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共 568 条记录，以下是21-30 订阅

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Optimizing Player Engagement in an Educational Virtual Game through Fuzzy logic-based Challenge Adaptation

Optimizing Player Engagement in an Educational Virtual Game ...

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South Eastern European Design Automation, Computer Engineering, Computer Networks and Social Media conference (SEEDA-CECNSM)

作者： Akrivi Krouska Christos Troussas Yorghos Voutos Phivos Mylonas Cleo Sgouropoulou Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

Adaptive virtual learning environments provide an ideal foundation for enhancing personalized learning experience. Moreover, the incorporation of game elements enhances motivation levels, further enhancing the potential for improving learning outcomes. this paper explores the integration of fuzzy logic as a dynamic adaptivity tool in virtual educational games. By employing user-specific characteristics as input, including student progress, gaming time, and knowledge, the output of the fuzzy logic system represents the challenging level presented to the player in the educational game. then, a set of rules is applied to tailor the complexity and pacing of challenges presented to learners. this adaptation extends to virtual character behavior, the learning path, and task complexity, aligning with learning objectives and proficiency levels. the study presents a practical implementation of this fuzzy logic-driven adaptivity mechanism in a virtual game environment designed for C++ programming education. the virtual game has been evaluated and the results of the evaluation showcase the potential of this approach in enhancing the learning process by tailoring the environment to learners’ specific needs and preferences.

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SequalsK-A Bidirectional Swift-Kotlin-Transpiler 8

SequalsK-A Bidirectional Swift-Kotlin-Transpiler

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IEEE/ACM 8th International conference on Mobile Software Engineering and Systems (MobileSoft)

作者： Schultes, Dominik TH Mittelhessen Friedberg Germany

ISBN: (纸本)9781728187112

Developing two separate versions of an app for iOS and Android causes considerable efforts. therefore, a lot of cross-platform development frameworks are available that are able to produce apps for both platforms out of a single code base. However, there are tradeoffs that are connected with these frameworks, in particular, a high tool dependency. therefore, we propose to stick with native development but to take advantage of the shrinking gap between both platforms due to the fact that the current programming languages, Swift for iOS and Kotlin for Android, are considerably more alike than their respective predecessors. Relating to the model-view-controller design pattern, we propose to automatically transpile the model part (which includes the business logic) of the app in a bidirectional fashion, i.e., from Kotlin to Swift and vice versa. this way, iOS experts can concentrate on optimizing the user interface (view- and controller-part) for iOS, and Android experts can do the same for Android, but all developers can jointly work on the shared model part: each developer can read, correct, and enhance the source code of the model using their own preferred programming language;the resulting version is then transpiled to an equivalent version in the other language. We present a working prototype of a transpiler-which we call SequalsK-that supports the majority of the important constructs of both languages and is able to generate syntactically and semantically correct Kotlin code out of Swift code and vice versa. In a case study we show that the model part of a board game app can be transpiled in both directions without any limitations. Starting with a working Swift version, the Android version can be derived with little manual effort: the automatically transpiled model part forms 86 percent of the resulting source code.

关键词： mobile app transpilers transcompilers Swift Kotlin cross-platform

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A Probabilistic Approach for Scheduling of Wind-Energy-Integrated Power Systems Incorporating Dynamic Lines and Transformers Rating

A Probabilistic Approach for Scheduling of Wind-Energy-Integ...

引用

Technology and Energy Management (ICTEM), International conference on

作者： Mohammad Akhlaghi Zahra Moravej Amir Bagheri Faculty of Electrical & Computer Engineering Semnan University Semnan Iran Department of Electrical Engineering Faculty of Engineeing University of Zanjan Zanjan Iran

In today's electric power systems, transmission congestion and integration of renewable-based power generations are challenging issues. To tackle with these problems, power system operators can make use of smart-grid technologies. Dynamic transformer rating (DTR) and dynamic line rating (DLR) are employed as two smart-grid technologies for stochastic day-ahead optimal power flow (OPF) under uncertainty condition. the uncertainty scenarios are generated using Monte-Carlo simulation where the K-means clustering algorithm is employed for the scenario reduction purpose. A convexified version of AC power flow and dynamic rating relations are employed to formulate the optimization problem as a mixed-integer quadratically-constrained programming (MIQCP). the conducted approach is applied to the IEEE-24-bus network in GAMS, and the simulation results are investigated to validate efficiency of the proposed method. the simulations show that the wind power curtailment and load shedding becomes zero in the case of utilizing dynamic ratings. Also, a total cost reduction of 41% is observed by the aid of DTR and DLR.

关键词： Uncertainty Power system dynamics Voltage Wind power generation Dynamic scheduling Transformers Probabilistic logic

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Multidimensional Prediction Regions of Wind Power for Robust Economic Dispatch

Multidimensional Prediction Regions of Wind Power for Robust...

