Miracle-dz/UniFAR-FTUs huggingface dataset:
C HAPTER 5 ‒A Review of Student Models Used in Intelligent Tutoring Systems

This chapter reviews current practice in student modeling in ITSs, focusing on current practices and optimal contexts of application. A student model is an essential part of an ITS since the student model is the basis from which these systems adapt to the student ’ s current knowledge as indicated by their performance. Intelligent tutoring systems use a student model of performance in the context of a particular domain model, a particular tutoring model, and in reference to specific user interfaces for student interaction. To better understand these dependencies, this chapter surveys how the various types of student models have been implemented in various systems and how these specific systems integrate the student model, domain model, pedagogical strategy (tutoring model) and the user interface. The goal of this survey is to consider the strengths and weaknesses of each student model to help researchers consider what best suits the need of new ITS or framework design. Further, the chapter addresses commonalities and points of overlap between the different student models, particularly in reference to how they depend on similar domain knowledge representations, similar pedagogical inference methods, and similarities among user interfaces. This review covers multiple student models, including knowledge tracing, constraint-based models, knowledge space models, and expectation-and misconception-tailored dialog models.
A Faster Decentralized Algorithm for Nonconvex Minimax Problems

In this paper, we study the nonconvex-strongly-concave minimax optimization problem on decentralized setting. The minimax problems are attracting increasing attentions because of their popular practical applications such as policy evaluation and adversarial training. As training data become larger, distributed training has been broadly adopted in machine learning tasks. Recent research works show that the decentralized distributed data-parallel training techniques are specially promising, because they can achieve the efﬁcient communications and avoid the bottleneck problem on the central node or the latency of low bandwidth network. However, the decentralized minimax problems were seldom studied in literature and the existing methods suffer from very high gradient complexity. To address this challenge, we propose a new faster decentralized algorithm, named as DM-HSGD, for nonconvex minimax problems by using the variance reduced technique of hybrid stochastic gradient descent. We prove that our DM-HSGD algorithm achieves stochastic ﬁrst-order oracle (SFO) complexity of O ( κ 3 (cid:15) − 3 ) for decentralized stochastic nonconvex-strongly-concave problem to search an (cid:15) -stationary point, which improves the exiting best theoretical results. Moreover, we also prove that our algorithm achieves linear speedup with respect to the number of workers. Our experiments on decentralized settings show the superior performance of our new algorithm.
A study of the conversion of different evaporation pans in South China based on the extreme learning machine model

ABSTRACT Evaporation is important basic information for irrigation decision making in water resources management. Developing countries usually use a small pan to observe surface evaporation, but the relationship between evaporation in different small pans is not sufficiently clear. In this paper, we use an extreme learning machine (ELM) model to predict and convert E20 (diameter 0.20 m) and E601 (diameter 0.62 m) pan data for 38 meteorological stations in southern China. Firstly, we obtained the best combination of meteorological parameters for forecasting E20 and E601, respectively, and we also found that the accuracy of the model can be significantly improved by adding pan data. Secondly, we found that during the conversion between E20 and E601, the model performance when using E601 data to predict the E20 evaporation is better than that when using E20 data to predict the E601 evaporation. Finally, the geographical factors were analysed, and the model performance was found to be relatively poor in the coastal area and the North–South junction.
Synthesis Paper State-and-Transition Models for Heterogeneous Landscapes: A Strategy for Development and Application

