Existing deep reinforcement learning (RL) are devoted to research applications on video games, e.g., The Open Racing Car Simulator (TORCS) and Atari games. However, it remains under-explored for vision-based autonomous urban driving navigation (VB-AUDN). VB-AUDN requires a sophisticated agent working safely in structured, changing, and unpredictable environments; otherwise, inappropriate operations may lead to irreversible or catastrophic damages. In this work, we propose a deductive RL (DeRL) to address this challenge. A deduction reasoner (DR) is introduced to endow the agent with ability to foresee the future and to promote policy learning. Specifically, DR first predicts future transitions through a parameterized environment model. Then, DR conducts self-assessment at the predicted trajectory to perceive the consequences of current policy resulting in a more reliable decision-making process. Additionally, a semantic encoder module (SEM) is designed to extract compact driving representation from the raw images, which is robust to the changes of the environment. Extensive experimental results demonstrate that DeRL outperforms the state-of-the-art model-free RL approaches on the public CAR Learning to Act (CARLA) benchmark and presents a superior performance on success rate and driving safety for goal-directed navigation.

Wireless sensor network (WSN) can reduce human labour and monitoring difficulties in forest fire prevention and environment monitoring due to its low power consumption, small size, self‐organising, flexible setting, and unattended operation. However, the limited WSN hardware resources and power capacity, the practical application requirements such as real‐time monitoring and timely warning of emergencies, pose higher challenges to WSN on its lifespan, reliability, and real‐time performance. This paper proposes a QoS adaptive and energy aware cross‐layer opportunistic routing protocol (QE‐COR). The protocol sets an adaptive QoS function Q i comprehensively considering multiple metrics, through which the best forwarding node can be selected, and the QoS of transmission can adaptively adjust according to the data's needs. The protocol can also select a standby node to start retransmission when forwarding errors occur. A RTS‐QACK and an ASS‐DATA‐SACK response mechanisms are designed to improve the transmission efficiency and avoid message conflicts. The sleep mechanism of MAC layer related with node's working state enables dynamical switching between active and sleep modes, which improves the energy utilisation and the flexibility of the network. Compared with similar opportunistic routing protocols, QE‐COR can improve energy utilisation and extend network lifetime, while ensuring transmission reliability and end‐to‐end delay performance.

Restricted Boltzmann Machines (RBMs) are widely used in modern machine learning tasks. Existing implementations are limited in network size and training throughput by available DSP resources. In this work we propose a new algorithm and architecture for FPGAs called dropout-RBM (dRBM) system. Compared to the state-of-art design methods on the same FPGA, dRBM with a dropout rate 0.5 doubles the maximum affordable network size using only half of DSP and BRAM resources. This is achieved by an application of a technique called dropout, which is a relatively new method used to avoid overfitting of data. Here we instead apply dropout as a technique for reducing the required DSPs and BRAM resources, while also having the side-effect of increasing robustness of training. Also to improve the processing throughput, we propose a multi-mode matrix multiplication module that maximizes the DSP efficiency. For the MNIST classificationbenchmark, a Stratix IV EP4SGX530 FPGA running dRBM is 34x faster than a single-precision Matlab implementation running on Intel i7 2.9GHz CPU.

Semi-supervised learning algorithms are widely used to build strong learning models when there are not enough labeled instances. Some semi-supervised learning algorithms, including co-training and co-EM, use multiple views to build learning models. P

Bayesian network classifiers have been widely used for classification problems. Given a fixed Bayesian network structure, parameters learning can take two different approaches: generative and discriminative learning. While generative parameter learning is more efficient, discriminative parameter learning is more effective. In this paper, we propose a simple, efficient, and effective discriminative parameter learning method, called Discriminative Frequency Estimate (DFE), which learns parameters by discriminatively computing frequencies from data. Empirical studies show that the DFE algorithm integrates the advantages of both generative and discriminative learning: it performs as well as the state-of-the-art discriminative parameter learning method ELR in accuracy, but is significantly more efficient.

