Towards Sensor-free Academic Emotion Prediction in Programming Environment

he transition from traditional learning to practice-oriented programming learning will bring learners discomfort.The discomfort quickly breeds negative emotions when encountering programming difficulties,which leads the learner to lose interest in programming or even give up.Emotion plays a crucial role in learning.Educational psychology research shows that positive emotion can promote learning performance,increase learning interest and cultivate creative thinking.Accurate recognition and interpretation of programming learners’emotions can give them feedback in time,and adjust teaching strategies accurately and individually,which is of considerable significance to improve effects of programming learning and education.The existing methods of sensor-free emotion prediction include emotion prediction based on keyboard dynamic,mouse interaction data and interaction logs,respectively.However,none of the three studies considered the temporal characteristics of emotion,resulting in low recognition accuracy.For the first time,this paper proposes an emotion prediction model based on time series and context information.Then,we establish a Bi-recurrent neural network,obtain the time sequence characteristics of data automatically,and explore the application of deep learning in the field of Academic Emotion prediction.The results show that the classification ability of this model is much better than that of the original LSTM(Long-Short Term Memory),GRU(Gate Recurrent Unit)and RNN(Re-current Neural Network),and this model has better generalization ability.

Dynamic Behavior Modeling in Multi-Agent System By Evolutionary Programming

In a multi-agent system, each agent must adapt itself to the environment and coordinate with other agents dynamically. TO predict or cooperate with the behavior of oiller agents. An agent should dynamically establish and evolve the cooperative behavior model of itself. In this paper, we represent the behavior model of an agent as a f-mite state machine and propose a new method of dynamically evolving the behavior model of an agent by evolutionary programming.

Enhance Information Flow Tracking with Function Recognition

With the spread use of the computers, a new crime space and method are presented for criminals. Thus computer evidence plays a key part in criminal cases. Traditional computer evidence searches require that the computer specialists know what is stored in the given computer. Binary-based information flow tracking which concerns the changes of control flow is an effective way to analyze the behavior of a program. The existing systems ignore the modifications of the data flow, which may be also a malicious behavior. Thus the function recognition is introduced to improve the information flow tracking. Function recognition is a helpful technique recognizing the function body from the software binary to analyze the binary code. And that no false positive and no false negative in our experiments strongly proves that our approach is effective.

Programming Shape-Morphing Behavior of Zwitterionic Polymer/Liquid Crystal Composite with Humidity-responsive Self-healing Performance

In this study,a zwitterionic polymer/liquid crystals composite film with programming shape-morphing behavior and humidityresponsive self-healing performance was prepared by blending a zwitterionic polymer and liquid crystalline azobenzene compound in solution,followed by film-forming in a mold without tedious or multistep synthetic route.The as-obtained zwitterionic polymer/liquid crystal composite film exhibited programming shape-morphing behavior under different stimuli.In this process,the temporary shape of the composite film was memorized after the removal of the stimuli.Such characteristics would fit the requirements of intelligence and energy-saving for stimuliresponsive shape-changing materials.Moreover,the composite film showed humidity-responsive self-healing performances under wet conditions at room temperature.In summary,the simple design and preparation route of the zwitterionic polymer/liquid crystal composite film with programming shape-morphing behavior and mild condition-responsive self-healing performance look promising for the fabrication and practical application of novel photo-driven devices and soft robotics.

OPTIMAL PRICING AND INVENTORY POLICY WITH ORDER CANCELATIONS UNDER THE CASH-ON-DELIVERY PAYMENT SCHEME

Considering a periodic review system where the online seller allows the customers to pay when the products are delivered to them(referred as cash-on-delivery payment scheme in this paper),the authors investigate the seller's joint pricing and inventory control policy with a finite planning horizon.In particular,the authors incorporate the customers' possible order cancellation behavior with the cash-on-delivery scheme.It can be proven that the base-stock list price policy is optimal under mild conditions.The authors also analyze the impact of the customers' forward looking behavior on the optimal policy.

Model adjointization and its cost

In this article, the least program behavior decomposition method (LPBD) is put forward from a program structure point of view. This method can be extensively used both in algorithms of automatic differentiation (AD) and in tools design, and does not require programs to be evenly separable but the cost in terms of operations count and memory is similar to methods using checkpointing. This article starts by summarizing the rules of adjointization and then presents the implementation of LPBD. Next, the definition of the separable program space, based on the fundamental assumptions (FA) of automatic differentiation, is given and the differentiation cost functions are derived. Also, two constants of fundamental importance in AD, s and m, are derived under FA. Under the assumption of even separability, the adjoint cost of simple and deep decomposition is subsequently discussed quantitatively using checkpointing. Finally, the adjoint costs in terms of operations count and memory through the LPBD method are shown to be uniformly dependent on the depth of structure or decomposition.