Interpretation of assessment and monitoring data requires information about how reference conditions and ecological resilience vary in space and time. Reference conditions used as benchmarks are often specified via potential-based land classifications (e.g., ecological sites) that describe the plant communities potentially observed in an area based on soil and climate. State-and-transition models (STMs) coupled to ecological sites specify indicators of ecological resilience and thresholds. Although general concepts surrounding STMs and ecological sites have received increasing attention, strategies to apply and quantify these concepts have not. In this paper, we outline concepts and a practical approach to potential-based land classification and STM development. Quantification emphasizes inventory techniques readily available to natural resource professionals that reveal processes interacting across spatial scales. We recommend a sequence of eight steps for the co-development of ecological sites and STMs, including 1) creation of initial concepts based on literature and workshops; 2) extensive, low-intensity traverses to refine initial concepts and to plan inventory; 3) development of a spatial hierarchy for sampling based on climate, geomorphology, and soils; 4) stratified medium-intensity inventory of plant communities and soils across a broad extent and with large sample sizes; 5) storage of plant and soil data in a single database; 6) model-building and analysis of inventory data to test initial concepts; 7) support and/ or refinement of concepts; and 8) high-intensity characterization and monitoring of states. We offer a simple example of how data assembled via our sequence are used to refine ecological site classes and STMs. The linkage of inventory to expert knowledge and site-based mechanistic experiments and monitoring provides a powerful means for specifying management hypotheses and, ultimately, promoting resilience in grassland, shrubland, savanna, and forest ecosystems.
A Random Walk with Restart Model Based on Common Neighbors for Predicting the Clinical Drug Combinations on Coronary Heart Disease

As the approaching of the clinical big data era, the prediction of whether drugs can be used in combination in clinical practice is a fundamental problem in the analysis of medical data. Compared with high-throughput screening, it is more cost-effective to treat this problem as a link prediction problem and predict by algorithms. Inspired by the rule of combined clinical medication, a new computational model is proposed. The drug-drug combination was predicted by combining the number of adjacent complete subgraphs shared by the two points with the restart random walk algorithm. The model is based on the semisupervised random walk algorithm, and the same neighborhood is used to improve the random walk with restart (CN-RWR). The algorithm can effectively improve the prediction performance and assign a score to any combination of drugs. To fairly compare the predictive performance of the improved model with that of the random walk with restart model (RWR), a cross-validation of the two models on the same drug data was performed. The AUROC of CN-RWR and RWR under the LOOCV validation framework is 0.9741 and 0.9586, respectively, and the improved model results are more reliable. In addition, the top 3 predictive drug combinations have been approved by the public. The new model is expected that this model can be extended to predict the use of combination drugs for other diseases to find combinations of drugs with potential clinical benefits.
Adherence to antihypertensive drug treatment in kidney transplant recipients

Abstract Purpose Hypertension is a common cardiovascular co-morbidity after kidney transplantation and contributes to shortened graft and patient survival outcomes. However, by contrast with adherence to immunosuppressive drugs, adherence to antihypertensive treatment in kidney transplant recipients has been seldom explored. The aim of the current study was to assess adherence to antihypertensive drugs in kidney transplant recipients from the Cliniques Universitaires Saint-Luc and to look for demographic and clinical characteristics associated with drug adherence. Methods Demographic and clinical data were collected from medical files in a standardised case report form. Blood pressure was measured in the sitting position after 5 min rest, using validated oscillometric devices. Drug adherence was assessed by drug dosage in urine using liquid chromatography coupled with tandem mass spectrometry. Results Our analysis included 53 kidney transplants recipients (75% of men, mean age: 57.2 ± 12.6 years, time since kidney transplantation: 9.5 ± 7.3 years, blood pressure: 130 ± 16/78 ± 11 mmHg on 2.1 ± 1.1 antihypertensive drugs). The proportion of patients showing full drug adherence, partial drug adherence, and total non-adherence to antihypertensive drugs was 79% (N = 42), 15% (N = 8), and 6% (N = 3), respectively. Adherent patients did not differ from less or non- adherers in any of the analysed characteristics. Conclusion The proportion of patients adhering to antihypertensive drug treatment among kidney transplant recipients appears similar to that reported for immunosuppressive drugs in renal transplanted patients (∼70%), but much higher than that observed in patients with drug-resistant hypertension (30–40%). Our results need further confirmation in a large, multicenter, prospective cohort.
Segmentation and recognition of high throughput soybean pods with a supervised edge attention network and synthetic dataset

