Reviewerly/exHarmony huggingface dataset:
Radar-Based Gesture Recognition System using Spiking Neural Network

Hand gesture recognition has become an increasingly important functionality in intelligent human-computer interfaces, finding applications in automotive, gaming and consumer industries. In this paper, we present an embedded gesture recognition system using frequency modulated continuous wave radar operating at 60 GHz. To facilitate low-power and low latency operation of the proposed system, spiking neural networks are used which are sparse in time and space, and event-driven. The experimental results demonstrate that the proposed neuromorphic implementation is capable of achieving a high recognition rate of 97.5% which is comparable to its deep learning counterparts in identifying radar gestures, namely up-down, down-up, swipe and finger rub.
Imbalanced fault identification via embedding-augmented Gaussian prototype network with meta-learning perspective

Abstract In practical engineering, the number of acquired fault samples from different categories can be vastly different due to the low probability of key equipment malfunctioning. When training the imbalanced data, many methods focus on balancing the number of samples or weights between different categories, which may be time-consuming and easy to over-fit. To address this problem, we propose the embedding-augmented Gaussian prototype network (EGPN), which applies a new training mechanism from the perspective of meta-learning. We only train the categories with large samples and the remaining categories only appear in the testing process to calculate untrained prototypes. EGPN includes a feature-embedding augmented module, weighted prototype module and metric module. Firstly, ordinary convolution and dilated convolution are mixed to capture different frequency bands simultaneously, and the residual attention module is added to highlight key features and suppress unimportant features. Different prototypes are calculated by weighting to the embedding vectors through the Gaussian covariance matrix. Finally, the classification is done according to the modified distance. The experiments in the two datasets indicate that the proposed method can effectively recognize the untrained categories with only a few samples used as the prototypes, and can tackle the problem of identifying imbalanced fault data efficiently.
Talisman: Targeted Active Learning for Object Detection with Rare Classes and Slices Using Submodular Mutual Information

Deep neural networks based object detectors have shown great success in a variety of domains like autonomous vehicles, biomedical imaging, etc., however their success depends on the availability of a large amount of data from the domain of interest. While deep models perform well in terms of overall accuracy, they often struggle in performance on rare yet critical data slices. For e.g., detecting objects in rare data slices like "motorcycles at night" or "bicycles at night" for self-driving applications. Active learning (AL) is a paradigm to incrementally and adaptively build training datasets with a human in the loop. However, current AL based acquisition functions are not well-equipped to mine rare slices of data from large real-world datasets, since they are based on uncertainty scores or global descriptors of the image. We propose Talisman, a novel framework for Targeted Active Learning for object detectIon with rare slices using Submodular MutuAl iNformation. Our method uses the submodular mutual information functions instantiated using features of the region of interest (RoI) to efficiently target and acquire images with rare slices. We evaluate our framework on the standard PASCAL VOC07+12 [8] and BDD100K [31], a real-world large-scale driving dataset. We observe that Talisman consistently outperforms a wide range of AL methods by $$\approx $$ 5%- - 14% in terms of average precision on rare slices, and $$\approx $$ 2%–4% in terms of mAP. The code for Talisman is available here: https://github.com/surajkothawade/talisman .
An in-depth experimental study of sensor usage and visual reasoning of robots navigating in real environments

Visual navigation by mobile robots is classically tackled through SLAM plus optimal planning, and more recently through end-to-end training of policies implemented as deep networks. While the former are often limited to waypoint planning, but have proven their efficiency even on real physical environments, the latter solutions are most frequently employed in simulation, but have been shown to be able learn more complex visual reasoning, involving complex semantic regularities. Navigation by real robots in physical environments is still an open problem. End-to-end training approaches have been thoroughly tested in simulation only, with experiments involving real robots being restricted to rare performance evaluations in simplified laboratory conditions. In this work we present an in-depth study of the performance and reasoning capacities of real physical agents, trained in simulation and deployed to two different physical environments. Beyond benchmarking, we provide insights into the generalization capabilities of different agents training in different conditions. We visualize sensor usage and the importance of the different types of signals. We show, that for the PointGoal task, an agent pre-trained on wide variety of tasks and fine-tuned on a simulated version of the target environment can reach competitive performance without modelling any sim2real transfer, i.e. by deploying the trained agent directly from simulation to a real physical robot.
Review4Repair: Code review aided automatic program repairing

