In this paper, we introduce a refined Educational Data Mining approach, which refrains from explicit learner modeling along with an evaluation concept. We use a Data Mining technology, which models students' learn...
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In this paper, we introduce a refined Educational Data Mining approach, which refrains from explicit learner modeling along with an evaluation concept. We use a Data Mining technology, which models students' learning characteristics by considering real data instead of deriving their characteristics explicitly. It aims at mining course characteristics similarities of former students' study traces and utilizing them to optimize curricula of current students based to their performance traits revealed by their educational history. This refined technology generates suggestions of personalized curricula. The technology includes an adaptation mechanism, which compares recent data with historical data to ensure that the similarity of mined characteristics follow the dynamic changes affecting curriculum (e.g., revision of course contents and materials, and changes in teachers, etc.). Finally, the paper shows some pre-validation results and approaches for a final validation.
Social media is widely utilized in the tobacco control campaigns. It is a great challenge to evaluate the efficiency of tobacco control policies on social network sites and find gaps among tobacco-oriented social netw...
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Due to its nonlinear potential, hyper video offers improvement in users' browsing efficiency and visual information's representation ability compared with that of traditional video. Based on the advantages of ...
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作者:
János RudanGábor SzederkényiKatalin M. HangosFaculty of Information Technology
Pázmány Péter Catholic University Práter u. 50/a H-1083 Budapest Hungary Faculty of Information Technology
Pázmány Péter Catholic University Práter u. 50/a H-1083 Budapest Hungary and Process Control Research Group Computer and Automation Research Institute Hungarian Academy of Sciences. Hungary Process Control Research Group
Computer and Automation Research Institute Hungarian Academy of Sciences. Kende u. 13-17 H-1111 Budapest Hungary and Dept. of Electrical Engineering and Information Systems Faculty of Information Technology Univer Hungary
An algorithm for the computation of mass conservative dynamically equivalent chemical reaction network structures is proposed in this paper. The algorithm is formulated in an optimization-based framework as a mixed-in...
An algorithm for the computation of mass conservative dynamically equivalent chemical reaction network structures is proposed in this paper. The algorithm is formulated in an optimization-based framework as a mixed-integer linear programming problem.
In this paper, we present a systematic control design for strict-feedback nonlinear systems with output constraints using tangent Barrier Lyapunov Functions (BLFs-Tan). Both symmetric and asymmetric BLF-Tan based back...
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In this paper, we present a systematic control design for strict-feedback nonlinear systems with output constraints using tangent Barrier Lyapunov Functions (BLFs-Tan). Both symmetric and asymmetric BLF-Tan based backstepping control design methods are developed. It is proved that with the proposed approach and Lyapunov synthesis, the output constraints are not violated, all signals in the closed-loop systems are bounded, and asymptotical tracking is achieved. Through the simulation, we also perform a comparison study between BLF-Tan and the logarithmic BLF (BLF-Log) to illustrate the difference in control performance, and show that the performance of asymmetric BLF-Tan based controller depends on the position of initial tracking error with respect to the transformed constraint boundary.
We introduce a simple approach to account for the ATP consumption and production in the case of mass action models of metabolic pathways including protein turnover. Under some simplifying assumptions, the method makes...
We introduce a simple approach to account for the ATP consumption and production in the case of mass action models of metabolic pathways including protein turnover. Under some simplifying assumptions, the method makes it possible to characterize the optimal rate of enzyme synthesis if the substrate concentrations and other rate constants are known. Furthermore we demonstrate that the proposed approach is capable of the comparison of the efficiency of different feedback laws in dynamic environment, considering time-varying substrate concentration.
Object tracking algorithm based on Meanshift algorithm with fixed kernel bandwidth does not realize object tracking correctly with the scale of object changed. According to this, a scheme of kernel bandwidth adaptive ...
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Object tracking algorithm based on Meanshift algorithm with fixed kernel bandwidth does not realize object tracking correctly with the scale of object changed. According to this, a scheme of kernel bandwidth adaptive adjustment and predictions of object cancroids based on Kalman filter is proposed in this paper. In this algorithm, Object location predicted based on Kalman filter is used to initialize the Meanshift algorithm. The variation tendency of the kernel bandwidth is also determined based on Kalman filter. Experiment results demonstrate that this algorithm can realize the kernel bandwidth adaptive adjustment and object location prediction. The robustness of the tracking algorithm is also enhanced.
The security requirements specification (SRS) is an integral aspect of the development of secured informationsystems and entails the formal documentation of the security needs of a system in a correct and consistent ...
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[Context and motivation] Implicit requirements (ImRs) are defined as requirements of a system which are not explicitly expressed during requirements elicitation, often because they are considered so basic that develop...
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Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applications including computer vision and biom...
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Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applications including computer vision and biomedical informatics. Most of the existing multi-task sparse feature learning algorithms are formulated as a convex sparse regularization problem, which is usually suboptimal, due to its looseness for approximating an l0-type regularizer. In this paper, we propose a non-convex formulation for multi-task sparse feature learning based on a novel nonconvex regularizer. To solve the non-convex optimization problem, we propose a Multi-Stage Multi-Task Feature Learning (MSMTFL) algorithm; we also provide intuitive interpretations, detailed convergence and reproducibility analysis for the proposed algorithm. Moreover, we present a detailed theoretical analysis showing that MSMTFL achieves a better parameter estimation error bound than the convex formulation. Empirical studies on both synthetic and real-world data sets demonstrate the effectiveness of MSMTFL in comparison with the state of the art multi-task sparse feature learning algorithms.
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