Reviewerly/exHarmony huggingface dataset:
Tools for Image Analysis and First Wall Protection at W7-X

Wendelstein 7-X (W7-X) is the most advanced operating stellarator in fusion research.The monitoring and control of the heat loads on the plasma facing components (PFCs) will be critical during the next operation phase, in which the ten water-cooled divertors will have to withstand heat loads of the order of 10MW/m 2 .This paper gives an overview of the image analysis tools for the camera spatial calibration and scene modelling, which are an essential part of the real-time PFCs protection system currently under development at W7-X.Some applications of these tools are also provided, specifically for the strike-line characterization.
Physics-Driven Machine Learning for Time-Optimal Path Planning in Stochastic Dynamic Flows

Optimal path planning of autonomous marine agents is important to minimize operational costs of ocean observation systems. Within the context of DDDAS, we present a Reinforcement Learning (RL) framework for computing a dynamically adaptable policy that minimizes expected travel time of autonomous vehicles between two points in stochastic dynamic flows. To forecast the stochastic dynamic environment, we utilize the reduced order data-driven dynamically orthogonal (DO) equations. For planning, a novel physics-driven online Q-learning is developed. First, the distribution of exact time optimal paths predicted by stochastic DO Hamilton-Jacobi level set partial differential equations are utilized to initialize the action value function (Q-value) in a transfer learning approach. Next, the flow data collected by onboard sensors are utilized in a feedback loop to adaptively refine the optimal policy. For the adaptation, a simple Bayesian estimate of the environment is performed (the DDDAS data assimilation loop) and the inferred environment is used to update the Q-values in an $$\epsilon -$$ greedy exploration approach (the RL step). To validate our Q-learning solution, we compare it with a fully offline, dynamic programming solution of the Markov Decision Problem corresponding to the RL framework. For this, novel numerical schemes to efficiently utilize the DO forecasts are derived and computationally efficient GPU-implementation is completed. We showcase the new RL algorithm and elucidate its computational advantages by planning paths in a stochastic quasi-geostrophic double gyre circulation.
Generic Framework for Privacy Preservation in Cyber-Physical Systems

Cyber-physical system (CPS) is an evolving technology, and as usual, security is a vital issue in its adaptation. Privacy is a primary security requirement in CPS and can cause havoc if unresolved. Much work is done in the area of privacy preservation in CPS, but they are domain-specific. There is no generic mechanism for privacy preservation in CPS. Here, we design a framework for privacy preservation in CPS. The proposed study aims to integrate separate privacy protection mechanisms in different levels of the CPS architecture, addressing different kinds of privacy as information contents, locations, identities, dates and times, addresses, etc., within a common structure.
A Novel Time-Aware Hybrid Recommendation Scheme Combining User Feedback and Collaborative Filtering

Nowadays, recommender systems are used widely in various fields to solve the problem of information overload. Collaborative filtering and content-based are representative solutions in recommender systems, however, the content-based model has some shortcomings, such as single kind of recommendation results, lack of effective perception of user preferences; while, for the collaborative filtering model, there is a cold start problem, and such a model is greatly affected by its adopted clustering algorithm. To address these issues, a hybrid recommendation scheme is proposed in this article, which is based on both collaborative filtering and content-based. In this scheme, we propose the concept of time impact factor, and a time-aware user preference model is built based on it. Also, user feedback on recommendation items is utilized to improve the accuracy of our proposed recommendation model. Finally, the proposed hybrid model combines the results of content recommendation and collaborative filtering based on the logistic regression algorithm.
Hand Sign Translation to Audio Message and Text Message: A Device

In an enormous world population of more than 7.6 billion people about 47 million people are verbally challenged or speech impaired. Thus, in order to express themselves, these people adapt the method of visual contact and non verbal communication that includes gestures. This paper provides an easy technique to convert the hand gestures by speech impaired person to precise text and speech messages. The hand gestures are taken as an input with the help of the flex sensors and are further processed by a microcontroller in which a predefined data base is created and stored in order to deliver the desired message. This message is then evaluated to be flashed on the LCD display and the corresponding sound signal stowed in the Secure Digital card is transferred to the speaker after amplification for the assurance of higher sound quality of the audio message signal.
Multi-source transfer regression via source-target pairwise segment

