Recommending suitable exercises and providing the reasons for these recommendations is a highly valuable task, as it can significantly improve students' learning efficiency. Nevertheless, the extensive range of exercise resources and the diverse learning capacities of students present a notable difficulty in recommending exercises. Collaborative filtering approaches frequently have difficulties in recommending suitable exercises, whereas deep learning methods lack explanation, which restricts their practical use. To address these issue, this paper proposes KG4EER, an explainable exercise recommendation with a knowledge graph. KG4EER facilitates the matching of various students with suitable exercises and offers explanations for its recommendations. More precisely, a feature extraction module is introduced to represent students' learning features, and a knowledge graph is constructed to recommend exercises. This knowledge graph, which includes three primary entities - knowledge concepts, students, and exercises - and their interrelationships, serves to recommend suitable exercises. Extensive experiments conducted on three real-world datasets, coupled with expert interviews, establish the superiority of KG4EER over existing baseline methods and underscore its robust explainability.

Effective exercise recommendation is crucial for guiding students' learning trajectories and fostering their interest in the subject matter. However, the vast exercise resource and the varying learning abilities of individual students pose a significant challenge in selecting appropriate exercise questions. Collaborative filtering-based methods often struggle with recommending suitable exercises, while deep learning-based methods lack explanation, limiting their practical adoption. To address these limitations, this paper proposes KG4Ex, a knowledge graph-based exercise recommendation method. KG4Ex facilitates the matching of diverse students with suitable exercises while providing recommendation reasons. Specifically, we introduce a feature extraction module to represent students' learning states and construct a knowledge graph for exercise recommendation. This knowledge graph comprises three key entities (knowledge concepts, students, and exercises) and their interrelationships, and can be used to recommend suitable exercises. Extensive experiments on three real-world datasets and expert interviews demonstrate the superiority of KG4Ex over existing baseline methods and highlight its strong explainability.

Cognitive diagnosis is a crucial task in the field of educational measurement and psychology, which aims to diagnose the strengths and weaknesses of participants. Existing cognitive diagnosis methods only consider which of knowledge concepts are involved in the knowledge components of exercises, but ignore the fact that different knowledge concepts have different effects on practice scores in actual learning situations. Therefore, researchers need to reshape the learning scene by combining the multi-factor relationships between knowledge components. In this paper, in order to more comprehensively simulate the interaction between students and exercises, we developed a neural network-based CDMFKC model for cognitive diagnosis. Our method not only captures the nonlinear interaction between exercise characteristics, student performance, and their mastery of each knowledge concept, but also further considers the impact of knowledge concepts by designing the difficulty and discrimination of knowledge concepts, and uses multiple neural layers to model their interaction so as to obtain accurate and interpretable diagnostic results. In addition, we propose an improved CDMFKC model with guessing parameter and slipping parameter designed by knowledge concept proficiency and student proficiency vectors. We validate the performance of these two diagnostic models on six real datasets. The experimental results show that the two models have better effects in the aspects of accuracy, rationality and interpretability.

Abstract In the large scale Internet of things, edge gateway (EG) deployment is used to find the minimal number of gateways required in the network and their optimal locations under the design constraints to meet different service requirements, which is one significant issue for improving network performances. We formulate the EG deployment problem as a k ‐median problem with some constraints, which is known as a NP‐hard problem. For that, we propose a new heuristic based on simulated annealing with external penalty function (SA‐AEP) for its solution. The external penalty function is used to transform the multi‐constrained optimization problem into a single constraint one. Also the complexity of proposed heuristic algorithm is analyzed. In addition, in order to evaluate the proposed algorithm, we compare it with an adaptive variable‐length particle swarm optimization algorithm with varying lengths. Simulation results show that the proposed SA‐AEP algorithm has much better performances on network cost and efficiency than other algorithms when the higher service rate is required.

Narrow passages in changing environments create huge difficulties, since locations and shapes of narrow passages in Configuration Space(C-space) change frequently. It is very important for a planner to identify narrow passages in real time and boost valid points within them effectively. A novel narrow passage predictive model for designing a path planner in changing environments is proposed in this paper. Firstly, an Expanded Dynamic Bridge Builder is presented to identify narrow passages rapidly with validity-toggle sampling points in C-space. Secondly, the predictive model is adopted to sample possibly free points within these narrow passages without invoking any collision detection in order to avoid intense computational complexity. The predictive model is obtained by the famous classification method of Support Vector Machine (SVM). A new feature, which includes a group of points' distance and validity information, is proposed in SVM training process to capture approximate structure of local narrow passages. Therefore, the predictive model can excavate the hidden similar structure of local narrow passages. Experiments carried out with two 6-DOFs manipulators show that our approach gain higher success rate of planning and time efficiency than other related methods.

With the rapid increasing demand on the data traffic and energy efficiency, centralized radio access network architecture is proposed for next generation communication. Similar to the VM (Virtual Machine) running on the cloud computer servers, V-BS (Virtual Base Station) is proposed. Problem exists since characteristics of V-BS PHY layer tasks are different from traditional computing tasks in the cloud-computing, such as time constraint, data dependency and heterogeneous mapping. This paper proposed a new virtualization method based on ASIP (Application Specified Instruction Set Processor) architecture, with consideration of both scheduling efficiency and resource utilizing. Experiments are made to evaluate the validation of the new virtualization method.

The paper presents a switched reluctance wind power generator system. The elements of the system is introduced, including the three-phase 12/8 structure switched reluctance generator, rotor position detector, three-phase bifilar winding excited power converter and the controller based on the Intel MCS-SOC196KB CPU. The mathematical models of the main circuit of the exciting power converter are given during the periods of excitation and generation. The mathematical model of the electromagnetic power of the generator is also given. The constant-power output control with the constant output voltage could be implemented based on the fixed conducting angle PWM control strategy. A 5KW switched reluctance variable speed wind generator system prototype is developed. The output voltage is DC 110V, the excited voltage is DC 24 V. The turn ratio of the main winding and auxiliary winding is 1:5. The experimental test results of the prototype and the phase current waveform of the prototype are presented. It is shown that the developed generator system at the wide wind velocity range has the high systematic efficiency and the low peak value of phase current with the adopted fixed conducting angle PWM control strategy. The bifilar winding power converter contributes to excite the phase winding with low voltage and output the electrical energy with high voltage.