Reviewerly/exHarmony huggingface dataset:
Learn from All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition

Noisy label Facial Expression Recognition (FER) is more challenging than traditional noisy label classification tasks due to the inter-class similarity and the annotation ambiguity. Recent works mainly tackle this problem by filtering out large-loss samples. In this paper, we explore dealing with noisy labels from a new feature-learning perspective. We find that FER models remember noisy samples by focusing on a part of the features that can be considered related to the noisy labels instead of learning from the whole features that lead to the latent truth. Inspired by that, we propose a novel Erasing Attention Consistency (EAC) method to suppress the noisy samples during the training process automatically. Specifically, we first utilize the flip semantic consistency of facial images to design an imbalanced framework. We then randomly erase input images and use flip attention consistency to prevent the model from focusing on a part of the features. EAC significantly outperforms state-of-the-art noisy label FER methods and generalizes well to other tasks with a large number of classes like CIFAR100 and Tiny-ImageNet. The code is available at https://github.com/zyh-uaiaaaa/Erasing-Attention-Consistency.
Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview

Health informatics is a vital technology that holds great promise in the healthcare setting. We describe two prominent health informatics tools relevant to emergency care, as well as the historical background and the current state of informatics. We also identify recent research findings and practice changes. The recent advances in machine learning and natural language processing (NLP) are a prominent development in health informatics overall and relevant in emergency medicine (EM). A basic comprehension of machine-learning algorithms is the key to understand the recent usage of artificial intelligence in healthcare. We are using NLP more in clinical use for documentation. NLP has started to be used in research to identify clinically important diseases and conditions. Health informatics has the potential to benefit both healthcare providers and patients. We cover two powerful tools from health informatics for EM clinicians and researchers by describing the previous successes and challenges and conclude with their implications to emergency care.
Performance Evaluation of Techniques for Identifying Abnormal Energy Consumption in Buildings

Energy consumption in buildings has steadily increased.Buildings consume more energy than necessary due to suboptimal design and operation.Apart from retro-fitting, not much can be done with the design of the existing building, but the operation of the building can be improved.Ignoring or failing to fix the faults can lead to problems like the higher cost in excess energy usage or premature component failure.At the same time understanding, identifying, and addressing abnormal energy consumption in buildings can lead to energy savings and detection of faulty appliances.This paper investigates two key challenges found in energy anomaly detection research: 1) the lack of labeled ground truth and 2) the lack of consistent performance accuracy metrics.In the first challenge, labeled ground truth is imperative for training and benchmarking algorithms to detect anomalies.In the second challenge, consistent performance accuracy metrics are crucial to quantifying how well algorithms perform against each other.There exists no publicly available energy consumption dataset with labeled anomaly events.Therefore, we propose two approaches that help in the automatic annotation of the ground truth data from publicly available datasets: a statistical approach for short-term data and a piecewise linear regression method for long-term data.We demonstrate these approaches using two publicly available datasets called Dataport (Pecan Street) and HUE.Using different existing accuracy metrics, we run a series of experiments on anomaly detection algorithms and discuss what metrics can be best used for consistent accuracy testing amongst researchers.In addition, while providing the source code, we also release an anomaly annotated dataset produced by this source code.
Event Detection without Triggers

Shulin Liu, Yang Li, Feng Zhang, Tao Yang, Xinpeng Zhou. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers

The vast majority of processors in the world are actually microcontroller units (MCUs), which find widespread use performing simple control tasks in applications ranging from automobiles to medical devices and office equipment. The Internet of Things (IoT) promises to inject machine learning into many of these every-day objects via tiny, cheap MCUs. However, these resource-impoverished hardware platforms severely limit the complexity of machine learning models that can be deployed. For example, although convolutional neural networks (CNNs) achieve state-of-the-art results on many visual recognition tasks, CNN inference on MCUs is challenging due to severe memory limitations. To circumvent the memory challenge associated with CNNs, various alternatives have been proposed that do fit within the memory budget of an MCU, albeit at the cost of prediction accuracy. This paper challenges the idea that CNNs are not suitable for deployment on MCUs. We demonstrate that it is possible to automatically design CNNs which generalize well, while also being small enough to fit onto memory-limited MCUs. Our Sparse Architecture Search method combines neural architecture search with pruning in a single, unified approach, which learns superior models on four popular IoT datasets. The CNNs we find are more accurate and up to 7.4× smaller than previous approaches, while meeting the strict MCU working memory constraint.
Quadratic Video Interpolation