Transfer learning addresses the problem of how to leverage acquired knowledge from a source domain to improve the learning efficiency and accuracy of the target domain that has insufficient labeled data. Instead of one source domain, multiple domains could be the source domains that are available for knowledge transfer in practice. However, there are large differences between the source and target domains, how to extract the useful knowledge from these different source domains remains a problem. To solve this problem, we propose a source-target pairwise segment method for multi-source transfer regression (STPS-MTR). The STPS-MTR method adaptively segments the different source domains and the target domain into different similar parts, and it extracts the most similar part in different source domains as the transfer knowledge. The STPS-MTR method can effectively extract the transfer knowledge from different source domains even when the source domain and the target domain have relatively low similarity, and it can avoid the negative influence between different source domains to ensure the transfer performance. Experimental results using synthetic datasets and real-world datasets demonstrate that the proposed method has better performance than existing methods, particularly when there are significant differences between different source domains and the target domain.
WITHDRAWN: Key point oriented shape features and SVM classifier for content based image retrieval

For personal digital libraries as well as automatically acquired image collections, traditional text and metadata based approaches for image retrieval are not sufficient. Commonly, these methods lack detailed descriptions that could be used for searching the required image. In digital libraries, content based image retrieval (CBIR) methods are required to support better image search. A highly robust and efficient shape descriptor based CBIR model has been proposed in this work. Canny edge detection algorithm is employed to identify strong key points on the edges. For key point set formation, the generalized distance transform scheme has been used. Combination of shape and key point identification are used to make proposed CBIR system more robust. Extracted features are fed to Support Vector Machine (SVM) classifier. For retrieval we used similarity matching using Euclidean distance metric. The proposed model provides superior performance while comparing with existing retrieval models.
Detecting and Tracking Moving Airplanes from Space Based on Normalized Frame Difference Labeling and Improved Similarity Measures

The emerging satellite videos provide the opportunity to detect moving objects and track their trajectories, which were not possible for remotely sensed imagery with limited temporal resolution. So far, most studies using satellite video data have been concentrated on traffic monitoring through detecting and tracking moving cars, whereas the studies on other moving objects such as airplanes are limited. In this paper, an integrated method for monitoring moving airplanes from a satellite video is proposed. First, we design a normalized frame difference labeling (NFDL) algorithm to detect moving airplanes, which adopts a non-recursive strategy to deliver stable detection throughout the whole video. Second, the template matching (TM) technique is utilized for tracking the detected moving airplanes in the frame sequence by improved similarity measures (ISMs) with better rotation invariance and model drift suppression ability. Template matching with improved similarity measures (TM-ISMs) is further implemented to handle the leave-the-scene problem. The developed method is tested on a satellite video to detect and track eleven moving airplanes. Our NFDL algorithm successfully detects all the moving airplanes with the highest F1 score of 0.88 among existing algorithms. The performance of TM-ISMs is compared with both its traditional counterparts and other state-of-the-art tracking algorithms. The experimental results show that TM-ISMs can handle both rotation and leave-the-scene problems. Moreover, TM-ISMs achieve a very high tracking accuracy of 0.921 and the highest tracking speed of 470.62 frames per second.
Internet of Things technology in mineral remote sensing monitoring

Abstract At present, the rapid development of the national economy consumes a lot of mineral resources, but the national resource extraction efficiency is low. The traditional prospecting methods consume time, money, and personnel, which cannot meet the national demand for resources in a short time. Therefore, technological innovation is needed to reduce consumption in all aspects. With the continuous development of Internet of Things technology, its application is also more extensive. This paper studies the application of Internet of Things technology in mineral remote sensing monitoring. First of all, this research mainly uses the wireless communication technology in the Internet of Things to achieve data transmission and sharing in a short distance and, second, based on the remote sensing monitoring results of the mine environment to comprehensively understand the mining area and find the suitable mining mineral resources at the fastest speed; finally, the dynamic frame time slot ALOHA algorithm is used to estimate the existing data to ensure the availability of the data. The experimental data show that vector point encryption is performed on the center line of the mine at intervals of 10, 15, 20, 30, and 50 m, respectively, and the dynamic frame slot algorithm is used to calculate the appropriate mine mining curve radius. The experimental results show that the application of Internet of Things technology in mineral remote sensing monitoring can accelerate the mining of mineral resources.
CoTeRe-Net: Discovering Collaborative Ternary Relations in Videos

Modeling relations is crucial to understand videos for action and behavior recognition. Current relation models mainly reason about relations of invisibly implicit cues, while important relations of visually explicit cues are rarely considered, and the collaboration between them is usually ignored. In this paper, we propose a novel relation model that discovers relations of both implicit and explicit cues as well as their collaboration in videos. Our model concerns Collaborative Ternary Relations (CoTeRe), where the ternary relation involves channel (C, for implicit), temporal (T, for implicit), and spatial (S, for explicit) relation (R). We devise a flexible and effective CTSR module to collaborate ternary relations for 3D-CNNs, and then construct CoTeRe-Nets for action recognition. Extensive experiments on both ablation study and performance evaluation demonstrate that our CTSR module is significantly effective with approximate $$3\%$$ gains and our CoTeRe-Nets outperform state-of-the-art approaches on three popular benchmarks. Boosts analysis and relations visualization also validate that relations of both implicit and explicit cues are discovered with efficacy by our method. Our code is available at https://github.com/zhenglab/cotere-net .
Moving Target Defense Considerations in Real-Time Safety- and Mission-Critical Systems