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IEEE conference on Energy Internet and Energy System Integration (EI2)

作者： Kaiming Zhang Chenxin Yue Can Wan Yuqi Xu Hongpei Qin Zhiqiang He College of Electrical Engineering Zhejiang University Hangzhou China Polytechnic Institute Zhejiang University Hangzhou China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

the dramatic increase in non-dispatchable wind power generation poses a significant challenge to power system operations. In this context, probabilistic forecasting becomes increasingly crucial for power systems, as it quantifies the uncertainty of volatile energy sources and supports various decisionmaking processes. this paper proposes a novel nonparametric probabilistic forecasting-based robust economic dispatch (NPF-RED) paradigm aimed at enhancing power system reliability and efficiency. the proposed NPF-RED methodology captures probabilistic information regarding uncertainties by evaluating the feature similarity between prediction targets and historical observations. A minimum volume uncertainty set in the form of multidimensional prediction regions (MPRs) based on similar samples is constructed using similar samples and optimized as a mixed integer programming (MIP). the proposed MPRs serve as inputs of subsequential operation decisions, which can maintain solution robustness while reducing conservativeness simultaneously. Comprehensive experiments conducted on IEEE 14-bus and 118-bus test systems using actual wind power data demonstrate that at a similar risk level, the NPF-RED approach effectively reduces the total cost by 2.56% and 0.50% in the 14-bus and 118-bus systems, respectively.

关键词： Integer programming Uncertainty Costs System integration Wind power generation Probabilistic logic Robustness Power system reliability Wind forecasting Forecasting

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8th International conference on Computers Communications and Control, ICCCC 2020

8th International Conference on Computers Communications and...

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8th International conference on Computers Communications and Control, ICCCC 2020

ISBN: (纸本)9783030536503

the proceedings contain 26 papers. the special focus in this conference is on Computers Communications and Control. the topics include: Experimenting with beta distributions for approximating hammocks’ reliability;general frameworks for designing arithmetic components for residue number systems;the shape of the reliability polynomial of a hammock network;Queuing theory application on DTN buffer management;Redesign of a conference from in-person to online. case study: ICCCC;proposal of a multi-standard model for measuring maturity business levels with reference to information security standards and controls;IT solutions for big data processing and analysis in the finance and banking sectors;automatons immersed in ocean currents for transformation of biomass into fuel;challenges porting blockchain library to opencl;using two-level context-based predictors for assembly assistance in smart factories;visual analysis of multidimensional scaling using geogebra;edge computing in real-time electricity consumption optimization algorithm for smart grids;the study of trajectories of the development of state capacity using ordinal-invariant pattern clustering and hierarchical cluster analysis;a micro simulation approach for a sustainable reduction traffic jam;empirical versus analytical solutions to full fuzzy linear programming;on recursively defined combinatorial classes and labelled trees;critical analysis of faults in operation of energy systems using fuzzy logic;fuzzy-logic based diagnosis for high voltage equipment predictive maintenance;making a multi-criteria analysis model for choosing an erp for smes in a km world;a multi-objective model for devices procurement with application in health care.

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Distributed Probability Orchestrating for Probabilistic In-Band Network Telemetry

Distributed Probability Orchestrating for Probabilistic In-B...

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International conference on Computer and Communications (ICCC)

作者： Zhongxiang Wei Wei Chen Liyuan Gu Ye Tian School of Computer Science and Technology University of Science and Technology of China

In-band telemetry (INT) is an emerging framework for network diagnosis, and probabilistic in-band telemetry (PINT) is its variant which consumes less bandwidth with a probabilistic sampling method. However, as we show in this paper, PINT causes non-uniform sampling probability at different switches within a topology, resulting in measurement accuracy not optimal. therefore, it is meaningful to orchestrate probability in per-path and per-hop granularity. In this paper, we address this problem by formulating it as convex programming models and solve them. We also apply the solution to satisfy PINT's digest-overwritten fact. the method is implemented in software programmable switch with p4, a de facto standard for data plane programmability. Evaluation on a FatTree testbed shows that our method overperforms PINT with negligible extra bandwidth consumption.

关键词： Network topology Bandwidth Switches programming Probabilistic logic Software Topology

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Human-Vehicle Cooperative Driving with Multi-Lane Decision-Making via Incomplete Information Game

Human-Vehicle Cooperative Driving with Multi-Lane Decision-M...

