Personal customized recommendation system reflecting purchase criteria and product reviews sentiment analysis

As the size of the e-commerce market grows, the consequences of it are appearing throughout society. The business environment of a company changes from a product center to a user center and introduces a recommendation system. However, the existing research has shown a limitation in deriving customized recommendation information to reflect the detailed information that users consider when purchasing a product. Therefore, the proposed system reflects the user's subjective purchasing criteria in the recommendation algorithm. And conduct sentiment analysis of product review data. Finally, the final sentiment score is weighted according to the purchase criteria priority, recommends the results to the user

Generating Goal-directed Visuomotor Plans with Supervised Learning using a Predictive Coding Deep Visuomotor Recurrent Neural Network

The ability to plan and visualize object manipulation in advance is vital for both humans and robots to smoothly reach a desired goal state. In this work, we demonstrate how our predictive coding based deep visuomotor recurrent neural network (PDVMRNN) can generate plans for a robot to manipulate objects based on a visual goal. A Tokyo Robotics Torobo Arm robot and a basic USB camera were used to record visuo-proprioceptive sequences of object manipulation. Although limitations in resolution resulted in lower success rates when plans were executed with the robot, our model is able to generate long predictions from novel start and goal states based on the learned patterns

Blank Collapse: Compressing CTC emission for the faster decoding

Connectionist Temporal Classification (CTC) model is a very efficient method for modeling sequences, especially for speech data. In order to use CTC model as an Automatic Speech Recognition (ASR) task, the beam search decoding with an external language model like n-gram LM is necessary to obtain reasonable results. In this paper we analyze the blank label in CTC beam search deeply and propose a very simple method to reduce the amount of calculation resulting in faster beam search decoding speed. With this method, we can get up to 78% faster decoding speed than ordinary beam search decoding with a very small loss of accuracy in LibriSpeech datasets. We prove this method is effective not only practically by experiments but also theoretically by mathematical reasoning. We also observe that this reduction is more obvious if the accuracy of the model is higher.Comment: Accepted in Interspeech 202

Achieving Synergy in Cognitive Behavior of Humanoids via Deep Learning of Dynamic Visuo-Motor-Attentional Coordination

The current study examines how adequate coordination among different cognitive processes including visual recognition, attention switching, action preparation and generation can be developed via learning of robots by introducing a novel model, the Visuo-Motor Deep Dynamic Neural Network (VMDNN). The proposed model is built on coupling of a dynamic vision network, a motor generation network, and a higher level network allocated on top of these two. The simulation experiments using the iCub simulator were conducted for cognitive tasks including visual object manipulation responding to human gestures. The results showed that synergetic coordination can be developed via iterative learning through the whole network when spatio-temporal hierarchy and temporal one can be self-organized in the visual pathway and in the motor pathway, respectively, such that the higher level can manipulate them with abstraction.Comment: submitted to 2015 IEEE-RAS International Conference on Humanoid Robot

A novel method for crystalline silicon solar cells with low contact resistance and antireflection coating by an oxidized Mg layer

One of the key issues in the solar industry is lowering dopant concentration of emitter for high-efficiency crystalline solar cells. However, it is well known that a low surface concentration of dopants results in poor contact formation between the front Ag electrode and the n-layer of Si. In this paper, an evaporated Mg layer is used to reduce series resistance of c-Si solar cells. A layer of Mg metal is deposited on a lightly doped n-type Si emitter by evaporation. Ag electrode is screen printed to collect the generated electrons. Small work function difference between Mg and n-type silicon reduces the contact resistance. During a co-firing process, Mg is oxidized, and the oxidized layer serves as an antireflection layer. The measurement of an Ag/Mg/n-Si solar cell shows that Voc, Jsc, FF, and efficiency are 602 mV, 36.9 mA/cm2, 80.1%, and 17.75%, respectively. It can be applied to the manufacturing of low-cost, simple, and high-efficiency solar cells