Moving-target defenses (MTDs) have been widely studied for common general-purpose and enterprise-computing applications. Indeed, such work has produced highly effective, low-overhead defenses that are now commonly deployed in many systems today. One application space that has seen comparatively little focus is that of safety- and mission-critical systems, which are often real-time systems (RTS) with temporal requirements. Furthermore, such systems are increasingly being targeted by attackers, such as in industrial control systems (ICS), including power grids. The strict timing requirements of these systems presents a different design objective than is common in general-purpose applications -- systems should be designed around the worst-case performance, rather than the average case. Perhaps in part due to these alternative design considerations, many real-time systems have not benefited from much of the work on software security that common general-purpose and enterprise applications have, despite the ubiquity of real-time systems that actively control so many applications we as a society have come to rely on, from power generation and distribution, to automotive and avionic applications, and many others.
Comparison of Genetic Algorithm and Particle Swarm Optimization of Ensemble Neural Networks for Complex Time Series Prediction

In this paper the development of a method for the design of the general architecture of ensemble neural networks for time series prediction is presented. This method consists on the optimization of the ensemble neural network with response integration based on type-1 and type-2 fuzzy logic. The optimization algorithms that are used to illustrate the proposed method are the genetic algorithm and the particle swarm optimization algorithm and the purpose of applying these methods is to find the best possible architecture of the neural network ensemble with the lowest prediction error. Simulation results with several complex times series are presented to show the advantage of the proposed method, also comparisons are made with other time series, such as Mexican Stock Exchange, and Dow Jones Stock Exchange.
Towards a Free, Forced Phonetic Aligner for Brazilian Portuguese Using Kaldi Tools

Phonetic analysis of speech, in general, requires the alignment of audio samples to its phonetic transcription. This task could be performed manually for a couple of files, but as the corpus grows large it becomes unfeasibly time-consuming, which emphasizes the need for computational tools that perform such speech-phonemes forced alignment automatically. Therefore, due to the scarce availability of phonetic alignment tools for Brazilian Portuguese (BP), this work describes the evolution process towards creating a free phonetic alignment tool for BP using Kaldi, a toolkit that has been the state of the art for open-source speech recognition. Five acoustic models were trained with Kaldi and tested in phonetic alignment, where the evaluation took place in terms of the phone boundary metric. The results show that its performance is similar to some Kaldi-based aligners for other languages, and superior to an outdated phonetic aligner for BP based on HTK toolkit.
Deep Learning for Privacy in Multimedia

We discuss the design and evaluation of machine learning algorithms that provide users with more control on the multimedia information they share. We introduce privacy threats for multimedia data and key features of privacy protection. We cover privacy threats and mitigating actions for images, videos, and motion-sensor data from mobile and wearable devices, and their protection from unwanted, automatic inferences. The tutorial offers theoretical explanations followed by examples with software developed by the presenters and distributed as open source.
Unsupervised Deep Persistent Monocular Visual Odometry and Depth Estimation in Extreme Environments

In recent years, unsupervised deep learning approaches have received significant attention to estimating the depth and visual odometry (VO) from unlabelled monocular image sequences. However, their performance is limited in challenging environments due to perceptual degradation, occlusions, and rapid motions. Moreover, the existing unsupervised methods suffer from the lack of scale-consistency constraints across frames, which causes that the VO estimators fail to provide persistent trajectories over long sequences. In this study, we propose an unsupervised monocular deep VO framework that predicts a six-degrees-of-freedom pose camera motion and depth map of the scene from unlabelled RGB image sequences. We provide detailed quantitative and qualitative evaluations of the proposed framework on a) a challenging dataset collected during the DARPA Subterranean challenge <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> ; and b) the benchmark KITTI and Cityscapes datasets. The proposed approach significantly outperforms state-of-the-art unsupervised deep VO and depth prediction methods under perceptually degraded conditions providing better results for both pose estimation and depth recovery. Furthermore, it achieves state-of-the-art results in most of the VO and depth metrics on benchmark datasets. The presented approach is part of the solution used by the COSTAR team participating in the DARPA Subterranean Challenge.
Resampling Factor Estimation via Dual-stream Convolutional Neural Network