It is well known that naive Bayes performs surprisingly well in classification, but its probability estimation is poor. AUC (the area under the receiver operating characteristics curve) is a measure different from classification accuracy and probability estimation, which is often used to measure the quality of rankings. Indeed, an accurate ranking of examples is often more desirable than a mere classification. What is the general performance of naive Bayes in yielding optimal ranking, measured by AUC? In this paper, we study it systematically by both empirical experiments and theoretical analysis. In our experiments, we compare naive Bayes with a state-of-the-art decision-tree learning algorithm C4.4 for ranking, and some popular extensions of naive Bayes which achieve a significant improvement over naive Bayes in classification, such as the selective Bayesian classifier (SBC) and tree-augmented naive Bayes (TAN). Our experimental results show that naive Bayes performs significantly better than C4.4 and comparably with TAN. This provides empirical evidence that naive Bayes performs well in ranking. Then we analyse theoretically the optimality of naive Bayes in ranking. We study two example problems: conjunctive concepts and m-of-n concepts, which have been used in analysing the performance of naive Bayes in classification. Surprisingly, naive Bayes performs optimally on them in ranking, even though it does not in classification. We present and prove a sufficient condition for the optimality of naive Bayes in ranking. From both empirical and theoretical studies, we believe that naive Bayes is a competitive model for ranking.

The structure of a Bayesian network (BN) encodes variable independence. Learning the structure of a BN, however, is typically of high computational complexity. In this paper, we explore and represent variable independence in learning conditional probability tables (CPTs), instead of in learning structure. A full Bayesian network is used as the structure and a decision tree is learned for each CPT. The resulting model is called full Bayesian network classifiers (FBCs). In learning an FBC, learning the decision trees for CPTs captures essentially both variable independence and context-specific independence. We present a novel, efficient decision tree learning, which is also effective in the context of FBC learning. In our experiments, the FBC learning algorithm demonstrates better performance in both classification and ranking compared with other state-of-the-art learning algorithms. In addition, its reduced effort on structure learning makes its time complexity quite low as well.

Naive Bayes is an effective and efficient learning algorithm in classification. In many applications, however, an accurate ranking of instances based on the class probability is more desirable. Unfortunately, naive Bayes has been found to produce poor probability estimates. Numerous techniques have been proposed to extend naive Bayes for better classification accuracy, of which selective Bayesian classifiers (SBC) (Langley & Sage, 1994), tree-augmented naive Bayes (TAN) (Friedman et al., 1997), NBTree (Kohavi, 1996), boosted naive Bayes (Elkan, 1997), and AODE (Webb et al., 2005) achieve remarkable improvement over naive Bayes in terms of classification accuracy. An interesting question is: Do these techniques also produce accurate ranking? In this paper, we first conduct a systematic experimental study on their efficacy for ranking. Then, we propose a new approach to augmenting naive Bayes for generating accurate ranking, called hidden naive Bayes (HNB). In an HNB, a hidden parent is created for each attribute to represent the influences from all other attributes, and thus a more accurate ranking is expected. HNB inherits the structural simplicity of naive Bayes and can be easily learned without structure learning. Our experiments show that HNB outperforms naive Bayes, SBC, boosted naive Bayes, NBTree, and TAN significantly, and performs slightly better than AODE in ranking.

Accurate ranking is desired in many real-world data mining applications. Traditional learning algorithms, however, aim only at high classification accuracy. It has been observed that both traditional decision trees and naive Bayes produce good classification accuracy but poor probability estimates. In this paper, we use a new model, conditional independence tree (CITree), which is a combination of decision tree and naive Bayes and more suitable for ranking and more learnable in practice. We propose a novel algorithm for learning CITree for ranking, and the experiments show that the CITree algorithm outperforms the state-of-the-art decision tree learning algorithm C4.4 and naive Bayes significantly in yielding accurate rankings. Our work provides an effective data mining algorithm for applications in which an accurate ranking is required.