Learning-based automatic program repair techniques are showing promise to provide quality fix suggestions for detected bugs in the source code of the software. These tools mostly exploit historical data of buggy and fixed code changes and are heavily dependent on bug localizers while applying to a new piece of code. With the increasing popularity of code review, dependency on bug localizers can be reduced. Besides, the code review-based bug localization is more trustworthy since reviewers’ expertise and experience are reflected in these suggestions. The natural language instructions scripted on the review comments are enormous sources of information about the bug’s nature and expected solutions. However, none of the learning-based tools has utilized the review comments to fix programming bugs to the best of our knowledge. In this study, we investigate the performance improvement of repair techniques using code review comments. We train a sequence-to-sequence model on 55,060 code reviews and associated code changes. We also introduce new tokenization and preprocessing approaches that help to achieve significant improvement over state-of-the-art learning-based repair techniques. We boost the top-1 accuracy by 20.33% and top-10 accuracy by 34.82%. We could provide a suggestion for stylistics and non-code errors unaddressed by prior techniques. We believe that the automatic fix suggestions along with code review generated by our approach would help developers address the review comment quickly and correctly and thus save their time and effort.
FAMINet: Learning Real-Time Semisupervised Video Object Segmentation With Steepest Optimized Optical Flow

Semisupervised video object segmentation (VOS) aims to segment a few moving objects in a video sequence, where these objects are specified by annotation of the first frame. The optical flow has been considered in many existing semisupervised VOS methods to improve the segmentation accuracy. However, the optical flow-based semisupervised VOS methods cannot run in real time due to high complexity of optical flow estimation. A FAMINet, which consists of a feature extraction network (F), an appearance network (A), a motion network (M), and an integration network (I), is proposed in this study to address the above-mentioned problem. The appearance network outputs an initial segmentation result based on static appearances of objects. The motion network estimates the optical flow via very few parameters, which are optimized rapidly by an online memorizing algorithm named relaxed steepest descent. The integration network refines the initial segmentation result using the optical flow. Extensive experiments demonstrate that the FAMINet outperforms other state-of-the-art semisupervised VOS methods on the DAVIS and YouTube-VOS benchmarks and achieves a good trade-off between accuracy and efficiency. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/liuziyang123/FAMINet</uri> .
Estimation of Interpupillary Distance Based on Eye Movements in Virtual Reality Devices

A mismatch between the interpupillary distance (IPD) and inter-optical system distance (IOSD) in virtual reality (VR) applications can lead to discomfort. The IOSD must be adjustable according to the user’s IPD to solve this issue. In this study, we investigate IPD estimation methods by tracking eye movements such as conjugate eye movement (CEM) and vergence. We hypothesize that the distance between the two pupils maintained during the CEM and is identical to the IPD. The vergence-based method induces eye divergence and determines the IPD as the maximum distance between pupils. Experiments with visual stimuli to induce CEM and divergence were conducted. The average errors of the estimated IPDs for the CEM-based and vergence-based methods were 2.06 and 1.30 mm, respectively. Furthermore, the analysis results show that the proposed methods can effectively reduce the IPD-IOSD difference and are especially helpful for users with a small IPD. If the IOSD is adjusted to the IPD estimated by the proposed methods, then VR discomfort can be eliminated.
De-CAPTCHA: A novel DFS based approach to solve CAPTCHA schemes

CAPTCHA stands for Completely Automated Public Turing Test to Tell Computers and Humans Apart. CAPTCHAs are used as security mechanism in web applications to differentiate between real users and automated users, also known as bots. Text-based CAPTCHAs are the popularly used CAPTCHA schemes due to their simplicity and thus, they are still being used despite the proposal of several attack mechanisms. In this work, the authors have proposed a novel approach to solve CAPTCHA schemes. In this approach, the authors have used Depth First Search algorithm for the extraction of characters from CAPTCHAs and Convolutional Neural Network for recognizing these extracted characters. The proposed approach was validated on 3000+ CAPTCHA schemes and proved to be efficient by providing an average accuracy of more than 92.0% in detecting CAPTCHA schemes. • Achieved an average Accuracy Rate of more than 92.0% in detecting CAPTCHA schemes. • The model solved the CAPTCHA schemes in IRCTC with average Accuracy Rate of 98.0%. • The schemes in UIDAI, Digital India are solved with average Accuracy Rate of 90.6%.
Memory at your service

Co-location and memory sharing between latency-critical services, such as key-value store and web search, and best-effort batch jobs is an appealing approach to improving memory utilization in multi-tenant datacenter systems. However, we find that the very diverse goals of job co-location and the GNU/Linux system stack can lead to severe performance degradation of latency-critical services under memory pressure in a multi-tenant system.
Generalized Facial Manipulation Detection with Edge Region Feature Extraction

