Reviewerly/exHarmony huggingface dataset:
Phase unwrapping based on accumulation of residual maps with local denoising

Phase unwrapping aims to reconstruct the absolute phase from its values mod 2π, which is a key problem in many non-contact optical metrologies and is still a challenging issue in the presence of noise. We present a robust phase unwrapping method with two steps based on local denoising and the accumulation of residual map (ARM). In the first step, based on the local polynomial approximations and intersection of confidence intervals, we adopt a sine–cosine filtering and iteration algorithm to denoise the wrapped phase, which uses the periodicity of the trigonometric function to avoid the influence of phase jumps effectively. In the second step, the denoised wrapped phase is unwrapped by the improved ARMs method, in which we modify the edge regular term to avoid over-fitting and maintain edge information. The algorithm is tested with a set of numerical simulations under various noise levels, and the experimental results confirm its effectiveness and robustness.
Toolset for Run-Time Dataset Collection of Deep-Scene Information

Virtual reality (VR) provides many exciting new application opportunities, but also present new challenges. In contrast to 360 $$^{\circ }$$ videos that only allow a user to select its viewing direction, in fully immersive VR, users can also move around and interact with objects in the virtual world. To most effectively deliver such services it is therefore important to understand how users move around in relation to such objects. In this paper, we present a methodology and software tool for generating run-time datasets capturing a user’s interactions with such 3D environments, evaluate and compare different object identification methods that we implement within the tool, and use datasets collected with the tool to demonstrate example uses. The tool was developed in Unity, easily integrates with existing Unity applications through the use of periodic calls that extracts information about the environment using different ray-casting methods. The software tool and example datasets are made available with this paper.
A Matrix Factorization Based Recommendation Algorithm for Science and Technology Resource Exploitation

The past decade witnesses a great progress of personalized recommendation systems, which extract potential characteristics of users and items based on users' ratings information on items, and then recommend other items that may be of interest to users. However, traditional recommendation algorithms ignore the users social relationships and suffer with the cold start problem. In this paper, we propose a recommendation algorithm based on similarity calculation matrix factorization (SCMF) with the consideration of users social relationships and items associated relationships when ratings information is available. At the same time, when ratings information is unavailable, the proposed algorithm can obtain potential feature relationships and makes personalized recommendations. We also introduce a penalty factor of positive items to the similarity calculation equation in case of over fitting. By this way, the SCFM algorithm can improve the prediction accuracy and alleviate the problem of cold start of sparse data. We compared our approach with other 4 algorithms and conducted experiments on 4 common used data sets. Regarding the experimental results, the recommendation precision of SCMF can be improved by 2% to 9% compared with the traditional best algorithm, and by 0.17% to 18% for cold start users. Additionally, our approaches were applied to patent data resource exploitation, provided by Wanfang patent retrieval system. The experimental results also verify the effectiveness of SCMF algorithm in improving recommendation performance.
An Improved One Point Quadrature Formula

Sweldens and Piessens [23 Sweldens, W., Piessens, R. (1992). Calculation of the wavelet decomposition using quadrature formulae. CWI Quarterly. 5(1):33–52. [Google Scholar]] gave the crucial relationship between the first and the second scaling moment in order to derive an effective one-point quadrature formula to reproduce polynomials up to degree 2. Finěk [6 Finěk, V. (2005). Quadrature formulas based on the scaling function. Appl. Math. 50(4):387–399. DOI: 10.1007/s10492-005-0029-8.[Crossref] , [Google Scholar]] derived quadrature formulas with exactness for polynomials of degree 3 with the use of scaling functions of the Daubechies wavelets. In this paper, some relations between the moments of the scaling function associated with multiresolution analysis are derived and a one point quadrature formula with degree of precision 4 is constructed which helps in the approximation of a function f∈L2(R) in Vj,j∈Z.
HyperDynamics: Generating Expert Dynamics Models by Observation

We propose HyperDynamics, a framework that conditions on an agent’s interactions with the environment and optionally its visual observations, and generates the parameters of neural dynamics models based on inferred properties of the dynamical system. Physical and visual properties of the environment that are not part of the low-dimensional state yet affect its temporal dynamics are inferred from the interaction history and visual observations, and are implicitly captured in the generated parameters. We test HyperDynamics on a set of object pushing and locomotion tasks. It outperforms existing dynamics models in the literature that adapt to environment variations by learning dynamics over high dimensional visual observations, capturing the interactions of the agent in recurrent state representations, or using gradient-based meta-optimization. We also show our method matches the performance of an ensemble of separately trained experts, while also being able to generalize well to unseen environment variations at test time. We attribute its good performance to the multiplicative interactions between the inferred system properties—captured in the generated parameters—and the low-dimensional state representation of the dynamical system.
Manipulating Time Experience