Video interpolation is an important problem in computer vision, which helps overcome the temporal limitation of camera sensors. Existing video interpolation methods usually assume uniform motion between consecutive frames and use linear models for interpolation, which cannot well approximate the complex motion in the real world. To address these issues, we propose a quadratic video interpolation method which exploits the acceleration information in videos. This method allows prediction with curvilinear trajectory and variable velocity, and generates more accurate interpolation results. For high-quality frame synthesis, we develop a flow reversal layer to estimate flow fields starting from the unknown target frame to the source frame. In addition, we present techniques for flow refinement. Extensive experiments demonstrate that our approach performs favorably against the existing linear models on a wide variety of video datasets.
The Oklahoma Geological Survey Statewide Seismic Network

Abstract The Oklahoma Geological Survey (OGS) monitors seismicity throughout the state of Oklahoma utilizing permanent and temporary seismometers installed by OGS and other agencies, while producing a real-time earthquake catalog. The OGS seismic network was recently added to the Advanced National Seismic System (ANSS) as a self-supporting regional seismic network, and earthquake locations and magnitudes are automatically reported through U.S. Geological Survey and are part of the ANSS Comprehensive Earthquake Catalog. In Oklahoma, before 2009, background seismicity rates were about 2 M 3.0+ earthquakes per year, which increased to 579 and 903 M 3.0+ earthquakes in 2014 and 2015, respectively. After seismicity peaked, the rate fell to 624, 304, and 194 M 3.0+ earthquakes in 2016, 2017, and 2018, respectively. The catalog is complete down to M 2.2 from mid-2014 to present, despite the significant workload for a primarily state-funded regional network. That astonishing uptick in seismicity has been largely attributed to wastewater injection practices. The OGS provides the Oklahoma Corporation Commission, the agency responsible for regulating oil and gas activities within the state, with technical guidance and earthquake products that inform their “traffic-light” mitigation protocol and other mitigating actions. We have initiated a citizen-scientist-driven, educational seismometer program by installing Raspberry Shake geophones throughout the state at local schools, museums, libraries, and state parks. The seismic hazard of the state portends a continued need for expansion and densification of seismic monitoring throughout Oklahoma.
Face Recognition and Classification Using GoogleNET Architecture

Anand, R. Shanthi, T. Nithish, M. S. Lakshman, S.Face recognition is the most important tool in computer vision and an inevitable technology finding applications in robotics, security, and mobile devices. Though it is a technology of the past, state-of-the-art machine learning (ML) techniques have made this technology game-changing and even surpass human counterparts in terms of accuracy. This paper focuses on applying one of the advanced machine learning tools in face recognition to achieve higher accuracy. We created our own dataset and trained it on the GoogleNet (inception) deep learning model using the Caffe and Nvidia DIGITS framework. We achieved an overall accuracy of 91.43% which was fairly high enough to recognize the faces better than the conventional ML techniques. The scope of the application of deep learning is enormous and by training a huge volume of data with massive computational power, accuracy greater than 99% can be achieved. This paper will give a glimpse of deep learning, from creation of dataset to training and deploying the models, and the method can be applied for dataset corresponding to any field, be it medicine, agriculture or manufacturing, reducing the human effort and thus triggering the revolution of automation.
Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation

Non-autoregressive translation (NAT) models remove the dependence on previous target tokens and generate all target tokens in parallel, resulting in significant inference speedup but at the cost of inferior translation accuracy compared to autoregressive translation (AT) models. Considering that AT models have higher accuracy and are easier to train than NAT models, and both of them share the same model configurations, a natural idea to improve the accuracy of NAT models is to transfer a well-trained AT model to an NAT model through fine-tuning. However, since AT and NAT models differ greatly in training strategy, straightforward fine-tuning does not work well. In this work, we introduce curriculum learning into fine-tuning for NAT. Specifically, we design a curriculum in the fine-tuning process to progressively switch the training from autoregressive generation to non-autoregressive generation. Experiments on four benchmark translation datasets show that the proposed method achieves good improvement (more than 1 BLEU score) over previous NAT baselines in terms of translation accuracy, and greatly speed up (more than 10 times) the inference process over AT baselines.
A color image encryption method based on memristive hyperchaotic system and DNA encryption

