Reviewerly/exHarmony huggingface dataset:
QwertyRing

The software keyboard is widely used on digital devices such as smartphones, computers, and tablets. The software keyboard operates via touch, which is efficient, convenient, and familiar to users. However, some emerging technology devices such as AR/VR headsets and smart TVs do not support touch-based text entry. In this paper, we present QwertyRing, a technique that supports text entry on physical surfaces using an IMU (Inertial Measurement Unit) ring. Users wear the ring on the middle phalanx of the index finger and type on any desk-like surface, as if there is a QWERTY keyboard on the surface. While typing, users do not focus on monitoring the hand motions. They receive text feedback on a separate screen, e.g., an AR/VR headset or a digital device display, such as a computer monitor. The basic idea of QwertyRing is to detect touch events and predict users' desired words by the orientation of the IMU ring. We evaluate the performance of QwertyRing through a five-day user study. Participants achieved a speed of 13.74 WPM in the first 40 minutes and reached 20.59 WPM at the end. The speed outperforms other ring-based techniques [24, 30, 45, 68] and is 86.48% of the speed of typing on a smartphone with an index finger. The results show that QwertyRing enables efficient touch-based text entry on physical surfaces.
Mathematical Models of Overparameterized Neural Networks

Deep learning has received considerable empirical success in recent years. However, while many ad hoc tricks have been discovered by practitioners, until recently, there has been a lack of theoretical understanding for tricks invented in the deep learning literature. Known by practitioners that overparameterized neural networks (NNs) are easy to learn, in the past few years, there have been important theoretical developments in the analysis of overparameterized NNs. In particular, it was shown that such systems behave like convex systems under various restricted settings, such as for two-layer NNs, and when learning is restricted locally in the so-called neural tangent kernel space around specialized initializations. This article discusses some of these recent signs of progress leading to a significantly better understanding of NNs. We will focus on the analysis of two-layer NNs and explain the key mathematical models, with their algorithmic implications. We will then discuss challenges in understanding deep NNs and some current research directions.
IIRC: Incremental Implicitly-Refined Classification

We introduce the "Incremental Implicitly-Refined Classification (IIRC)" setup, an extension to the class incremental learning setup where the incoming batches of classes have two granularity levels. i.e., each sample could have a high-level (coarse) label like "bear" and a low-level (fine) label like "polar bear". Only one label is provided at a time, and the model has to figure out the other label if it has already learned it. This setup is more aligned with real-life scenarios, where a learner usually interacts with the same family of entities multiple times, discovers more granularity about them, while still trying not to forget previous knowledge. Moreover, this setup enables evaluating models for some important lifelong learning challenges that cannot be easily addressed under the existing setups. These challenges can be motivated by the example "if a model was trained on the class bear in one task and on polar bear in another task, will it forget the concept of bear, will it rightfully infer that a polar bear is still a bear? and will it wrongfully associate the label of polar bear to other breeds of bear?". We develop a standardized benchmark that enables evaluating models on the IIRC setup. We evaluate several state-of-the-art lifelong learning algorithms and highlight their strengths and limitations. For example, distillation-based methods perform relatively well but are prone to incorrectly predicting too many labels per image. We hope that the proposed setup, along with the benchmark, would provide a meaningful problem setting to the practitioners.
Annotation-Efficient Learning for Medical Image Segmentation Based on Noisy Pseudo Labels and Adversarial Learning

Despite that deep learning has achieved state-of-the-art performance for medical image segmentation, its success relies on a large set of manually annotated images for training that are expensive to acquire. In this paper, we propose an annotation-efficient learning framework for segmentation tasks that avoids annotations of training images, where we use an improved Cycle-Consistent Generative Adversarial Network (GAN) to learn from a set of unpaired medical images and auxiliary masks obtained either from a shape model or public datasets. We first use the GAN to generate pseudo labels for our training images under the implicit high-level shape constraint represented by a Variational Auto-encoder (VAE)-based discriminator with the help of the auxiliary masks, and build a Discriminator-guided Generator Channel Calibration (DGCC) module which employs our discriminator's feedback to calibrate the generator for better pseudo labels. To learn from the pseudo labels that are noisy, we further introduce a noise-robust iterative learning method using noise-weighted Dice loss. We validated our framework with two situations: objects with a simple shape model like optic disc in fundus images and fetal head in ultrasound images, and complex structures like lung in X-Ray images and liver in CT images. Experimental results demonstrated that 1) Our VAE-based discriminator and DGCC module help to obtain high-quality pseudo labels. 2) Our proposed noise-robust learning method can effectively overcome the effect of noisy pseudo labels. 3) The segmentation performance of our method without using annotations of training images is close or even comparable to that of learning from human annotations.
Static Neural Compiler Optimization via Deep Reinforcement Learning