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conference on Vehicle Control and Intelligence (CVCI)

作者： Jiawen Sun Wenyu Li Qiuting Lin Feng Duan Zhenning Li Shengbo Eben Li College of Artificial Intelligence Nankai University Tianjin China Tianjin Key Laboratory of IBCI&IR Nankai University Tianjin China State Key Laboratory of IoTSC University of Macau Macau China School of Vehicle and Mobility College of AI Tsinghua University Beijing China

ISBN: (数字)9798331504892

ISBN: (纸本)9798331504908

Nowadays, human-vehicle cooperative driving is still an important solution for intelligent transportation in unstructured scenarios. However, the conflict between the driver and the automated system for multi-lane decision-making remains to be solved. When leaving the intended path, driver's expected trajectory cannot be identified by the automated system, re-sulting in safety hazards related to cooperation failure under incomplete decision information. this paper proposes a trajectory probability game control (TPGC) method to model the situation and intend to relieve the conflict. A Bayesian Nash equilibria strategy is derived using the proposed probabilistic dual iterative approximate dynamic programming (PADP). the equilibrium indicates a stable state for the human-vehicle system given the probability of driver's trajectory selection, where the uncertainty of the driver is reduced. A multi-lane simulation scenario is constructed to verify the effectiveness of the proposed control and solution method. the experimental results indicate that the designed controller can be applied to various trajectory selections and maintain an average lateral error within 0.17m on multi-lane roads.

关键词： Uncertainty Roads Decision making Games Probabilistic logic Trajectory Bayes methods PD control Vehicle dynamics Vehicles

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Research on Elastic Extension of Multi Type Resources for OpenMP Program

Research on Elastic Extension of Multi Type Resources for Op...

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Junfeng Zhao Xuesong Gao Yuancong Li College of Computer Science Inner Mongolia University Hohhot China

OpenMP is one of the most popular programming models in the field of parallel computing, which is widely used in multi-threaded programming with shared memory. But with the continuous change of program computing scale, the problem of resource supply has become increasingly prominent. Making use of the cloud computing to dynamically provide resources for OpenMP program is a meaningful work. Based on the internal structure characteristics of OpenMP program, a flexible elastic resource allocation strategy is designed, and a load prediction method combining active and reactive prediction based on machine learning is proposed to realize the load prediction of OpenMP program. then, the fuzzy controller is used to coordinate the resources and infer the CPU and memory resources needed by the program in the next cycle. On this basis, the middleware OPEC is designed and implemented to realize the dynamic allocation of cloud resources during the operation of OpenMP program. Parsec, a benchmark test suite based on shared memory, is selected for experimental verification. the experimental results show that the solution proposed in this paper can dynamically provide a variety of resources for OpenMP program to meet program running requirements, effectively improve resource utilization efficiency and reduce program running costs.

关键词： Fuzzy logic Cloud computing Fuzzy control Machine learning Prediction methods programming Parallel processing

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LEARN TO EXPLAIN EFFICIENTLY VIA NEURAL logic INDUCTIVE LEARNING 8

LEARN TO EXPLAIN EFFICIENTLY VIA NEURAL LOGIC INDUCTIVE LEAR...

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8th International conference on Learning Representations, ICLR 2020

作者： Yang, Yuan Song, Le Georgia Institute of Technology United States

the capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems. In this work, we study the learning to explain problem in the scope of inductive logic programming (ILP). We propose Neural logic Inductive Learning (NLIL), an efficient differentiable ILP framework that learns first-order logic rules that can explain the patterns in the data. In experiments, compared with the state-of-the-art methods, we find NLIL can search for rules that are x10 times longer while remaining x3 times faster. We also show that NLIL can scale to large image datasets, i.e. Visual Genome, with 1M entities. © 2020 8th International conference on Learning Representations, ICLR 2020. All rights reserved.

关键词： Large dataset

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Backup Resource Allocation Model with Two-Stage Probabilistic Protection

Backup Resource Allocation Model with Two-Stage Probabilisti...

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IEEE conference on Network Softwarization (NetSoft)

作者： Kento Yokouchi Fujun He Eiji Oki Kyoto University Kyoto Japan

ISBN: (数字)9781665406949

ISBN: (纸本)9781665406956

this paper proposes a backup resource allocation model with two-stage probabilistic protection to minimize the total required backup capacity for multiple simultaneous failures of physical machines (PMs). Probabilistic protection ensures that the probability that the PM used for backup fails to backup due to lack of computing capacity does not exceed a given survivability parameter. the proposed model protects the primary virtual machines by allocating computing capacity to backup PMs with probabilistic protection. Since it is uncertain which primary PMs fail, we apply robust optimization to the probabilistic protection. By using a table that takes into account the survivability parameter and the failure probability of PMs, this proposed model is formulated as a mixed integer linear programming problem. the proposed model extends the probabilistic protection to two stages; the VMs that fail to be protected in the first stage are protected in the second stage to achieve the probabilistic protection with the final survivability parameter. this model can reduce the total required backup capacity compared to the conventional model with one-stage probabilistic protection.

关键词： Computational modeling conferences Probabilistic logic Virtual machining Numerical models Mixed integer linear programming Resource management

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