In this paper, a color image encryption method using the memristive hyperchaotic system and deoxyribonucleic acid (DNA) encryption is proposed. First, the pseudo-random sequences are generated by a keystream generation mechanism based on a memristive hyperchaotic system and the plain image. Due to this, the memristive hyperchaotic system has a complex dynamical behavior and is highly sensitive to initial conditions, the proposed keystream generation mechanism is highly random which is also dependent on the plain images. Second, a permutation based on the cycle-shift operation is designed to eliminate the correlations between adjacent pixels in the plain images. Then, the scrambled sequences are processed by DNA encryption to increase the system ability to defense the brute force attacks. Finally, the cipher image is obtained after the diffusion and interaction among red, green and blue components. Experimental analysis and performance comparisons show that the proposed method has high security, good efficiency and strong robustness under different attacks.
The Experience-Memory Q-Learning Algorithm for Robot Path Planning in Unknown Environment

In order to solve the problem of slow convergence speed and long planned path when the robot plans a path in unknown environment by using Q-learning algorithm, we propose the Experience-Memory Q-Learning (EMQL) algorithm based on the continuous update of the shortest distance from the current state node to the start point. The autonomous learning ability of the robot is enhanced by the different role assignments of two tables in the proposed algorithm. EM table with $(m*1)$ dimension is designed to record the distance information, reflecting the learning process of the robot. Q table is adopted as an auxiliary guidance for the experience transfer strategy and experience reuse strategy, and these strategies enable the robot accomplish the task even if the destination is changed or the path is blocked. Further, the learning efficiency of the robot in the EMQL algorithm is improved by the dual reward mechanism consisting of static reward and dynamic reward. The static reward is designed to prevent the robot from exploring a state node excessively. The dynamic reward is responsible for helping the robot avoid searching blindly in unknown environment. We test the effectiveness of the proposed algorithm on both grid maps and road network maps. The comparison results in planning time, iteration times and path length show that the performance of the EMQL algorithm is superior to Q-learning algorithm in convergence speed and optimization ability. Additionally, the practicability of the proposed algorithm is validated in a real-world experiment using the Turtlebot3 burger robot.
Adjusting Decision Boundary for Class Imbalanced Learning

The training of deep neural networks heavily depends on the data distribution. In particular, these networks easily suffer from class imbalance. The trained networks would recognize the frequent classes better than the infrequent classes. To resolve this problem, existing approaches typically propose novel loss functions to obtain better feature embedding. In this paper, we argue that drawing a better decision boundary is as important as learning better features. Based on our observations, we investigate how the class imbalance affects the decision boundary and deteriorates the performance. We also investigate the feature-distributional discrepancy between training and test time. Accordingly, we propose a novel, yet simple method for class imbalanced learning. Despite its simplicity, our method exhibits outstanding performance. Specifically, the experimental results show that we can significantly improve a network by scaling the weight vectors, even without additional training processes.
The impact of long-term changes in air temperature on renewable energy in Poland

This paper analysed from the statistical point of view the trends in observed air temperature in major Polish cities and presented a qualitative analysis of their potential impact on the operation of the selected renewable energy sources. It also reviews the relation between the air temperature and observed electrical load as well as changing numbers of cooling and heating degree days. The method involved a statistical analysis of historical mean daily temperature observed in 19 major Polish cities over the 1968–2018 period. The air temperature change impact on renewable energy sector in Poland, by affecting the heating and cooling demand, the electrical load and the renewables working conditions both, on supply and demand side. The analysis reports that the mean daily temperature in all major polish cities is exhibiting a statistically significant increasing trend, up to 0.52 °C/decade. The observed increase in air temperature reduces the heating demand in Poland, beneficially for the environment and renewable supply. Increasing cooling needs in summer raises the energy consumption and indoor thermal stress. The climate warming affects the operation conditions, energy source, driving force, capacity and efficiency of renewable energy sources. The investigated changes were favourable and unfavourable depending on the renewable technology and operation mode, and were stronger on the demand side than on the supply side.
Adaptive Algorithms for Signature Wavelet recognition in the Musical Sounds

The scaling and as well as the wavelet-functions of the wavelet is detected engaging the wavelet-filters that are empowered with the filter-bank principle that is utilized in recognizing the rough calculation and the feature co-efficient of the wavelet-filter. The coefficients recognized by the filter-bank for the musical sounds produced by the musical-instruments enables one to have a signature-wavelet of the sound signal. The signature-wavelet renovates the actual musical signal with insignificant disturbance. In order to recognize the factors (coefficients) the paper employs the least mean square (L-MS), normalized least means square (NL-MS), recursive least square (R-LS) and the QR-Recursive least square (QR-RLS). Among the above four the R-LS and the QR-RLS performs well under all grounds. More over the algorithm converges swiftly compared to the other algorithm. Thus providing an accuracy and SOC (speed of convergence) improved scaling and wavelet-function recognition.
Span Selection Pre-training for Question Answering