The phase-ordering problem of modern compilers has received a lot of attention from the research community over the years, yet remains largely unsolved. Various optimization sequences exposed to the user are manually designed by compiler developers. In designing such a sequence developers have to choose the set of optimization passes, their parameters and ordering within a sequence. Resulting sequences usually fall short of achieving optimal runtime for a given source code and may sometimes even degrade the performance when compared to unoptimized version. In this paper, we employ a deep reinforcement learning approach to the phase-ordering problem. Provided with sub-sequences constituting LLVM's O3 sequence, our agent learns to outperform the O3 sequence on the set of source codes used for training and achieves competitive performance on the validation set, gaining up to 1.32x speedup on previously-unseen programs. Notably, our approach differs from autotuning methods by not depending on one or more test runs of the program for making successful optimization decisions. It has no dependence on any dynamic feature, but only on the statically-attainable intermediate representation of the source code. We believe that the models trained using our approach can be integrated into modern compilers as neural optimization agents, at first to complement, and eventually replace the handcrafted optimization sequences.
Quantum Channel State Masking

Communication over a quantum channel that depends on a quantum state is considered when the encoder has channel side information (CSI) and is required to mask information on the quantum channel state from the decoder. A full characterization is established for the entanglement-assisted masking equivocation region with a maximally correlated channel state, and a regularized formula is given for the quantum capacity-leakage function without assistance. For Hadamard channels without assistance, we derive single-letter inner and outer bounds, which coincide in the standard case of a channel that does not depend on a state.
Concept drift-aware temporal cloud service APIs recommendation for building composite cloud systems

The booming advances of cloud computing promote rapid growth of the number of cloud service Application Program Interfaces (APIs) published at the large-scale software cloud markets. Cloud service APIs recommendation remains a challenging issue for a composite cloud system construction, due to massively available candidate component cloud services with similar (or identical) functionalities in the cloud markets. As for a specific user, the probability distribution of the data indicating his/her preferences to the cloud service APIs may change with time, resulting in concept drifting preferences. To adapt users’ preference drifts and provide effective recommendation results to composite cloud system developers, we propose a concept drift-aware temporal cloud service APIs recommendation approach for composite cloud systems (or CD-APIR) in this paper. First, we track users temporal preferences through users’ behavior-aware information analysis. Second, we utilize Singular Value Decomposition (SVD) method to predict the missing values in the user–service matrices. Third, we identify the degree of users preference drifts by Jensen–Shannon (or JS) divergence. Finally, we recommend cloud service APIs by presenting a piecewise trading-off equation. Experimental evaluations conducted on WS-Dream dataset demonstrate that the CD-APIR approach can effectively improve the accuracy of cloud service APIs recommendation comparing with 7 representative approaches.
Image Steganography in Spatial Domain: Current Status, Techniques, and Trends

This research article provides an up-to-date review of spatial-domain steganography. Maintaining the communication as secure as possible when transmitting secret data through any available communication channels is the target... | Find, read and cite all the research you need on Tech Science Press
Using Generative Adversarial Networks and Parameter Optimization of Convolutional Neural Networks for Lung Tumor Classification

Cancer is the leading cause of death worldwide. Lung cancer, especially, caused the most death in 2018 according to the World Health Organization. Early diagnosis and treatment can considerably reduce mortality. To provide an efficient diagnosis, deep learning is overtaking conventional machine learning techniques and is increasingly being used in computer-aided design systems. However, a sparse medical data set and network parameter tuning process cause network training difficulty and cost longer experimental time. In the present study, the generative adversarial network was proposed to generate computed tomography images of lung tumors for alleviating the problem of sparse data. Furthermore, a parameter optimization method was proposed not only to improve the accuracy of lung tumor classification, but also reduce the experimental time. The experimental results revealed that the average accuracy can reach 99.86% after image augmentation and parameter optimization.
RETRACTED: Internet financial risk assessment based on web embedded system and data mining algorithm