Figure 1: Overview of our method. (a) Real-world high throughput soybean pods dataset. (b) Single soybean pod image pool. (c) Examples of high throughput fine-labeled multi-class soybean pods dataset. (d) The improved SEANet with transfer learning, which is retrained on our synthetic dataset with the pretrained COCO weights. (e) Visualized results of the retrained model raw output (24 epoch, ResNet101-FPN backbone): (1) Example of synthetic test image and real-world test image, (2) Mask R-CNN, (3) SEANet, (4) Our method which improved the loss function of classification based on SEANet.
A roadmap on learning and reasoning for distributed computing continuum ecosystems

A captivating set of hypotheses from the field of neuroscience suggests that human and animal brain mechanisms result from few powerful principles. If proved to be accurate, these assumptions could open a deep understanding of the way humans and animals manage to cope with the unpredictability of events and imagination. Modern distributed systems also deal with uncertain scenarios, where environments, infrastructures, and applications are widely diverse. In the scope of Edge- Fog-Cloud computing, leveraging these neuroscience-inspired principles and mechanisms could aid in building more flexible solutions able to generalize over different environments. In this work, we focus on the approaches that center on high-level, general strategies, like the Free Energy Principle and Global Neuronal Workspace theories. The goal of exploring these techniques is to introduce principles that can potentially help us build distributed systems able to jointly work on the whole computing continuum, from the Edge to the Cloud, with self-adapting capabilities, i.e., dealing with uncertainty and the need for generalization, which is currently an open issue.
Sequential Causal Imitation Learning with Unobserved Confounders

"Monkey see monkey do"is an age-old adage, referring to na\"ive imitation without a deep understanding of a system's underlying mechanics. Indeed, if a demonstrator has access to information unavailable to the imitator (monkey), such as a different set of sensors, then no matter how perfectly the imitator models its perceived environment (See), attempting to reproduce the demonstrator's behavior (Do) can lead to poor outcomes. Imitation learning in the presence of a mismatch between demonstrator and imitator has been studied in the literature under the rubric of causal imitation learning (Zhang et al., 2020), but existing solutions are limited to single-stage decision-making. This paper investigates the problem of causal imitation learning in sequential settings, where the imitator must make multiple decisions per episode. We develop a graphical criterion that is necessary and sufficient for determining the feasibility of causal imitation, providing conditions when an imitator can match a demonstrator's performance despite differing capabilities. Finally, we provide an efficient algorithm for determining imitability and corroborate our theory with simulations.
Onboard Flight Dynamic Route Optimization

Nowadays the flight management systems (FMS) of an airplane can fly it autonomously from takeoff to landing with little intervention from the pilots assuming there are no anomalous events. However, some events require a diversion and subsequent route replanning. This replan is non-trivial, subject to a restricted criteria and takes attention away from the pilots and from air traffic controllers, attention that could be spent monitoring other flight systems. Route planning involves finding a new efficient route, communicate it to ATC, receive approval and reprogram the flight computer to follow the new path. Our work presents a module capable of finding a feasible flight path while complying with all the existing restrictions whether they are space restrictions such as weather events or no fly zones, or the airplane movement restrictions, removing the need of pilot intervention in this area lightening up their work as well as the air controller’s work. We model the problem as an extension to the knapsack model and we then utilize a modified labeling algorithm created to solve the bicriteria 0-1 knapsack problem and efficiently recalculate the routes while taking into account all existing restrictions. The evaluation made was focused on both the accuracy of the route calculated and the time it took to recalculate the route and present the
Automating the Risk of Bias

