used a real dataset collected from KSA, Qassim region, which is the first region of cultivation of palm, that produces the best types of dates in the Arab region. The data preprocessed and analyzed by the Apriori algorithm. The results show important features and insights related to the health benefits of dates, production, its consumption, consumers types, and marketing. Consequently, these results can be employed, for instance, to encourage individuals to consume dates for their nutritional value and their important health benefits. Furthermore, the results encourage producers to focus on the production of preferable types and to improve the marketing policies of the other types.

We propose a family of efficient algorithms for learning the parameters of a Bayesian network from incomplete data. Our approach is based on recent theoretical analyses of missing data problems, which utilize a graphical representation called the missingness graph. In the case of MCAR and MAR data, this graph need not be explicit, and yet we can still obtain closed form asymptotically consistent parameter estimates without the need for inference. When this missingness graph is explicated (based on background knowledge), even partially, we can obtain even more accurate estimates with less data. Empirically we illustrate how we can learn the parameters of large networks from large datasets which are beyond the scope of algorithms like EM (which require inference).

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This thesis demonstrates an entropy-based, Bayesian dependency algorithm-Minimum Error Tree Decomposition II (METD2)-that performs computer-based generation of probabilistic hidden-structure domain models from a database of cases. The system learns probabilistic hidden-structure domain models from data, which partially automates the task of expert system construction and the task of scientific discovery. Existing probabilistic systems find associations among the observable variables but do not consider the presence of hidden variables, or else, do not use cross-correlation error as the metric for building the hidden structure. The algorithm decomposes a joint distribution of n observable variables into n+l observable and hidden variables. The hidden variable exists in the form of a tree consisting of n-l interior nodes. The final product of the procedure is a combined tree whose n leaves are the observable variables in a sample and whose n-l interior nodes are the marginalizations for the leaves.

Meet Adharsh P, a passionate learner who transformed his career with the Postgraduate Program in Data Science and Analytics at Imarticus Learning. From mastering Python and SQL to gaining hands-on experience in machine learning and AI-driven analytics, Adharsh's journey is a testament to what focused learning and mentorship can achieve.

Recent advances in data storage and acquisition technologies have made it possible to produce increasingly large data repositories. Most of these data sources are physically distributed and assembling them together at a central site is expensive in terms of network bandwidth and insecure. Hence there is a need for Learning Algorithms that are able to learn from distributed data without collecting it in a central location. We present provably exact algorithms for learning decision trees from distributed data sets. We prove that the results obtained in this case are the same as those obtained if the data were stored at a central location. We also give a time, space and communication cost analysis. We conclude with a discussion of a general technique for adapting some of the existing learning algorithms to learn from distributed datasets.

random2, 'Andrew Park - Data Science for Beginners_ 4 Books in 1_ Python Programming, Data Analysis, Machine Learning. A Complete Overview to Master The Art of Data Science From Scratch Using Python for Busines'

random2, 'Andrew Park - Data Science for Beginners_ 4 Books in 1_ Python Programming, Data Analysis, Machine Learning. A Complete Overview to Master The Art of Data Science From Scratch Using Python for Busines'

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.

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.

random2, 'Andrew Park - Data Science for Beginners_ 4 Books in 1_ Python Programming, Data Analysis, Machine Learning. A Complete Overview to Master The Art of Data Science From Scratch Using Python for Busines'

The study aims to evaluate how distance social studies courses were conducted during the pandemic, in line with teachers' experiences. The semi-structured interviews were conducted with 14 social studies teachers working in various provinces of Turkey in this study, which was carried out with interpretative phenomenology design, one of the qualitative research methods. The data were evaluated by the content analysis method. The results show that social studies teachers perceive distance education as a system that allows using different materials and tools, maintaining education, learning technology, and eliminating the need for time and place. On the other hand, teachers expressed some limitations of distance education arising from the lack of internet and technological tools. In addition, the results indicated teachers mostly used direct instruction and question-answer methods for teaching during the distance education period, EBA, Zoom, and some Web 2.0 tools as educational technologies, and tests and question-answer methods for measurement and evaluation. Finally, many problems, gathered under the titles of "technological-systemic", "communication-coordination", "student", "family, and "other" in the distance education process were emphasized. When examining the experiences, all problems are seen to be related to the internet connection, access to technological tools, and how families play a role in their children's education. In this context, compared with other studies in the literature, it is possible to say that teachers working in different branches also experienced similar problems during the distance education period. Therefore, innovative applications that can be developed will be beneficial for all branches.