BERT (Bidirectional Encoder Representations from Transformers) and related pre-trained Transformers have provided large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA).BERT is pretrained on two auxiliary tasks: Masked Language Model and Next Sentence Prediction.In this paper we introduce a new pre-training task inspired by reading comprehension to better align the pre-training from memorization to understanding.Span Selection Pre-Training (SSPT) poses cloze-like training instances, but rather than draw the answer from the model's parameters, it is selected from a relevant passage.We find significant and consistent improvements over both BERT BASE and BERT LARGE on multiple Machine Reading Comprehension (MRC) datasets.Specifically, our proposed model has strong empirical evidence as it obtains SOTA results on Natural Questions, a new benchmark MRC dataset, outperforming BERT LARGE by 3 F1 points on short answer prediction.We also show significant impact in HotpotQA, improving answer prediction F1 by 4 points and supporting fact prediction F1 by 1 point and outperforming the previous best system.Moreover, we show that our pre-training approach is particularly effective when training data is limited, improving the learning curve by a large amount.
Chip-Based Measurement-Device-Independent Quantum Key Distribution Using Integrated Silicon Photonic Systems

Photonic chip integration would greatly benefit quantum key distribution (QKD) systems for secure communication, in terms of compactness, scalability, and well-established fabrication techniques. This study demonstrates proof of concept for an all-chip measurement-device-independent QKD system using integrated silicon photonic technology. The prototype generates a key rate per pulse of $2.923\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}6}$ over a distance corresponding to 50 km of standard fiber with 25% detection efficiency; the system is projected to be able to work at 120 km with 85% detection efficiency. This technology shows great promise for highly integrated quantum communication networks.
Multiple Kernel <i>k</i>-Means Clustering by Selecting Representative Kernels

To cluster data that are not linearly separable in the original feature space, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering was extended to the kernel version. However, the performance of kernel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering largely depends on the choice of the kernel function. To mitigate this problem, multiple kernel learning has been introduced into the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering to obtain an optimal kernel combination for clustering. Despite the success of multiple kernel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering in various scenarios, few of the existing work update the combination coefficients based on the diversity of kernels, which leads to the result that the selected kernels contain high redundancy and would degrade the clustering performance and efficiency. We resolve this problem from the perspective of subset selection in this article. In particular, we first propose an effective strategy to select a diverse subset from the prespecified kernels as the representative kernels, and then incorporate the subset selection process into the framework of multiple <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering. The representative kernels can be indicated as a significant combination weights. Due to the nonconvexity of the obtained objective function, we develop an alternating minimization method to optimize the combination coefficients of the selected kernels and the cluster membership alternatively. In particular, an efficient optimization method is developed to reduce the time complexity of optimizing the kernel combination weights. Finally, extensive experiments on benchmark and real-world data sets demonstrate the effectiveness and superiority of our approach in comparison with existing methods.
DLBench: a comprehensive experimental evaluation of deep learning frameworks

Abstract Deep Learning (DL) has achieved remarkable progress over the last decade on various tasks such as image recognition, speech recognition, and natural language processing. In general, three main crucial aspects fueled this progress: the increasing availability of large amount of digitized data, the increasing availability of affordable parallel and powerful computing resources (e.g., GPU) and the growing number of open source deep learning frameworks that facilitate and ease the development process of deep learning architectures. In practice, the increasing popularity of deep learning frameworks calls for benchmarking studies that can effectively evaluate and understand the performance characteristics of these systems. In this paper, we conduct an extensive experimental evaluation and analysis of six popular deep learning frameworks, namely, TensorFlow , MXNet , PyTorch , Theano , Chainer , and Keras , using three types of DL architectures Convolutional Neural Networks (CNN), Faster Region-based Convolutional Neural Networks (Faster R-CNN), and Long Short Term Memory (LSTM). Our experimental evaluation considers different aspects for its comparison including accuracy, training time, convergence and resource consumption patterns. Our experiments have been conducted on both CPU and GPU environments using different datasets. We report and analyze the performance characteristics of the studied frameworks. In addition, we report a set of insights and important lessons that we have learned from conducting our experiments.