The Financial requirements of the investigation and the specific design techniques for Web applications in the financial services industry Web Embedded Systems (WES) of. In discussing some of the financial services industry has decision-making technology, using data mining motives are related to the basic challenge of some typical applications using embedded design, after the realization of prompt analysis and user behavior online risks Accounting Oversight Internet control, early warning accounting software module risk In the monitoring system and embedded, the construction of interest rate risk, currency risk, accounting for risk prediction of the network, warning the Multiple of supervision of discriminant analysis model, accounting for boot loader in accordance with the classification Improve internet controls and requirements, real-time early tracking alert payment network and information on the efficiency of various fees and interest, corporate accounting fees business operations and regular financial reporting system. Internet financial analysis and risk measurement receivers. As a result, to achieve real-time risk-aware network can use the Internet shows that the supervision of accounting, risk management capabilities and improve prevention.
EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels

The efficacy of deep learning depends on large-scale data sets that have been carefully curated with reliable data acquisition and annotation processes. However, acquiring such large-scale data sets with precise annotations is very expensive and time-consuming, and the cheap alternatives often yield data sets that have noisy labels. The field has addressed this problem by focusing on training models under two types of label noise: 1) closed-set noise, where some training samples are incorrectly annotated to a training label other than their known true class; and 2) open-set noise, where the training set includes samples that possess a true class that is (strictly) not contained in the set of known training labels. In this work, we study a new variant of the noisy label problem that combines the open-set and closed-set noisy labels, and introduce a benchmark evaluation to assess the performance of training algorithms under this setup. We argue that such problem is more general and better reflects the noisy label scenarios in practice. Furthermore, we propose a novel algorithm, called EvidentialMix, that addresses this problem and compare its performance with the state-of-the-art methods for both closed-set and open-set noise on the proposed benchmark. Our results show that our method produces superior classification results and better feature representations than previous state-of-the-art methods. The code is available at https:/github.com/ragavsachdeva/EvidentialMix.
SWAG: Superpixels Weighted by Average Gradients for Explanations of CNNs

Providing an explanation of the operation of CNNs that is both accurate and interpretable is becoming essential in fields like medical image analysis, surveillance, and autonomous driving. In these areas, it is important to have confidence that the CNN is working as expected and explanations from saliency maps provide an efficient way of doing this. In this paper, we propose a pair of complementary contributions that improve upon the state of the art for region-based explanations in both accuracy and utility. The first is SWAG, a method for generating accurate explanations quickly using superpixels for discriminative regions which is meant to be a more accurate, efficient, and tunable drop in replacement method for Grad-CAM, LIME, or other region-based methods. The second contribution is based on an investigation into how to best generate the superpixels used to represent the features found within the image. Using SWAG, we compare using superpixels created from the image, a combination of the image and backpropagated gradients, and the gradients themselves. To the best of our knowledge, this is the first method proposed to generate explanations using superpixels explicitly created to represent the discriminative features important to the network. To compare we use both ImageNet and challenging fine-grained datasets over a range of metrics. We demonstrate experimentally that our methods provide the best local and global accuracy compared to Grad-CAM, Grad-CAM++, LIME, XRAI, and RISE.
A Service-based Joint Model Used for Distributed Learning: Application for Smart Agriculture

Distributed analytics facilitate to make the data-driven services smarter for a wider range of applications in many domains, including agriculture.The key to producing services at such level is timely analysis for deriving insights from reliable data.Centralized data analytic services are becoming infeasible due to limitations in the Information and Communication Technologies (ICT) infrastructure, timeliness of the information, and data ownership.Distributed Machine Learning (DML) platforms facilitate efficient data analysis and overcome such limitations effectively.Federated Learning (FL) is a DML methodology, which enables optimizing resource consumption while performing privacy-preserved timely analytics.In order to create such services through FL, there needs to be innovative machine learning (ML) models as data complexity as well as application requirements limit the applicability of existing ML models.Even though NN-based models are highly advantageous, use of NN in FL settings is limited with thin clients (with less computational capabilities) and high-dimensional data (with large number of model parameters).Therefore, in this paper, we propose a novel Neural Network (NN)and Partial Least Square (PLS) regression-based joint FL model (FL-NNPLS).Its predictive performance is evaluated under sequentialand parallel-updating based FL algorithms in a smart farming context for milk quality analysis.Smart farming is a fast-growing industrial sector which requires effective analytics platforms to enable sustainable farming practices.However, the use of advanced ML techniques are still at a early stage for improving the effectiveness of farming practices.Our FL-NNPLS approach performs and compares well with a centralized approach and demonstrates state-of-the-art performance.
Self-supervised Visual-LiDAR Odometry with Flip Consistency