Working collaboratively in groups can positively impact performance and student engagement. Intelligent social agents can provide a source of personalized support for students, and their benefits likely extend to collaborative settings, but it is difficult to determine how these agents should interact with students. Reinforcement learning (RL) offers an opportunity for adapting the interactions between the social agent and the students to better support collaboration and learning. However, using RL in education with social agents typically involves training using real students. In this work, we train an RL agent in a high-quality simulated environment to learn how to improve students' collaboration. Data was collected during a pilot study with dyads of students who worked together to tutor an intelligent teachable robot. We explore the process of building an environment from the data, training a policy, and the impact of the policy on different students, compared to various baselines. [For the full proceedings, see ED623995.]

conway game sex brit bots/died out fails

conway game sex brit bots/died out fails

conway game sex brit bots/died out fails

We consider the problem of learning a policy for a Markov decision process consistent with data captured on the state-actions pairs followed by the policy. We assume that the policy belongs to a class of parameterized policies which are defined using features associated with the state-action pairs. The features are known a priori, however, only an unknown subset of them could be relevant. The policy parameters that correspond to an observed target policy are recovered using $\ell_1$-regularized logistic regression that best fits the observed state-action samples. We establish bounds on the difference between the average reward of the estimated and the original policy (regret) in terms of the generalization error and the ergodic coefficient of the underlying Markov chain. To that end, we combine sample complexity theory and sensitivity analysis of the stationary distribution of Markov chains. Our analysis suggests that to achieve regret within order $O(\sqrt{\epsilon})$, it suffices to use training sample size on the order of $\Omega(\log n \cdot poly(1/\epsilon))$, where $n$ is the number of the features. We demonstrate the effectiveness of our method on a synthetic robot navigation example.

Statistical models have been successful in accurately estimating the biochemical contents of vegetation from the reflectance spectra. However, their performance deteriorates when there is a scarcity of sizable amount of ground truth data for modeling the complex non-linear relationship occurring between the spectrum and the biochemical quantity. We propose a novel Gaussian process based multitask learning method for improving the prediction of a biochemical through the transfer of knowledge from the learned models for predicting related biochemicals. This method is most advantageous when there are few ground truth data for the biochemical of interest, but plenty of ground truth data for related biochemicals. The proposed multitask Gaussian process hypothesizes that the inter-relationship between the biochemical quantities is better modeled by using a combination of two or more covariance functions and inter-task correlation matrices. In the experiments, our method outperformed the current methods on two real-world datasets.

In this paper we consider the problem of learning the genetic-interaction-map, i.e., the topology of a directed acyclic graph (DAG) of genetic interactions from noisy double knockout (DK) data. Based on a set of well established biological interaction models we detect and classify the interactions between genes. We propose a novel linear integer optimization program called the Genetic-Interactions-Detector (GENIE) to identify the complex biological dependencies among genes and to compute the DAG topology that matches the DK measurements best. Furthermore, we extend the GENIE-program by incorporating genetic-interactions-profile (GI-profile) data to further enhance the detection performance. In addition, we propose a sequential scalability technique for large sets of genes under study, in order to provide statistically stressable results for real measurement data. Finally, we show via numeric simulations that the GENIE-program as well as the GI-profile data extended GENIE (GI-GENIE)-program clearly outperform the conventional techniques and present real data results for our proposed sequential scalability technique.