Artificial intelligence (“AI”) is a transformative technology that has radically altered decision-making processes. Evaluating the case for algorithmic or automated decision-making (“ADM”) platforms requires navigating tensions between two normative concerns. On the one hand, ADM platforms may lead to more efficient, accurate, and objective decisions. On the other hand, early and disturbing evidence suggests ADM platform results may demonstrate biases, undermining claims that this special class of algorithms will democratize markets and increase inclusion. State law assigns decision-making authority to the boards of directors of corporations. State courts and lawmakers accord significant deference to the board in the execution of its duties. Among its duties, a board must employ effective oversight policies and procedures to manage known risks. Consequently, the board of directors and senior management of firms integrating ADM platforms must monitor operations to mitigate enterprise risks including litigation, reputation, compliance, and regulatory risks that arise as a result of the integration of algorithms. After the recent financial crisis, firms adopted structural and procedural governance reforms to mitigate various enterprise risks; these approaches may prove valuable in mitigating the risk of algorithmic bias. Evidence demonstrates that heterogeneous teams may identify and mitigate risks more successfully than homogeneous teams. Heterogeneous teams are more likely to overcome cognitive biases such as confirmation, commitment, overconfidence, and relational biases. This Article argues that increasing gender inclusion in the development of AI technologies will introduce important and diverse perspectives, reduce the influence of cognitive biases in the design, training, and oversight of learning algorithms, and, thereby, mitigate bias-related risk management concerns. * McGlinchey Stafford Professor of Law, Associate Dean of Faculty Research and Gordon Gamm Faculty Scholar, Tulane University Law School. J.D. , University of Michigan Law School; B.S., Georgetown University Walsh School of Foreign Service. I am grateful to Lisa Fairfax, Joan MacLeod Heminway, Jill Fisch, Sung Hui Kim, Anne Simpson, June Carbone, and The George Washington Law Review Women and Corporate Governance Symposium participants for contributing to this Article. I thank Veronica Root Martinez, Gina-Gail Fletcher, Orly Lobel, Najarian Peters, and Katrice Copeland Bridges. I also thank the participants of the 2018 Lutie Lytle Writing Workshop, the 2019 National Business Law Scholars Conference at the University of California, Berkeley Law School, and the Notre Dame Law School Corporate Governance Roundtable for helpful comments and conversations. Special thanks to my research assistants Marissa-Elena Etta, Douglas Peters, Margaret Schale, and Kimberly Lindenmuth for invaluable research assistance, as well as Kelsey Stein and the editorial staff of The George Washington Law Review. September 2019 Vol. 87 No. 5
Making DL-Lite Planning Practical (Abstract) ?

AI planning is a well-investigated framework for describing the evolution of system states through actions [4]. Action preconditions are ﬁrst-order logic formulas, which are evaluated over states, i.e. ﬁnite sets of facts. Action eﬀects either add or remove facts from the current state. Formulas are evaluated using a closed-domain, closed-world semantics, i.e. the domain is ﬁxed a priori and facts that are not contained in a state are assumed to be false. The aim is to ﬁnd a plan , i.e. a sequence of (grounded) actions that satisfy a goal formula. Knowledge representation
Recurrent Bayesian Classifier Chains for Exact Multi-Label Classification

Exact multi-label classification is the task of assigning each datapoint a set of class labels such that the assigned set exactly matches the ground truth. Optimizing for exact multi-label classification is important in domains where missing a single label can be especially costly, such as in object detection for autonomous vehicles or symptom classification for disease diagnosis. Recurrent Classifier Chains (RCCs), a recurrent neural network extension of ensemble-based classifier chains, are the state-of-the-art exact multi-label classification method for maximizing subset accuracy. However, RCCs iteratively predict classes with an unprincipled ordering, and therefore indiscriminately condition class probabilities. These disadvantages make RCCs prone to predicting inaccurate label sets. In this work we propose Recurrent Bayesian Classifier Chains (RBCCs), which learn a Bayesian network of class dependencies and leverage this network in order to condition the prediction of child nodes only on their parents. By conditioning predictions in this way, we perform principled and non-noisy class prediction. We demonstrate the effectiveness of our RBCC method on a variety of real-world multi-label datasets, where we routinely outperform the state of the art methods for exact multi-label classification.
Modified Frank Wolfe in Probability Space

