With broad applications in various public services like aviation management and urban disaster warning, numerical precipitation prediction plays a crucial role in weather forecast. However, constrained by the limitation of observation and conventional meteorological models, the numerical precipitation predictions are often highly biased. To correct this bias, classical correction methods heavily depend on profound experts who have knowledge in aerodynamics, thermodynamics and meteorology. As precipitation can be influenced by countless factors, however, the performances of these expert-driven methods can drop drastically when some un-modeled factors change. To address this issue, this paper presents a data-driven deep learning model which mainly includes two blocks, i.e. a Denoising Autoencoder Block and an Ordinal Regression Block. To the best of our knowledge, it is the first expert-free models for bias correction. The proposed model can effectively correct the numerical precipitation prediction based on 37 basic meteorological data from European Centre for Medium-Range Weather Forecasts (ECMWF). Experiments indicate that compared with several classical machine learning algorithms and deep learning models, our method achieves the best correcting performance and meteorological index, namely the threat scores (TS), obtaining satisfactory visualization effect.

Outlier detection is a technique in data mining that aims to detect unusual or unexpected records in the dataset. Existing outlier detection algorithms have different pros and cons and exhibit different sensitivity to noisy data such as extreme values. In this paper, we propose a novel cluster-based outlier detection algorithm named MSD-Kmeans that combines the statistical method of Mean and Standard Deviation (MSD) and the machine learning clustering algorithm K-means to detect outliers more accurately with the better control of extreme values. There are two phases in this combination method of MSD-Kmeans: (1) applying MSD algorithm to eliminate as many noisy data to minimize the interference on clusters, and (2) applying K-means algorithm to obtain local optimal clusters. We evaluate our algorithm and demonstrate its effectiveness in the context of detecting possible overcharging of taxi fares, as greedy dishonest drivers may attempt to charge high fares by detouring. We compare the performance indicators of MSD-Kmeans with those of other outlier detection algorithms, such as MSD, K-means, Z-score, MIQR and LOF, and prove that the proposed MSD-Kmeans algorithm achieves the highest measure of precision, accuracy, and F-measure. We conclude that MSD-Kmeans can be used for effective and efficient outlier detection on data of varying quality on IoT devices.

Driver drowsiness increases crash risk, leading to substantial road trauma each year. Drowsiness detection methods have received considerable attention, but few studies have investigated the implementation of a detection approach on a mobile phone. Phone applications reduce the need for specialised hardware and hence, enable a cost-effective roll-out of the technology across the driving population. While it has been shown that three-dimensional (3D) operations are more suitable for spatiotemporal feature learning, current methods for drowsiness detection commonly use frame-based, multi-step approaches. However, computationally expensive techniques that achieve superior results on action recognition benchmarks (e.g. 3D convolutions, optical flow extraction) create bottlenecks for real-time, safety-critical applications on mobile devices. Here, we show how depthwise separable 3D convolutions, combined with an early fusion of spatial and temporal information, can achieve a balance between high prediction accuracy and real-time inference requirements. In particular, increased accuracy is achieved when assessment requires motion information, for example, when sunglasses conceal the eyes. Further, a custom TensorFlow-based smartphone application shows the true impact of various approaches on inference times and demonstrates the effectiveness of real-time monitoring based on out-of-sample data to alert a drowsy driver. Our model is pre-trained on ImageNet and Kinetics and fine-tuned on a publicly available Driver Drowsiness Detection dataset. Fine-tuning on large naturalistic driving datasets could further improve accuracy to obtain robust in-vehicle performance. Overall, our research is a step towards practical deep learning applications, potentially preventing micro-sleeps and reducing road trauma.

The main goal of this work is equipping convex and nonconvex problems with Barzilai-Borwein (BB) step size. With the adaptivity of BB step sizes granted, they can fail when the objective function is not strongly convex. To overcome this challenge, the key idea here is to bridge (non)convex problems and strongly convex ones via regularization. The proposed regularization schemes are \textit{simple} yet effective. Wedding the BB step size with a variance reduction method, known as SARAH, offers a free lunch compared with vanilla SARAH in convex problems. The convergence of BB step sizes in nonconvex problems is also established and its complexity is no worse than other adaptive step sizes such as AdaGrad. As a byproduct, our regularized SARAH methods for convex functions ensure that the complexity to find $\mathbb{E}[\| \nabla f(\mathbf{x}) \|^2]\leq \epsilon$ is ${\cal O}\big( (n+\frac{1}{\sqrt{\epsilon}})\ln{\frac{1}{\epsilon}}\big)$, improving $\epsilon$ dependence over existing results. Numerical tests further validate the merits of proposed approaches.