Interactive visual analytic systems enable users to discover insights from complex data. Users can express and test hypotheses via user interaction, leveraging their domain expertise and prior knowledge to guide and steer the analytic models in the system. For example, semantic interaction techniques enable systems to learn from the user's interactions and steer the underlying analytic models based on the user's analytical reasoning. However, an open challenge is how to not only steer models based on the dimensions or features of the data, but how to add dimensions or attributes to the data based on the domain expertise of the user. In this paper, we present a technique for inferring and appending dimensions onto the dataset based on the prior expertise of the user expressed via user interactions. Our technique enables users to directly manipulate a spatial organization of data, from which both the dimensions of the data are weighted, and also dimensions created to represent the prior knowledge the user brings to the system. We describe this technique and demonstrate its utility via a use case.

Processes such as disease propagation and information diffusion often spread over some latent network structure which must be learned from observation. Given a set of unlabeled training examples representing occurrences of an event type of interest (e.g., a disease outbreak), our goal is to learn a graph structure that can be used to accurately detect future events of that type. Motivated by new theoretical results on the consistency of constrained and unconstrained subset scans, we propose a novel framework for learning graph structure from unlabeled data by comparing the most anomalous subsets detected with and without the graph constraints. Our framework uses the mean normalized log-likelihood ratio score to measure the quality of a graph structure, and efficiently searches for the highest-scoring graph structure. Using simulated disease outbreaks injected into real-world Emergency Department data from Allegheny County, we show that our method learns a structure similar to the true underlying graph, but enables faster and more accurate detection.

Fast and robust three-dimensional reconstruction of facial geometric structure from a single image is a challenging task with numerous applications. Here, we introduce a learning-based approach for reconstructing a three-dimensional face from a single image. Recent face recovery methods rely on accurate localization of key characteristic points. In contrast, the proposed approach is based on a Convolutional-Neural-Network (CNN) which extracts the face geometry directly from its image. Although such deep architectures outperform other models in complex computer vision problems, training them properly requires a large dataset of annotated examples. In the case of three-dimensional faces, currently, there are no large volume data sets, while acquiring such big-data is a tedious task. As an alternative, we propose to generate random, yet nearly photo-realistic, facial images for which the geometric form is known. The suggested model successfully recovers facial shapes from real images, even for faces with extreme expressions and under various lighting conditions.

Automatic detection of anti-patterns from source code can reduce software maintenance costs massively. Nowadays, machine learning approaches are very commonly used to identify anti-patterns. Hence, it is very crucial to choose a classifier that can be useful for detecting anti-patterns. This work aims to help practitioners to choose a suitable classifier to detect anti-patterns. In this paper, we highlight 16 classifiers in four different categories to detect anti-patterns. Furthermore, the performance of these classifiers is identified with the data pre-processing (DPP) to detect four commonly occurring anti-patterns from the three commonly used open-source Java projects’ source code. The accuracy of Dagging classifiers is 98.4%. Kernel logistic regression (KLR) also performs well i.e., 97%. In the case of time complexity, naive Bayes (NB), decision trees (DT), support vector machines (SVM), library for support vector machines (LibSVM), logistic, and LightGBM (LB) have less time complexity to build a model in all the projects.

Enriching speech recognition output with sentence boundaries improves its human readability and enables further processing by downstream language processing modules. We have constructed a hidden Markov model (HMM) system to detect sentence boundaries that uses both prosodic and textural information.