Symbolic InteractionVolume 44, Issue 3 p. 689-692 BOOK REVIEW Manipulating Time Experience Filip Vostal, Filip Vostal orcid.org/0000-0003-4628-9180 Institute of Philosophy of the Czech Academy of Sciences, Praha, Czech RepublicSearch for more papers by this author Filip Vostal, Filip Vostal orcid.org/0000-0003-4628-9180 Institute of Philosophy of the Czech Academy of Sciences, Praha, Czech RepublicSearch for more papers by this author First published: 28 January 2021 https://doi.org/10.1002/symb.543Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume44, Issue3August 2021Pages 689-692 RelatedInformation
Representation of model for efficient collision detection in virtual reality environment

In conceptual design, virtual reality assurances to be a natural, cost-effective, and creative method. Representation of the model in the virtual reality environment is a significant step. In the virtual environment, while representing the model, it should behave realistically. Several techniques like implicit, tessellated, parametric representations are available for representing the models. In the present work, the model is created from data of point cloud using volumetric self-organising feature map algorithm. The virtual model generation from point cloud data helps in the secure exchange of database between commercially available computer-aided design (CAD) software. 3D B-splines have been selected to represent the virtual model. A three-dimensional virtual model has been generated from the data of point cloud. For efficient collision detection, surfaces have been also extracted from the three-dimensional model by considering the alternative values of u, v, w parameters as 0 and 1. Anyone of the six extracted surfaces can be used for collision detection. The results showed that the extraction of surfaces is beneficial for efficient collision detection.
CTC-based Compression for Direct Speech Translation

Previous studies demonstrated that a dynamic phone-informed compression of the input audio is beneficial for speech translation (ST). However, they required a dedicated model for phone recognition and did not test this solution for direct ST, in which a single model translates the input audio into the target language without intermediate representations. In this work, we propose the first method able to perform a dynamic compression of the input indirect ST models. In particular, we exploit the Connectionist Temporal Classification (CTC) to compress the input sequence according to its phonetic characteristics. Our experiments demonstrate that our solution brings a 1.3-1.5 BLEU improvement over a strong baseline on two language pairs (English-Italian and English-German), contextually reducing the memory footprint by more than 10%.
Virus Named Entity Recognition based on Pre-training Model

Virus plays an important role in the earth's ecosystem. It maintains the balance of the ecosystem. It infects host cells, causing damage or death to the host. Understanding the relationship between virus and host is the key to preventing viral diseases. There are a large number of proven relationships between viruses and hosts in the literature. Extracting the relationships between viruses and hosts in these literature and organizing them into a virus-host knowledge base is of great significance to medical and biological research. Virus named entity recognition (VNER) is the key prerequisite step of relationship extraction. The complexity of virus naming and classification makes the identification of virus named entities challenging. In this paper, we provide a labeled corpus for the task of VNER. Furthermore, we use different pre-training models to compare its performance on downstream virus entity recognition tasks. Finally, BioBERT_ BiLSTM_ CRF got best result on the task of VNER. The precision value, recall value and F1 value are 92.18%, 91.28% and 91.85%, respectively.
WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose.

We present an end-to-end head-pose estimation network designed to predict Euler angles through the full range head yaws from a single RGB image. Existing methods perform well for frontal views but few target head pose from all viewpoints. This has applications in autonomous driving and retail. Our network builds on multi-loss approaches with changes to loss functions and training strategies adapted to wide range estimation. Additionally, we extract ground truth labelings of anterior views from a current panoptic dataset for the first time. The resulting Wide Headpose Estimation Network (WHENet) is the first fine-grained modern method applicable to the full-range of head yaws (hence wide) yet also meets or beats state-of-the-art methods for frontal head pose estimation. Our network is compact and efficient for mobile devices and applications.
On the Performance Similarity Between Exponential Moving Average and Discrete Linear Kalman Filter