The estimation of image resampling factors is an important problem in image forensics.Among all the resampling factor estimation methods, spectrumbased methods are one of the most widely used methods and have attracted a lot of research interest.However, because of inherent ambiguity, spectrum-based methods fail to discriminate upscale and downscale operations without any prior information.In general, the application of resampling leaves detectable traces in both spatial domain and frequency domain of a resampled image.Firstly, the resampling process will introduce correlations between neighboring pixels.In this case, a set of periodic pixels that are correlated to their neighbors can be found in a resampled image.Secondly, the resampled image has distinct and strong peaks on spectrum while the spectrum of original image has no clear peaks.Hence, in this paper, we propose a dual-stream convolutional neural network for image resampling factors estimation.One of the two streams is gray stream whose purpose is to extract resampling traces features directly from the rescaled images.The other is frequency stream that discovers the differences of spectrum between rescaled and original images.The features from two streams are then fused to construct a feature representation including the resampling traces left in spatial and frequency domain, which is later fed into softmax layer for resampling factor estimation. Experimental results show that the proposed method is effective on resampling factor estimation and outperforms some CNN-based methods.
Characterizing HPC Performance Variation with Monitoring and Unsupervised Learning

As HPC systems grow larger and more complex, characterizing the relationships between their different components and gaining insight on their behavior becomes difficult. In turn, this puts a burden on both system administrators and developers who aim at improving the efficiency and reliability of systems, algorithms and applications. Automated approaches capable of extracting a system’s behavior, as well as identifying anomalies and outliers, are necessary more than ever.In this work we discuss our exploratory study of Bayesian Gaussian mixture models, an unsupervised machine learning technique, to characterize the performance of an HPC system’s components, as well as to identify anomalies, based on sensor data. We propose an algorithmic framework for this purpose, implement it within the DCDB monitoring and operational data analytics system, and present several case studies carried out using data from a production HPC system.
Learning Size and Fit from Fashion Images

Finding clothes that fit has increasingly become a hot topic in the e-commerce fashion industry. Mainly due to causing frustration on the customers side, and the large ecological and economical footprint on the companies side. Most approaches addressing this problem are based on statistical methods relying on historical data of articles purchased and returned to the store. Such approaches suffer from the cold start problem for the thousands of articles appearing on the shopping platforms every day, for which no prior purchase history is available. We propose to employ visual data to infer size and fit characteristics of fashion articles. We introduce SizeNet, a weakly-supervised teacher-student training framework that leverages the power of statistical models combined with the rich visual information from article images to learn visual cues for size and fit characteristics, capable of tackling the challenging cold start problem. We demonstrate the strong advantage of our approach through extensive experiments performed on thousands of textile garments, including dresses, trousers, knitwear, tops, etc. from hundreds of different brands.
Towards Objection Detection Under IoT Resource Constraints

We consider deploying an object detection pipeline over a heterogeneous IoT network. We consider a setting where a camera-equipped IoT edge node communicates wirelessly with a cloud server. In many real-world domains, the bandwidth of this connection is constrained and variable, while the edge node may have insufficient compute resources to perform complex machine learning tasks such as object detection. Building on prior work in the image classification space, we propose an approach for detection that first partitions a deep neural network model at a given layer and then applies progressive transmission of intermediate convolutional filter maps. This capability can be exercised in response to dynamically varying network bandwidth. Further, we consider the application of lossy compression to the filter maps themselves, exposing a broader set of communication compression trade-offs that includes a choice for the representation size transmitted as well as the lossy compression level applied. The model is trained specifically to optimize distributed operation including the need for the cloud stage to decode and process a variable size representation. We investigate the performance of this approach in terms of a detection accuracy-communication cost trade-off. We compare to approaches that compress images and perform detection using cloud offload. Our results show that our approach achieves a significantly better detection accuracy-communication cost trade-off compared to cloud offload of JPEG compressed images.
Baracca: a Multimodal Dataset for Anthropometric Measurements in Automotive

The recent spread of depth sensors has enabled new methods to automatically estimate anthropometric measurements, in place of manual procedures or expensive 3D scanners. Generally, the use of depth data is limited by the lack of depth-based public datasets containing accurate anthropometric annotations. Therefore, in this paper we propose a new dataset, called Baracca, specifically designed for the automotive context, including in-car and outside views. The dataset is multimodal: it has been acquired with synchronized depth, infrared, thermal and RGB cameras in order to deal with the requirements imposed by the automotive context. In addition, we propose several baselines to test the challenges of the presented dataset and provide considerations for future work.