Functional MRI (fMRI) and diffusion MRI (dMRI) are non-invasive imaging modalities that allow in-vivo analysis of a patient's brain network (known as a connectome). Use of these technologies has enabled faster and better diagnoses and treatments of neurological disorders and a deeper understanding of the human brain. Recently, researchers have been exploring the application of machine learning models to connectome data in order to predict clinical outcomes and analyze the importance of subnetworks in the brain. Connectome data has unique properties, which present both special challenges and opportunities when used for machine learning. The purpose of this work is to review the literature on the topic of applying machine learning models to MRI-based connectome data. This field is growing rapidly and now encompasses a large body of research. To summarize the research done to date, we provide a comparative, structured summary of 77 relevant works, tabulated according to different criteria, that represent the majority of the literature on this topic. (We also published a living version of this table online at http://connectomelearning.cs.sfu.ca that the community can continue to contribute to.) After giving an overview of how connectomes are constructed from dMRI and fMRI data, we discuss the variety of machine learning tasks that have been explored with connectome data. We then compare the advantages and drawbacks of different machine learning approaches that have been employed, discussing different feature selection and feature extraction schemes, as well as the learning models and regularization penalties themselves. Throughout this discussion, we focus particularly on how the methods are adapted to the unique nature of graphical connectome data. Finally, we conclude by summarizing the current state of the art and by outlining what we believe are strategic directions for future research.

A standard technique for understanding underlying dependency structures among a set of variables posits a shared conditional probability distribution for the variables measured on individuals within a group. This approach is often referred to as module networks, where individuals are represented by nodes in a network, groups are termed modules, and the focus is on estimating the network structure among modules. However, estimation solely from node-specific variables can lead to spurious dependencies, and unverifiable structural assumptions are often used for regularization. Here, we propose an extended model that leverages direct observations about the network in addition to node-specific variables. By integrating complementary data types, we avoid the need for structural assumptions. We illustrate theoretical and practical significance of the model and develop a reversible-jump MCMC learning procedure for learning modules and model parameters. We demonstrate the method accuracy in predicting modular structures from synthetic data and capability to learn influence structures in twitter data and regulatory modules in the Mycobacterium tuberculosis gene regulatory network.

The study examined some human resource management variables and academic staff job effectiveness in the university of Calabar in Cross River State, Nigeria. To achieve the aim of this study, two research questions were raised and two hypotheses were formulated to guide the study. A correlational research design was adopted for the study. The population of the study comprises all the academic staff in the University of Calabar. The total number of all the academic staff is three thousand eight hundred and sixty (3860). A stratified random sampling technique was used to select total numbers of one hundred (100) respondents from the University of Calabar. Relevant data for the study was collected with a researcher-developed questionnaire titled: "Human Resource Management Variable and Academic Staff Job Effectiveness Questionnaire" (HRMVASJEQ). The instrument was subjected to face and content validity by experts in the administration of higher education and measurement and evaluation, Faculty of Education, University of Calabar. The Pearson Product Moment Correlation Analysis with the aid of the statistical package for social science (SPSS) version 25, was used for data analysis. The result revealed that the management of lecturers' appraisal/promotion and management of lecturers' in-service training significantly relate to their job effectiveness in the university. It was recommended that the university management should ensure that lecturers are appraised and promoted appropriately to enhance their lecturer job performance. Also, that the university management should make provision for lecturer in-service training to enhance learning and improving lecturers' job effectiveness.

(Purpose) With the end of the spring 2020 academic term, a national survey was conducted to identify best practices of college educators regarding how they moved their traditional face-to-face tutoring and small group tutoring programs online due to Covid-19 pandemic. This document contains the results of that survey. (Methods) In early May 2020, invitations to complete a brief survey on postsecondary peer assisted learning (PAL) programs and their operation online in response to Covid-19 were posted to several national and international email listservs. Directors from 45 programs completed the survey. Since the survey was anonymous, it is impossible to know the institutional type and their locations. It is a reasonable guess that most respondents were from the U.S. with others from Australasia, Europe, and North America. As promised, the survey results are presented as they were received without data analysis. It is with deep gratitude to the program directors for taking time from the busiest time in the academic term in the middle of this pandemic to share valuable information with our world community of PAL professionals. (Results) Their comments were candid and honest about the things that went well and those that did not. The survey statements were grouped into six categories that included: needed equipment and meeting software, approaches and activities, program evaluation, expectations for participants and students leaders, and more. (Implications) Best education practices for providing online academic support were shared that can be studied by others as they make plans for fall 2020 academic term which may be offered online.