Addressing the non-uniform missing mechanism of rating feedback is critical to build a well-performing recommeder in the real-world systems. To tackle the challenging issue, we first define an ideal loss function that should be optimized to achieve the goal of recommendation. Then, we derive the generalization error bound of the ideal loss that alleviates the variance and the misspecification problems of the previous propensity-based methods. We further propose a meta-learning method minimizing the bound. Empirical evaluation using real-world datasets validates the theoretical findings and demonstrates the practical advantages of the proposed upper bound minimization approach.

Parallel exploration is a key to a successful search. The recently proposed Negatively Correlated Search (NCS) achieved this ability by constructing a set of negatively correlated search processes and has been applied to many real-world problems. In NCS, the key technique is to explicitly model and maximize the diversity among search processes in parallel. However, the original diversity model was mostly devised by intuition, which introduced several drawbacks to NCS. In this paper, a mathematically principled diversity model is proposed to solve the existing drawbacks of NCS, resulting a new NCS framework. A new instantiation of NCS is also derived and its effectiveness is verified on a set of multi-modal continuous optimization problems.

Palo Alto, CA, 94306, USA ABSTRACT We propose an architecture to jointly learn word and label embeddings for slot filling in spoken language understanding. The proposed approach encodes labels using a combination of word embeddings and straightforward word-label association from the training data. Compared to the state-of- the-art methods, our approach does not require label embed-dings as part of the input and therefore lends itself nicely to a wide range of model architectures. In addition, our architecture computes contextual distances between words and labels to avoid adding contextual windows, thus reducing memory footprint. We validate the approach on established spoken dialogue datasets and show that it can achieve state-of-the-art performance with much fewer trainable parameters. Index T erms-- Slot-filling, recurrent neural network, distributional semantics, sequence labelling 1. INTRODUCTION In spoken language understanding (SLU), an essential step is to associate each word in an utterance with one semantic class label. These annotated utterances can then serve as a basis for higher level SLU tasks, such as topic identification and dialogue response generation. This process of semantic label tagging in SLU, dubbed slot filling, labels utterance sequences with tags under a specific scheme. As an example, the BIO scheme prefixes tags with one of the characters { B, I, O } to indicate the continuity of a tag: Begin, Inside, or Outside, e.g., B-price indicates this position is the beginning of the tag price. Researchers also developed deep learning architecture for slot filling, e.g., [1, 2, 3].

All around the world, from small-town Illinois in the US to Rochdale in England, from Perth, Australia, to Dumka in northern India, a revolution is under way in how governments treat the poor. You can't see it happening, and may have heard nothing about it. It's being planned by engineers and coders behind closed doors, in secure government locations far from public view. Only mathematicians and computer scientists fully understand the sea change, powered as it is by artificial intelligence (AI), predictive algorithms, risk modeling and biometrics. But if you are one of the millions of vulnerable people at the receiving end of the radical reshaping of welfare benefits, you know it is real and that its consequences can be serious – even deadly.

The artificial intelligence in manufacturing market is poised to hike from USD 1 billion in 2018 to over USD 16 billion by 2025, according to a 2019 Global Market Insights, Inc. report. The AI in manufacturing market is driven by the rapid adoption of industry 4.0 technologies. The growing need among the manufacturers to reduce the cost of operation and enhance operational efficiency is the primary factor driving the adoption of Industry 4.0. The new technology solutions are enhancing operational efficiency and reducing the time to market the products. It allows enterprises to analyze the customer demand, align their operations to meet the customer's requirement, and analyze the process in real-time.

All around the world, from small-town Illinois in the US to Rochdale in England, from the Pacific shore of Perth, Australia, to Dumka in northern India, a revolution is under way in how governments treat the poor. You can't see it happening, and may have heard nothing about it. It's being planned by engineers and coders behind closed doors, in secure government locations far from public view. Only mathematicians and computer scientists fully understand the sea change, powered as it is by artificial intelligence (AI), predictive algorithms, risk modeling and biometrics. But if you are one of the millions of vulnerable people at the receiving end of the radical reshaping of welfare benefits, you know it is real and that its consequences can be serious – even deadly.