QPSO-CD: Quantum-behaved Particle Swarm Optimization Algorithm with Cauchy Distribution

Motivated by particle swarm optimization (PSO) and quantum computing theory, we have presented a quantum variant of PSO (QPSO) mutated with Cauchy operator and natural selection mechanism (QPSO-CD) from evolutionary computations. The performance of proposed hybrid quantum-behaved particle swarm optimization with Cauchy distribution (QPSO-CD) is investigated and compared with its counterparts based on a set of benchmark problems. Moreover, QPSO-CD is employed in well-studied constrained engineering problems to investigate its applicability. Further, the correctness and time complexity of QPSO-CD are analysed and compared with the classical PSO. It has been proven that QPSO-CD handles such real-life problems efficiently and can attain superior solutions in most of the problems. The experimental results showed that QPSO associated with Cauchy distribution and natural selection strategy outperforms other variants in the context of stability and convergence.

Real-Time Image Distortion Correction: Analysis and Evaluation of FPGA-Compatible Algorithms

Image distortion correction is a critical pre-processing step for a variety of computer vision and image processing algorithms. Standard real-time software implementations are generally not suited for direct hardware porting, so appropriated versions need to be designed in order to obtain implementations deployable on FPGAs. In this paper, hardware-compatible techniques for image distortion correction are introduced and analyzed in details. The considered solutions are compared in terms of output quality by using a geometrical-error-based approach, with particular emphasis on robustness with respect to increasing lens distortion. The required amount of hardware resources is also estimated for each considered approach.

Machine Learning based Pallets Detection and Tracking in AGVs

The use of automated guided vehicles (AGVs) has played a pivotal role in manufacturing and distribution operations, providing reliable and efficient product handling. In this project, we constructed a deep learning-based pallets detection and tracking architecture for pallets detection and position tracking. By using data preprocessing and augmentation techniques and experiment with hyperparameter tuning, we achieved the result with 25% reduction of error rate, 28.5% reduction of false negative rate, and 20% reduction of training time.

Towards Recognizing Unseen Categories in Unseen Domains

Current deep visual recognition systems suffer from severe performance degradation when they encounter new images from classes and scenarios unseen during training. Hence, the core challenge of Zero-Shot Learning (ZSL) is to cope with the semantic-shift whereas the main challenge of Domain Adaptation and Domain Generalization (DG) is the domain-shift. While historically ZSL and DG tasks are tackled in isolation, this work develops with the ambitious goal of solving them jointly,i.e. by recognizing unseen visual concepts in unseen domains. We presentCuMix (CurriculumMixup for recognizing unseen categories in unseen domains), a holistic algorithm to tackle ZSL, DG and ZSL+DG. The key idea of CuMix is to simulate the test-time domain and semantic shift using images and features from unseen domains and categories generated by mixing up the multiple source domains and categories available during training. Moreover, a curriculum-based mixing policy is devised to generate increasingly complex training samples. Results on standard SL and DG datasets and on ZSL+DG using the DomainNet benchmark demonstrate the effectiveness of our approach.

Dynamic Dispatching for Large-Scale Heterogeneous Fleet via Multi-agent Deep Reinforcement Learning

Dynamic dispatching is one of the core problems for operation optimization in traditional industries such as mining, as it is about how to smartly allocate the right resources to the right place at the right time. Conventionally, the industry relies on heuristics or even human intuitions which are often short-sighted and sub-optimal solutions. Leveraging the power of AI and Internet of Things (IoT), data-driven automation is reshaping this area. However, facing its own challenges such as large-scale and heterogenous trucks running in a highly dynamic environment, it can barely adopt methods developed in other domains (e.g., ride-sharing). In this paper, we propose a novel Deep Reinforcement Learning approach to solve the dynamic dispatching problem in mining. We first develop an event-based mining simulator with parameters calibrated in real mines. Then we propose an experience-sharing Deep Q Network with a novel abstract state/action representation to learn memories from heterogeneous agents altogether and realizes learning in a centralized way. We demonstrate that the proposed methods significantly outperform the most widely adopted approaches in the industry by $5.56\%$ in terms of productivity. The proposed approach has great potential in a broader range of industries (e.g., manufacturing, logistics) which have a large-scale of heterogenous equipment working in a highly dynamic environment, as a general framework for dynamic resource allocation.