Modern language learning applications have become 'smarter' and 'intelligent' by including Artificial Intelligence (AI) and Machine Learning (ML) technologies to collect different kinds of data. This data can be used for analysis on a microscopic and/or macroscopic level to provide granulation of knowledge. We analyzed 1,213 French language learner data over a 30-day period, publicly available from Duolingo, to compare the progression of individual learners (microscopic granularity) and large groups of learners (macroscopic granularity). Using network modeling, we compared patterns of individual learners against one another and that of a learning community and determined what groups of learners typically practice across communities. Preliminary results suggest how applications for L2 learning can be designed to create an optimal path for learning. [For the complete volume, "Intelligent CALL, Granular Systems and Learner Data: Short Papers from EUROCALL 2022 (30th, Reykjavik, Iceland, August 17-19, 2022)," see ED624779.]

The purpose of the present study is to demonstrate the utility of data analysis methodology in evaluative research relating pupil and curriculum variables to pupil achievement. Regression models which account for achievement will result from the application of the methodology to two evaluative problems--one of curriculum comparison and another exploring the relationships between achievement and instructional processes in different schools implementing the same curriculum. Evaluative studies focusing on such questions should yield more information about pupil achievement than evaluations following other models when the following practices are reflected in the design and execution of the study: (1) several dimensions of the curriculum, including material and instructional process aspects, are represented in the set of independent variables; (2) curricular and pupil variables are chosen whenever possible from those conceptualized by educational researchers and known to have a likely relationship to achievement; (3) direct measurements on all variables are incorporated in the data set rather than categorical representations of variables, as in a factorial design; (4) the shape as well as the location of the distributions of pupil achievement before and after instruction is represented in the analyses; (5) the techniques of the data analyst guide the model development process. These recommendations result from a review of substantive and methodological literature. (Author/BJG)

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.

Due to the advancement in treatment, more people are surviving cancer. However, cancer survivors also suffer from other chronic health problems (comorbidities). These chronic health conditions can make cancer treatment more challenging and may affect how well patients responds to treatment. This study aims to identify common patterns or clusters of comorbidities among patients with cancer, and how they can affect patients' survival. We will use a large nationally representative survey called the National Health and Nutrition Examination Survey (NHANES) that gather information from people in the United States. To find patterns of multiple health conditions among the participants with cancer, we will use different machine learning clustering methods to examine the combinations of health conditions, and then examine their associations with death using the Cox proportional hazards models.

Multi-instance data, in which each object (bag) contains a collection of instances, are widespread in machine learning, computer vision, bioinformatics, signal processing, and social sciences. We present a maximum entropy (ME) framework for learning from multi-instance data. In this approach each bag is represented as a distribution using the principle of ME. We introduce the concept of confidence-constrained ME (CME) to simultaneously learn the structure of distribution space and infer each distribution. The shared structure underlying each density is used to learn from instances inside each bag. The proposed CME is free of tuning parameters. We devise a fast optimization algorithm capable of handling large scale multi-instance data. In the experimental section, we evaluate the performance of the proposed approach in terms of exact rank recovery in the space of distributions and compare it with the regularized ME approach. Moreover, we compare the performance of CME with Multi-Instance Learning (MIL) state-of-the-art algorithms and show a comparable performance in terms of accuracy with reduced computational complexity.

This is the inspiring success story of K Bhavani Shree , a passionate learner who completed the Postgraduate Program in Data Science and Analytics at Imarticus Learning . With the growing importance of data in decision-making and innovation, Bhavani recognized the need to upskill and transition into the field of data science. Through Imarticus Learning’s comprehensive program, she gained practical, hands-on experience in key technologies such as machine learning, Python programming, SQL, data visualization, and AI-driven analytics . Her learning journey was enriched with real-world projects, case studies, and industry-relevant tools that prepared her for the demands of the evolving tech landscape. Today, Bhavani is equipped with the skills and confidence to build a successful career in data science and contribute meaningfully to the industry. This story serves as an example of how structured education, practical exposure, and continuous learning can empower individuals to achieve their career aspirations. At Imarticus Learning , we remain committed to transforming careers through skill development and future-ready education.