Raw signal from sensor is generally corrupted by noise and other uncertainties. To suppress the noise, a filtering mechanism is required. Exponential Moving Average filter (EMA) serves as a powerful yet exceptionally simple filter. However, selecting so-called Alpha parameter in EMA is not a straightforward task. Extremely large Alpha value lead to more noise in the signal and small Alpha value resulted in sluggish convergence to the true value. In many cases Alpha parameter is selected arbitrarily and then opted for the best performance, resulting in numerous trials and errors. This paper is aimed to present an insight to select Alpha parameter, that is by implementing Kalman Gain from Kalman Filter structure as Alpha parameter and simultaneously highlight the similarity between Discrete Linear Kalman filter and Exponential Moving Average filter through some mathematical manipulations. To demonstrate the filter performance, altitude data from BMP280 barometric sensor is filtered. The results show that the EMA with Kalman Gain is capable to converge to the true altitude value and for some reasons, EMA with Kalman Gain resembles Kalman Filter performance in this particular scenario.
Distributed Alternating Direction Multiplier Method Based on Optimized Topology and Nodes Selection Strategy

ADMM (The Alternating Direction Multiplier Method) has been widely used in large-scale distributed machine learning, however the classical distributed ADMM algorithm lacks of optimization for network characteristics, which makes the convergence speed not reach the optimal. In this paper, A distributed ADMM with optimized network topology and nodes selection strategy is proposed. The proposed algorithm improves the performance of distributed computing by optimizing the topology structure of the network and reasonably allocating the node identity to avoid the large delay edges in the ADMM iterations as much as possible. The experimental results shown that based on proposed algorithm the calculation time of ADMM is improved, while the accuracy is controlled within an acceptable range. Compared with the classical distributed ADMM algorithm, the convergence time of distributed ADMM algorithm can be reduced by about 60% to 70%. The results also shown that the proposed algorithm is more prominent in the scenario of high delay or dynamics networks.
Efficient self-adaptive access control for personal medical data in emergency setting

The notion of access control allows data owners to outsource their data to cloud server, while encouraging the sharing of data with legally authorised users. Note that the traditional access control techniques only allow authorised users to access the sharing data. However, it is intractable to obtain the required data when the data owner encounters some emergency circumstances, such as, medical first-aid. Recently, Yang et al. proposed a self-adaptive access control scheme, which can ensure secure data sharing in both normal and emergency medical scenarios. However, their construction needs to involve an emergency contact person. We argue that Yang et al.'s scheme suffers from two weaknesses: 1) it is vulnerable to single point of failure when the emergency contact person is offline; 2) the two cloud model brings extra computation and communication overhead. To overcome the above shortcomings, we present a new efficient self-adaptive medical data access control by integrating fuzzy identity-based encryption and convergent encryption. Specifically, our proposed construction can achieve patients' data access by their fingerprint in emergency setting. Furthermore, the proposed scheme supports cross-user data deduplication and improves the performance of system by convergent encryption. Experiment results show that our scheme has an advantage in efficiency.
Locally Adaptive Nearest Neighbors.

When training automated systems, it has been shown to be beneficial to adapt the representation of data by learning a problem-specific metric. This metric is global. We extend this idea and, for the widely used family of k nearest neighbors algorithms, develop a method that allows learning locally adaptive metrics. To demonstrate important aspects of how our approach works, we conduct a number of experiments on synthetic data sets, and we show its usefulness on real-world benchmark data sets.
Learning To Pay Attention To Mistakes

In convolutional neural network based medical image segmentation, the periphery of foreground regions representing malignant tissues may be disproportionately assigned as belonging to the background class of healthy tissues \cite{attenUnet}\cite{AttenUnet2018}\cite{InterSeg}\cite{UnetFrontNeuro}\cite{LearnActiveContour}. This leads to high false negative detection rates. In this paper, we propose a novel attention mechanism to directly address such high false negative rates, called Paying Attention to Mistakes. Our attention mechanism steers the models towards false positive identification, which counters the existing bias towards false negatives. The proposed mechanism has two complementary implementations: (a) "explicit" steering of the model to attend to a larger Effective Receptive Field on the foreground areas; (b) "implicit" steering towards false positives, by attending to a smaller Effective Receptive Field on the background areas. We validated our methods on three tasks: 1) binary dense prediction between vehicles and the background using CityScapes; 2) Enhanced Tumour Core segmentation with multi-modal MRI scans in BRATS2018; 3) segmenting stroke lesions using ultrasound images in ISLES2018. We compared our methods with state-of-the-art attention mechanisms in medical imaging, including self-attention, spatial-attention and spatial-channel mixed attention. Across all of the three different tasks, our models consistently outperform the baseline models in Intersection over Union (IoU) and/or Hausdorff Distance (HD). For instance, in the second task, the "explicit" implementation of our mechanism reduces the HD of the best baseline by more than $26\%$, whilst improving the IoU by more than $3\%$. We believe our proposed attention mechanism can benefit a wide range of medical and computer vision tasks, which suffer from over-detection of background.
Critical Thinking