We propose a novel Frank-Wolfe (FW) procedure for the optimization of inﬁnite-dimensional functionals of probability measures - a task which arises naturally in a wide range of areas including statistical learning (e.g. variational inference) and artiﬁcial intelligence (e.g. generative adversarial networks). Our FW procedure takes advantage of Wasserstein gradient ﬂows and strong duality results recently developed in Distributionally Robust Optimization so that gradient steps (in the Wasserstein space) can be efﬁciently computed using ﬁnite-dimensional, convex optimization methods. We show how to choose the step sizes in order to guarantee exponentially fast iteration convergence, under mild assumptions on the functional to optimize. We apply our algorithm to a range of functionals arising from applications in nonparametric estimation.
Discontinuities in the Age-Victimization Profile and the Determinants of Victimization

Dutch victimization rates increase by 9-15% immediately upon reaching ages 16 and 18. We disentangle the role of the many rights granted at these ages using offense location data, cross-cohort variation in the minimum legal drinking age driven by a 2014 reform, and survey data of alcohol/drug consumption and mobility behaviours. We conclude that access to weak alcohol, bars/clubs and smoking increases victimization at 16 and that age 18 rights (hard alcohol, marijuana coffee shops) exacerbate this risk; vehicle access does not play an important role. We find no evidence of systematic spillovers onto individuals still ineligible for these rights.
Blow-up phenomena for a class of metaparabolic equations with time dependent coefficient

Abstract: This paper deals with the initial boundary value problem for a metaparabolic equations with time dependent coefficient. Under suitable conditions on initial data, a blow-up criterion which ensures that u cannot exist all time is given, and an upper bound for blow up time is derived. Moreover, we also obtain a lower bound for blow-up time if blow up does occur by means of a differential inequality technique.
Collapsed Variational Bounds for Bayesian Neural Networks

Recent interest in learning large variational Bayesian Neural Networks (BNNs) has been partly hampered by poor predictive performance caused by underﬁtting, and their performance is known to be very sensitive to the prior over weights. Current practice often ﬁxes the prior parameters to standard values or tunes them using heuristics or cross-validation. In this paper, we treat prior parameters in a distributional way by extending the model and collapsing the variational bound with respect to their posteriors. This leads to novel and tighter Evidence Lower Bounds (ELBOs) for performing variational inference (VI) in BNNs. Our experiments show that the new bounds signiﬁcantly improve the performance of Gaussian mean-ﬁeld VI applied to BNNs on a variety of data sets, demonstrating that mean-ﬁeld VI works well even in deep models. We also ﬁnd that the tighter ELBOs can be good optimization targets for learning the hyperparameters of hierarchical priors.
The Effects of Soaking and Autoclaving on Chemical and Nutritional Composition of the Horse Chestnut (Aesculus hippocastanum) Seed

The aim of this study is to determine the nutrient composition of the horse chestnut (Aesculus hippocastanum) seed and to determine the differences of these nutrients with some technological processes. For this purpose, the horse chestnut seed was divided into 3 groups as 1) raw, 2) soaking in cold water for 24 hours and 3) autoclaving at 121 °C for 60 minutes. The obtained groups were then analyzed under two groups as shelled and unshelled. The obtained data were analyzed in general linear model (GLM). As a result, the effect of the treatments applied on horse chestnut seed on dry matter (DM), crude protein (CP), Ether extract (EE), crude ash (CA), cellulose fractions and metabolic energy values were insignificant, while the tannin ratio decreased significantly in the autoclaved group. When horse chestnut seeds are compared with shell and without shell; while the content of DM, CP, EE and CA did not change, the starch content increased, acid detergent fiber, neutral detergent fiber and hemicellulose contents decreased in the shell less horse chestnut seed. The tannin ratio was significantly lower in the shelled horse chestnut. In addition, as a result of mineral analysis of raw horse chestnut, P, K, Ca, Na, Cl, Si, Al, Ni, Cr, Rb, Zn, Fe, Mg and S contents were; 0.257%, 1.106%, 0.27%, 0.146%, 0.061%, 0.055%, 0.023%, 0.016%, 0.005%, 0.003%, 0.006%, 0.08%, 0.056% and 0.117%, respectively. The technological processes applied were effective in reducing the tannins content, or can be used without shell for the purpose of reducing the tannins content.