Most learning-based methods estimate ego-motion by utilizing visual sensors, which suffer from dramatic lighting variations and textureless scenarios. In this paper, we incorporate sparse but accurate depth measurements obtained from lidars to overcome the limitation of visual methods. To this end, we design a self-supervised visual-lidar odometry (Self-VLO) framework. It takes both monocular images and sparse depth maps projected from 3D lidar points as input, and produces pose and depth estimations in an end-to-end learning manner, without using any ground truth labels. To effectively fuse two modalities, we design a two-pathway encoder to extract features from visual and depth images and fuse the encoded features with those in decoders at multiple scales by our fusion module. We also adopt a siamese architecture and design an adaptively weighted flip consistency loss to facilitate the self-supervised learning of our VLO. Experiments on the KITTI odometry benchmark show that the proposed approach out-performs all self-supervised visual or lidar odometries. It also performs better than fully supervised VOs, demonstrating the power of fusion.
A Multi-Class Hinge Loss for Conditional GANs

We propose a new algorithm to incorporate class conditional information into the critic of GANs via a multi-class generalization of the commonly used Hinge loss that is compatible with both supervised and semi-supervised settings. We study the compromise between training a state of the art generator and an accurate classifier simultaneously, and propose a way to use our algorithm to measure the degree to which a generator and critic are class conditional. We show the trade-off between a generator-critic pair respecting class conditioning inputs and generating the highest quality images. With our multi-hinge loss modification we are able to improve Inception Scores and Frechet Inception Distance on the Imagenet dataset.
What is an alternation?

Abstract An important subset of the empirical research conducted within usage-based construction grammar is formed by alternation studies. Still, it is not always clear what exactly qualifies as an alternation. This paper takes stock of six possible ways of defining an alternation. Three of these definitions are argued to be particularly suitable for the research program of usage-based construction grammar. The paper zooms in on those and discusses their practical consequences and (dis)advantages.
Smart Object Detection and Home Appliances Control System in Smart Cities

During the last decade the emergence of Internet of Things (IoT) based applications inspired the world by providing state of the art solutions to many common problems. From traffic management systems to urban cities planning and development, IoT based home monitoring systems, and many other smart applications. Regardless of these facilities, most of these IoT based solutions are data driven and results in small accuracy values for smaller datasets. In order to address this problem, this paper presents deep learning based hybrid approach for the development of an IoT-based intelligent home security and appliance control system in the smart cities. This hybrid model consists of; convolution neural network and binary long short term model for the object detection to ensure safety of the homes while IoT based hardware components like; Raspberry Pi, Amazon Web services cloud, and GSM modems for remotely accessing and controlling of the home appliances. An android application is developed and deployed on Amazon Web Services (AWS) cloud for the remote monitoring of home appliances. A GSM device and Message queuing telemetry transport (MQTT) are integrated for communicating with the connected IoT devices to ensure the online and offline communication. For object detection purposes a camera is connected to Raspberry Pi using the proposed hybrid neural network model. The applicability of the proposed model is tested by calculating results for the object at varying distance from the camera and for different intensity levels of the light. Besides many applications the proposed model promises for providing optimum results for the small amount of data and results in high recognition rates of 95.34% compared to the conventional recognition model (k nearest neighbours) recognition rate of 76%.
Efficient Asynchronous Vertical Federated Learning via Gradient Prediction and Double-End Sparse Compression

Vertical federated learning is a subset of federated learning whose training dataset is vertically distributed among the federations. However, as a natural synchronous algorithm, classical vertical federated learning suffers from “Liebig's Law”. In this paper, we propose a novel asynchronous vertical federated learning framework with gradient prediction and double-end sparse compression to accelerate the training process and reduce the intermediate result transmission. Our simulation results show that our framework can achieve 65.41% training acceleration and 86.90% traffic volume reduction at no cost of accuracy compared with the classical framework.
Path Planning for Shepherding a Swarm in a Cluttered Environment using Differential Evolution

Shepherding involves herding a swarm of agents (sheep) by another a control agent (sheepdog) towards a goal. Multiple approaches have been documented in the literature to model this behaviour. In this paper, we present a modification to a well-known shepherding approach, and show, via simulation, that this modification improves shepherding efficacy. We then argue that given complexity arising from obstacles laden environments, path planning approaches could further enhance this model. To validate this hypothesis, we present a 2-stage evolutionary-based path planning algorithm for shepherding a swarm of agents in 2D environments. In the first stage, the algorithm attempts to find the best path for the sheepdog to move from its initial location to a strategic driving location behind the sheep. In the second stage, it calculates and optimises a path for the sheep. It does so by using way points on that path as the sequential sub-goals for the sheepdog to aim towards. The proposed algorithm is evaluated in obstacle laden environments via simulation with further improvements achieved.