In modern socio-economic conditions, one of the priority areas of higher education is the training of a competent specialist with a high culture of thinking, capable of independently making respons...
Network Traffic Classification Using WiFi Sensing

With the ubiquity of WiFi-enabled devices, WiFi Channel State Information (CSI) based sensing of the physical environment has been researched broadly, and for network management and monitoring, advanced measures for Network Traffic Classification (NTC) have been called. This paper proposes a novel CSI-based NTC model using off-the-shelf WiFi sensing tools. We conducted experiments in both controlled environment and real-world environment. Experiment results have shown that the frequency-selective CSI signatures can be used to distinguish four common NTC classes: ping, music streaming, buffered video streaming, and live video streaming. CSI features for NTC include the number of prominent CSI amplitude bins, locations of bins and relevant prominence of bins on the amplitude histogram over time for different subcarriers. We conclude with a clear WiFi sensing-based distinction of different network types where it is observed that ping and music streaming have similarities in their features, while buffered and live video streaming resemble each other in their CSI amplitude features.
Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation

Scene graph generation (SGG) aims to predict graph-structured descriptions of input images, in the form of objects and relationships between them. This task is becoming increasingly useful for progress at the interface of vision and language. Here, it is important - yet challenging - to perform well on novel (zero-shot) or rare (few-shot) compositions of objects and relationships. In this paper, we identify two key issues that limit such generalization. Firstly, we show that the standard loss used in this task is unintentionally a function of scene graph density. This leads to the neglect of individual edges in large sparse graphs during training, even though these contain diverse few-shot examples that are important for generalization. Secondly, the frequency of relationships can create a strong bias in this task, such that a blind model predicting the most frequent relationship achieves good performance. Consequently, some state-of-the-art models exploit this bias to improve results. We show that such models can suffer the most in their ability to generalize to rare compositions, evaluating two different models on the Visual Genome dataset and its more recent, improved version, GQA. To address these issues, we introduce a density-normalized edge loss, which provides more than a two-fold improvement in certain generalization metrics. Compared to other works in this direction, our enhancements require only a few lines of code and no added computational cost. We also highlight the difficulty of accurately evaluating models using existing metrics, especially on zero/few shots, and introduce a novel weighted metric.
An Efficient Framework for a Third Party Auditor in Cloud Computing Environments

Abstract The paper ‘An Efficient Framework for a Third Party Auditor in Cloud Computing Environments’, by S. Mahdavi-Hezavehi, Y. Alimardani and R. Rahmani, published in The Computer Journal, Volume 63, Issue 9, September 2020, explores a new framework for significant cost savings in the cloud.
VBNC: Voting Based Noise Classification Framework Using Deep CNN

In this revolutionary digital world, the role of images and videos for transmitting the information is much more than the text. Now, people prefer short, concise and informative data over the large volume of texts like the information in the form of images is preferable. Noise availability in an image is a major challenge in digital image processing and this presence of noise misleads the results during various computer vision operations. The image denoise step is must require in this case. Multiple noise reduction algorithms are developed and used till now, but mostly the work is done with the known information of noise. In this research work, the author(s) focused on noise type identification in an image that will helpful for the images having unknown information about the type of noise is present. Further, it will help to select the appropriate algorithm for noise reduction purposes. For this purpose, the author(s) proposed a voting based noise classification framework that utilized deep learning technology.
Hybrid combination of Convolutional Neural Network Features for Robust Writer Retrieval of Historical Documents

The present work focuses on combining Convolutional Neural Network (CNN) features to strengthen the writer retrieval in historical document databases. Various CNN models that are LeNet, ResNet, and VGG are used to get writer-independent features of handwritten documents. These features are associated with dissimilarity measures to achieve the retrieval task. Then, developed writer retrieval systems are combined through SVM classifier. Experiments are conducted on ICDAR-2017 dataset which contains historical handwritten documents. The results obtained highlight the robustness of CNN features and the combination stage as well.
Chest X-ray Lung Chinese Description Generation based on Semantic Labels and Hierarchical LSTM

The automatic generation of chest X-ray report is a hot research topic at present. Considering the lack of research on Chinese report generation, we propose a method suitable for lung description in Chinese reports-a model that combines semantic labels and hierarchical LSTM. The model analyzes the anomaly report, extracts high-frequency keywords as semantic labels, and adds the abnormal binary classification module in the encoder to correct the results of the semantic labels for the templated characteristics of the Chinese report. In the design of the decoder, to address the problem of lack of correlation between semantic Labels, a two-layer LSTM model that fuses semantic tags and image features is proposed. The comparison with the baseline experiment shows that the proposed model can effectively improve the quality of report generation.