Knowledge Tracing (KT) aims to trace students’ knowledge state and predict their performance on questions in online tutoring system. For the moment, most methods assume that all questions are equal to every student, ...
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Knowledge Tracing (KT) aims to trace students’ knowledge state and predict their performance on questions in online tutoring system. For the moment, most methods assume that all questions are equal to every student, that is to say, question itself has not any effect on students’ knowledge state. And they have not considered the effect of question difficulty (QD) on students’ knowledge state. To address this issue, some researchers have worked on the effect of question difficulty on students’ knowledge state. But, they have not considered the effect of question difficulty and knowledge concept difficulty (KCD) on students’ knowledge acquisition ability and knowledge state. In this paper, we propose a novel model called Question Difficulty Estimation Knowledge Tracing (QDEKT) model, which comprehensively considers question difficulty and knowledge concept difficulty and introduces students’ real response and predicted response on questions into the model, to evaluate students’ knowledge acquisition ability and dynamically update students’ knowledge state. Finally, extensive experiments show the effectiveness of our model.
Aiming at the problems that traditional feature selection methods are difficult to optimize and have high computational complexity, this paper proposes a feature selection algorithm: Spark-based Improved Sparrow Searc...
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By integrating edge computing with parallelcomputing, distributed edge computing (DEC) makes use of distributed devices in edge networks to perform computing in parallel, which can substantially reduce service delays...
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ISBN:
(纸本)9781728164120
By integrating edge computing with parallelcomputing, distributed edge computing (DEC) makes use of distributed devices in edge networks to perform computing in parallel, which can substantially reduce service delays. In this paper, we explore DEC that exploits distributed edge devices connected by a wireless network to perform a computation task offloaded from an end device. In particular, we study the fundamental problem of minimizing the delay of executing a distributed algorithm of the computation task. We first establish some structural properties of the optimal communication scheduling policy. Then, given these properties, we characterize the optimal computation allocation policy, which can be found by an efficient algorithm. Next, based on the optimal computation allocation, we characterize the optimal scheduling order of communications for some special cases, and develop an efficient algorithm with a finite approximation ratio to find it for the general case. Last, based on the optimal computation allocation and communication scheduling, we further show that the optimal selection of devices can be found efficiently for some special cases. Our results provide some useful insights for the optimal computation-communication co-design. We evaluate the performance of the theoretical findings using simulations.
Solar energy has become the most prominent renewable energy for electrical power generation of the sustainable development agenda. This project focuses on power quality improvement in the low voltage distribution netw...
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The method for calculating the attitude of the projectile requires high real-time performance and a large amount of calculation. The parallel calculation method of the missile-borne signal is studied. For the linear i...
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ISBN:
(纸本)9781728180250
The method for calculating the attitude of the projectile requires high real-time performance and a large amount of calculation. The parallel calculation method of the missile-borne signal is studied. For the linear iterative equations in the attitude solution algorithm, a parallel calculation based on Newton method is proposed. This method decomposes linear iterative equations in parallel to form a distributed multi-core parallel solution process, which can effectively optimize the algorithm solution time. Experiments were conducted through four sets of data with different data lengths and frame rates to verify the execution efficiency of multi-core parallelcomputing.
In sub-second stream computing, the answer to a complex query usually depends on operations of aggregation or join on streams, especially multi-way theta join. Some attribute keys are not distributed uniformly, which ...
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ISBN:
(纸本)9781728190747
In sub-second stream computing, the answer to a complex query usually depends on operations of aggregation or join on streams, especially multi-way theta join. Some attribute keys are not distributed uniformly, which is called the data intrinsic skew problem, such as taxi car plate in GPS trajectories and transaction records, or stock code in stock quotes and investment portfolios etc. In this paper, we define the concept of key redundancy for single stream as the degree of data intrinsic skew, and joint key redundancy for multi-way streams. We present an execution model for multi-way stream theta joins with a fine-grained cost model to evaluate its performance. We propose a solution named Group Join (GroJoin) to make use of key redundancy during transmission and execution in a cluster. GroJoin is adaptive to data intrinsic skew in the way that it depends on the grouping condition we find out, i.e., the selectivity of theta join results should be smaller than 25%. Experiments are carried out by our MS-Generator to produce multi-way streams, and the simulation results show that GroJoin can decrease at most 45% transmission overheads with different key redundancies and value-key proportionality coefficients, and reduce at most 70% query delay with different key distributions. We further implement GroJoin in Multi-Way Stream Theta Join by Spark Streaming. The experimental results demonstrate that there are about 40%-50% join latency reduced after our optimization with a very small computation cost.
Reinforcement learning is used in many applications in artificial intelligence fields such as computer vision and environmental contextualized decision-making scenarios; virtual human swarms provide research direction...
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Reinforcement learning is used in many applications in artificial intelligence fields such as computer vision and environmental contextualized decision-making scenarios; virtual human swarms provide research directions for multi-intelligent body collaboration and environmental field detection in multi-intelligent body swarms. Football is a group sport, which is characterized by its holistic nature, confrontation, versatility, and ease of implementation, as well as the characteristics of both individual intelligence and group intelligence, and is a typical application scenario for multi-intelligence collaboration. This paper takes football as the research object to study the team collaboration problem and team gaming problem of the virtual human swarm in a specific environment and solves the cooperation and competition relationship between intelligence with reinforcement learning of multi-intelligence training.
Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning oper...
Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial *** this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder. Our codes are available at: https://***/CGCL-codes/AdvEncoder.
We introduce SpDISTAL, a compiler for sparse tensor algebra that targets distributed systems. SpDISTAL combines separate descriptions of tensor algebra expressions, sparse data structures, data distribution, and compu...
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We introduce SpDISTAL, a compiler for sparse tensor algebra that targets distributed systems. SpDISTAL combines separate descriptions of tensor algebra expressions, sparse data structures, data distribution, and computation distribution. Thus, it enables distributed execution of sparse tensor algebra expressions with a wide variety of sparse data structures and data distributions. SpDISTAL is implemented as a C++ library that targets a distributed task-based runtime system and can generate code for nodes with both multi-core CPUs and multiple GPUs. SpDISTAL generates distributed code that achieves performance competitive with hand-written distributed functions for specific sparse tensor algebra expressions and that outperforms general interpretation-based systems by one to two orders of magnitude.
The regional scale source network load storage coordination system is an effective organization form to increase the proportion of clean energy and absorb distributed renewable energy. However, due to the different ow...
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