This paper presents a generalized and robust face manipulation detection method based on the edge region features appearing in images. Most contemporary face synthesis processes include color awkwardness reduction but damage the natural fingerprint in the edge region. In addition, these color correction processes do not proceed in the non-face background region. We also observe that the synthesis process does not consider the natural properties of the image appearing in the time domain. Considering these observations, we propose a facial forensic framework that utilizes pixel-level color features appearing in the edge region of the whole image. Furthermore, our framework includes a 3D-CNN classification model that interprets the extracted color features spatially and temporally. Unlike other existing studies, we conduct authenticity determination by considering all features extracted from multiple frames within one video. Through extensive experiments, including real-world scenarios to evaluate generalized detection ability, we show that our framework outperforms state-of-the-art facial manipulation detection technologies in terms of accuracy and robustness.
A Hybrid Approach for Predicting Bitcoin Price Using Bi-LSTM and Bi-RNN Based Neural Network

Bitcoin is an electronic or digital currency. However, unlike government-issued currencies, there is no single entity that issues bitcoin or is in charge of processing transactions. That’s why bitcoin has become popular in the recent era. As bitcoin’s price fluctuates a lot in a short period, it is very challenging to predict the bitcoin price accurately. In this paper, we proposed a method by merging different highest-level building blocks in deep learning such as Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), Bi-LSTM, Recurrent Neural Network (RNN), and Bi-RNN to predict bitcoin price as accurately as possible. Though CNN, LSTM, Bi-LSTM, RNN, Bi-RNN or ARIMA independently produce an acceptable result, our proposed hybrid method is fairly reliable compared to individual building block of the network as the proposed method outperforms other individual models and achieves RMSE, MAE, MAPE, MedAE of 2.69%, 1.78%, 2.20%, and 1.23% respectively.
A Vehicle Detection Model Based on 5G-V2X for Smart City Security Perception

Security perception systems based on 5G-V2X have become an indispensable part of smart city construction. However, the detection speed of traditional deep learning models is slow, and the low-latency characteristics of 5G networks cannot be fully utilized. In order to improve the safety perception ability based on 5G-V2X, increase the detection speed in vehicle perception. A vehicle perception model is proposed. First, an adaptive feature extraction method is adopted to enhance the expression of small-scale features and improve the feature extraction ability of small-scale targets. Then, by improving the feature fusion method, the shallow information is fused layer by layer to solve the problem of feature loss. Finally, the attention enhancement method is introduced to increase the center point prediction ability and solve the problem of target occlusion. The experimental results show that the UA-DETRAC data set has a good detection effect. Compared with the vehicle detection capability before the improvement, the detection accuracy and speed have been greatly improved, which effectively improves the security perception capability based on the 5G-V2X system, thereby promoting the construction of smart cities.
Second-Order Guarantees of Stochastic Gradient Descent in Nonconvex Optimization

Recent years have seen increased interest in performance guarantees of gradient descent algorithms for nonconvex optimization. A number of works have uncovered that gradient noise plays a critical role in the ability of gradient descent recursions to efficiently escape saddle-points and reach second-order stationary points. Most available works limit the gradient noise component to be bounded with probability one or sub-Gaussian and leverage concentration inequalities to arrive at high-probability results. We present an alternate approach, relying primarily on mean-square arguments and show that a more relaxed relative bound on the gradient noise variance is sufficient to ensure efficient escape from saddle points without the need to inject additional noise, employ alternating step sizes, or rely on a global dispersive noise assumption, as long as a gradient noise component is present in a descent direction for every saddle point.
An improved LogNNet classifier for IoT applications

In the age of neural networks and Internet of Things (IoT), the search for new neural network architectures capable of operating on devices with limited computing power and small memory size is becoming an urgent agenda. Designing suitable algorithms for IoT applications is an important task. The paper proposes a feed forward LogNNet neural network, which uses a semi-linear Henon type discrete chaotic map to classify MNIST-10 dataset. The model is composed of reservoir part and trainable classifier. The aim of the reservoir part is transforming the inputs to maximize the classification accuracy using a special matrix filing method and a time series generated by the chaotic map. The parameters of the chaotic map are optimized using particle swarm optimization with random immigrants. As a result, the proposed LogNNet/Henon classifier has higher accuracy and the same RAM usage, compared to the original version of LogNNet, and offers promising opportunities for implementation in IoT devices. In addition, a direct relation between the value of entropy and accuracy of the classification is demonstrated.
Advances in XAI: Explanation Interfaces in Healthcare