Optimally adaptive Bayesian spectral density estimation

This paper studies spectral density estimates obtained assuming a \emph{Gaussian process} prior, with various stationary and non-stationary covariance structures, modelling the log of the unknown power spectrum. We unify previously disparate techniques from machine learning and statistics, applying various covariance functions to spectral density estimation, and investigate their performance and properties. We show that all covariance functions perform comparatively well, with the smoothing spline model in the existing AdaptSPEC technique performing slightly worse. Subsequently, we propose an improvement on AdaptSPEC based on an optimisation of the number of eigenvectors used. We show this improves on every existing method in the case of stationary time series, and describe an application to non-stationary time series. We introduce new measures of accuracy for the spectral density estimate, inspired from the physical sciences. Finally, we validate our models in an extensive simulation study and with real data, analysing autoregressive processes with known spectra, and sunspot and airline passenger data respectively.

Road Surface Friction Prediction Using Long Short-Term Memory Neural Network Based on Historical Data

Road surface friction significantly impacts traffic safety and mobility. A precise road surface friction prediction model can help to alleviate the influence of inclement road conditions on traffic safety, Level of Service, traffic mobility, fuel efficiency, and sustained economic productivity. Most related previous studies are laboratory-based methods that are difficult for practical implementation. Moreover, in other data-driven methods, the demonstrated time-series features of road surface conditions have not been considered. This study employed a Long-Short Term Memory (LSTM) neural network to develop a data-driven road surface friction prediction model based on historical data. The proposed prediction model outperformed the other baseline models in terms of the lowest value of predictive performance measurements. The influence of the number of time-lags and the predicting time interval on predictive accuracy was analyzed. In addition, the influence of adding road surface water thickness, road surface temperature and air temperature on predictive accuracy also were investigated. The findings of this study can support road maintenance strategy development and decision making, thus mitigating the impact of inclement road conditions on traffic mobility and safety. Future work includes a modified LSTM-based prediction model development by accommodating flexible time intervals between time-lags.

Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic

Maneuvering in dense traffic is a challenging task for autonomous vehicles because it requires reasoning about the stochastic behaviors of many other participants. In addition, the agent must achieve the maneuver within a limited time and distance. In this work, we propose a combination of reinforcement learning and game theory to learn merging behaviors. We design a training curriculum for a reinforcement learning agent using the concept of level-$k$ behavior. This approach exposes the agent to a broad variety of behaviors during training, which promotes learning policies that are robust to model discrepancies. We show that our approach learns more efficient policies than traditional training methods.

Word Embeddings: Stability and Semantic Change

Word embeddings are computed by a class of techniques within natural language processing (NLP), that create continuous vector representations of words in a language from a large text corpus. The stochastic nature of the training process of most embedding techniques can lead to surprisingly strong instability, i.e. subsequently applying the same technique to the same data twice, can produce entirely different results. In this work, we present an experimental study on the instability of the training process of three of the most influential embedding techniques of the last decade: word2vec, GloVe and fastText. Based on the experimental results, we propose a statistical model to describe the instability of embedding techniques and introduce a novel metric to measure the instability of the representation of an individual word. Finally, we propose a method to minimize the instability - by computing a modified average over multiple runs - and apply it to a specific linguistic problem: The detection and quantification of semantic change, i.e. measuring changes in the meaning and usage of words over time.

A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News

In recent years, machine learning and deep learning have become popular methods for financial data analysis, including financial textual data, numerical data, and graphical data. This paper proposes to use sentiment analysis to extract useful information from multiple textual data sources and a blending ensemble deep learning model to predict future stock movement. The blending ensemble model contains two levels. The first level contains two Recurrent Neural Networks (RNNs), one Long-Short Term Memory network (LSTM) and one Gated Recurrent Units network (GRU), followed by a fully connected neural network as the second level model. The RNNs, LSTM, and GRU models can effectively capture the time-series events in the input data, and the fully connected neural network is used to ensemble several individual prediction results to further improve the prediction accuracy. The purpose of this work is to explain our design philosophy and show that ensemble deep learning technologies can truly predict future stock price trends more effectively and can better assist investors in making the right investment decision than other traditional methods.