Meet Vikas Mahindrakar, now working as an Associate Data Engineer at Atgeir Solutions. His inspiring journey began when he enrolled in the Postgraduate Program in Data Science and Analytics at Imarticus Learning. With a strong desire to grow and shift his career, Vikas took a bold step toward the tech world by choosing this program. Before joining Imarticus, he was looking for the right learning path to build a stable and rewarding future.  Through expert-led sessions, he gained hands-on experience with key tools and languages such as Python, SQL, and Cloud technologies. These technical skills, along with real-time projects and labs, helped him develop confidence and industry-ready knowledge. The program not only shaped his technical abilities but also prepared him to face real-world challenges. For Vikas, this turned out to be the best data analytics course , opening doors to a promising career in the field of data engineering.

A faculty's commitment to continuous learning and improvement is a critical dimension in defining schools as high-performing learning communities. When planning an improvement effort, a school's staff needs a conceptual framework that outlines the dimensions of school improvement. The AEL Continuous School Improvement Questionnaire (CSIQ) is a self-report inventory that asks professional staff to rate their school on six major dimensions: learning culture; connections among school, family, and community; shared leadership; shared goals for learning; purposeful student assessment; and effective teaching. Extensive pilot and field testing have shown the CSIQ to be a reliable and valid measure of faculty commitment to continuous learning and improvement. This paper reports normative CSIQ data from 3,821 staff in 132 schools in Kentucky, Tennessee, Virginia, and West Virginia that took part in field testing. Within the sample was a subgroup of 11 schools identified as continuously improving or high-performing schools. These schools were labeled "Known" schools. Schools were categorized by grade level and by Johnson Code indicating rural/urban locale. CSIQ scores were highly reliable. School's grade-level had a slight to modest effect, with elementary schools and those containing elementary grades having higher total and subscale scores. CSIQ scores had no relationship to extent of rurality. Educators in "Known" schools almost always scored higher than their counterparts in schools of the same type. (Contains 15 statistical data tables.) (SV)

Learning a classifier from private data collected by multiple parties is an important problem that has many potential applications. How can we build an accurate and differentially private global classifier by combining locally-trained classifiers from different parties, without access to any party's private data? We propose to transfer the `knowledge' of the local classifier ensemble by first creating labeled data from auxiliary unlabeled data, and then train a global $\epsilon$-differentially private classifier. We show that majority voting is too sensitive and therefore propose a new risk weighted by class probabilities estimated from the ensemble. Relative to a non-private solution, our private solution has a generalization error bounded by $O(\epsilon^{-2}M^{-2})$ where $M$ is the number of parties. This allows strong privacy without performance loss when $M$ is large, such as in crowdsensing applications. We demonstrate the performance of our method with realistic tasks of activity recognition, network intrusion detection, and malicious URL detection.

This qualitative and descriptive study examines the evolution of secondary preservice teachers' views on teaching and learning mathematics and science in historical and cultural contexts. Data were collected throughout participants' enrollment in a semester-long course entitled, Perspectives on Science and Mathematics, which is taken in conjunction with student teaching. Data sources included university classroom observations and field notes as well as preservice teachers' verbal and written responses to class discussions, reading assignments, and course activities. Common themes and categories of response were derived from the triangulation of data to include prospective teachers' critical reflections on teaching and learning. The paper ends with a discussion of findings and concluding remarks. [For the complete proceedings, see ED583608.]

This innovative session consists of a panel discussion on different approaches to researching and understanding action-learning programs, based on collaborative empirical work. Panel members compare their use of a critical-pragmatist approach and an actor-network approach. These different but complementary approaches are compared regarding their focus on managers vs. shop floor employees, the role of the set facilitator, implementation and continuation of learning following the program, and the integration of work and (self-directed) learning. [For complete proceedings, see ED491481.]