Artificial Intelligence based algorithms are gaining a main role in healthcare. However, the black-box nature of models such as deep neural networks challenges the users’ trust. Explainable Artificial Intelligence (XAI) strives for more transparent and interpretable AI, achieving intelligent systems that help the user understand the AI predictions and decisions increasing the trustfulness and reliability of the systems. In this work, we present an overview of contexts in healthcare where explanation interfaces are used. We conduct a search in main research databases and compile works related to healthcare to show the widespread applicability of the intelligent systems and how researchers offer explanations in form of natural text, parameters influence, visualizations of data graphs or saliency maps.
Using Bandit Algorithms for Project Selection in Cross-Project Defect Prediction

Background: defect prediction model is built using historical data from previous versions/releases of the same project. However, such historical data may not exist in case of newly developed projects. Alternatively, one can train a model using data obtained from external projects. This approach is known as cross-project defect prediction (CPDP). In CPDP, it is still difficult to utilize external projects' data or decide which particular project to use to train a model. Aim: to address this issue, we apply bandit algorithm (BA) to CPDP in order to select the most suitable training project from a set of projects. Method: BA-based prediction iteratively reselects the project after each module is tested, considering the accuracy of the predictions. As baselines, we used simple CPDP methods such as training a model with randomly selected project. All models were built using logistic regression. Results: We experimented our approach on two datasets (NASA and DAMB, with a total of 12 projects). The BA-based defect prediction models resulted in, on average, a higher accuracy (AUC and F1 score) than the baselines. Conclusion: in this preliminarily study, we demonstrate the feasibility of using BA in the context of CPDP. Our initial assessment shows that the use BA for predicting defects in CPDP is promising and may outperform existing approaches.
Arcsine laws for random walks generated from random permutations with applications to genomics

Abstract A classical result for the simple symmetric random walk with 2 n steps is that the number of steps above the origin, the time of the last visit to the origin, and the time of the maximum height all have exactly the same distribution and converge when scaled to the arcsine law. Motivated by applications in genomics, we study the distributions of these statistics for the non-Markovian random walk generated from the ascents and descents of a uniform random permutation and a Mallows( q ) permutation and show that they have the same asymptotic distributions as for the simple random walk. We also give an unexpected conjecture, along with numerical evidence and a partial proof in special cases, for the result that the number of steps above the origin by step 2n for the uniform permutation generated walk has exactly the same discrete arcsine distribution as for the simple random walk, even though the other statistics for these walks have very different laws. We also give explicit error bounds to the limit theorems using Stein’s method for the arcsine distribution, as well as functional central limit theorems and a strong embedding of the Mallows( q ) permutation which is of independent interest.
Forecasting in Enterprise Resource Planning (ERP) Systems: A Survey

Using machine learning in ERP enables the organization to store, manage, and analyze their data to get the right decisions and gain valuable insights that were previously unmanageable. There are many industries and lines of business that contain large volumes of data such as manufacturing, finance, and healthcare … etc. One of the most usages of machine learning in ERP is forecasting which refers to the prediction of the future based on past data. This paper surveys different approaches for forecasting in ERP to gain profit for the business process.
WITHDRAWN: Influencing factors affecting work performance and personal career development—taking college music teachers as an example

Music teachers' work performance and personal career development are essential for the improvement of work culture. There are influencing factors of job performance and satisfaction that affect music teachers' career development in most regions. Less motivation, lowering of wages, and lack of scheduled periods are the issues that impact job performance and satisfaction. Therefore a questionnaire is developed among 40 music teachers for finding the problems, and thus it is created by an encouraging satisfaction system using intelligence monitoring (ESS-IM). A set of related questionnaires is conducted using reliability test analysis based on the motivation factors, wages, and scheduled periods. The unit comprises tabulated dataset values combined into a factor by its method for residing phases of research. A data analysis phase that estimates the tabulated dataset for ensuring the values to college management with related coefficient factors is issued by a relative managerial method. The method provides an intelligent unit by analyzing the values in the management system for displaying the affected characteristics in the output of results by perceptions. Therefore the dataset acquisition ensures to provide organizational output units for restricting the issues. Thus, the simulation analysis developed in ESS-IM significantly impacts college management with an accuracy efficiency of 97.3% than current methods.
Application of Virtual Reality Human-Computer Interaction Technology Based on the Sensor in English Teaching

In order to improve the online English teaching effect, the paper applies the sensor and human-computer interaction into the English teaching. The paper improves the sensor information by applying Kalman Filter, combines sensor positioning algorithm to trace the students in the English teaching online, and turns the kernels by the skeleton algorithm into corresponding coordinates of space rectangular coordinate system taking the waist as a coordinate origin to get a human-computer interaction skeleton model in the virtual reality. According to the actual needs of English teaching human-computer interaction, the paper builds a new English teaching system based on the sensor and the human-computer interaction and tests its performance. The experiments suggest that the smart system in the paper can effectively improve English teaching effects.