Reviewerly/exHarmony huggingface dataset:
Monocular vision-based low-frequency vibration calibration method with correction of the guideway bending in a long-stroke shaker

Calibration is required to maximize the sensitivity and measurement accuracy of vibration sensors. In this study, a low-frequency vibration calibration method is proposed that is based on the concept of monocular vision. In this method, we employ a high-accuracy edge extraction method to extract the edges of sequential images so as to obtain the high calibration accuracy. However, the proposed method must rely on a long-stroke shaker to provide vibration excitation to the sensor, and the bending in the guideway caused by the mechanical processing reduces the calibration accuracy, especially at very low frequencies. The proposed setting compensates for the bending using an additional monocular vision technique to significantly improve the calibration accuracy. To validate the calibration accuracy of the proposed method, a comparison was conducted between results obtained via the laser interferometry, the Earth's gravitation method, and the proposed method when applied to calibrate the sensitivity of a tri-axial acceleration sensor at frequencies between 0.04 and 8 Hz. The results of the comparison showed the proposed method calibrated the sensor sensitivity with high accuracy and was able to accurately account for the bending when the frequency was lower than 0.3 Hz. In contrast, the calibration accuracy of the laser interferometry decreased because of the bending.
Topological correction of infant white matter surfaces using anatomically constrained convolutional neural network

Reconstruction of accurate cortical surfaces without topological errors (i.e., handles and holes) from infant brain MR images is very important in early brain development studies. However, infant brain MR images typically suffer extremely low tissue contrast and dynamic imaging appearance patterns. Thus, it is inevitable to have large amounts of topological errors in the segmented infant brain tissue images, which lead to inaccurately reconstructed cortical surfaces with topological errors. To address this issue, inspired by recent advances in deep learning, we propose an anatomically constrained network for topological correction on infant cortical surfaces. Specifically, in our method, we first locate regions of potential topological defects by leveraging a topology-preserving level set method. Then, we propose an anatomically constrained network to correct those candidate voxels in the located regions. Since infant cortical surfaces often contain large and complex handles or holes, it is difficult to completely correct all errors using one-shot correction. Therefore, we further enroll these two steps into an iterative framework to gradually correct large topological errors. To the best of our knowledge, this is the first work to introduce deep learning approach for topological correction of infant cortical surfaces. We compare our method with the state-of-the-art methods on both simulated topological errors and real topological errors in human infant brain MR images. Moreover, we also validate our method on the infant brain MR images of macaques. All experimental results show the superior performance of the proposed method.
Toward Intelligent Surveillance as an Edge Network Service (iSENSE) Using Lightweight Detection and Tracking Algorithms

Edge computing extends the realm of information technology beyond the boundaries defined by cloud computing. Performing computation near the sensors, edge computing is promising to address the challenges in many bandwidth-and delay-sensitive applications. Although recently many smart video surveillance approaches based on Machine Learning (ML) algorithms become available, it is still challenging to efficiently migrate those smart algorithms to edge. In this paper, we propose an intelligent Surveillance as an Edge Network Service (iSENSE), which explores the feasibility of moving ML to the edge by testing two popular human-object detection schemes. Besides, a lightweight Convolutional Neural Network (L-CNN) is introduced to improve computational execution by leveraging the depth-wise separable convolution. To enhance performance on edge, we propose a hybrid lightweight tracking algorithm, Kerman (Kernelized Kalman filter), which is a decision tree based hybrid Kernelized Correlation Filter algorithm designed for human-object tracking. We have implemented both Kerman and L-CNN algorithms on edge by using different types of single board computers. The proposed iSENSE system was validated using both real-world campus surveillance video and open image sets. The experimental results present that the proposed algorithms can track the human objects in real-time with a good accuracy with limited resource in edge devices.
A novel mirror-assisted multi-view digital image correlation for dual-surface shape and deformation measurements of sheet samples

We propose a novel mirror-assisted multi-view digital image correlation (DIC) method for dual-surface 3D shape and deformation measurements using only a binocular stereo-DIC system and two orthogonal planar mirrors. Through the reflection of the mirrors, virtual surfaces of front and rear surfaces of sheet specimens formed behind the mirrors are simultaneously captured and reconstructed by regular two-camera stereo-DIC. By taking advantage of the speckle patterns premade on the mirrors, reflection transformations of both mirrors are retrieved and used to transform the reconstructed virtual surfaces to their real positions in front of the mirrors. The 3D shapes of both surfaces at each state enable the measurements of full-field 3D deformation on both surfaces and at the third direction (i.e., through-thickness direction). Compared with existing multi-camera DIC methods, the proposed method offers several distinct advantages, such as simple setup, low cost and easy implementation. Real experiments, including dual-surface reconstruction of a coin, in-plane and out-of-plane translation of a plate specimen and full-field deformation measurement of a planar aluminum specimen subjected to uniaxial tensile loading, validated the effectiveness and accuracy of the proposed method. The proposed method opens a new low-cost and convenient avenue for strain measurement in thickness direction, demonstrating great potential in material testing of anisotropic and/or ductile materials.
Time Series Classification using Fuzzy Cognitive Maps

This paper presents a time-series classification method based on fuzzy cognitive maps. We advocate that fuzzy cognitive maps provide a sound representation of time series, and we can construct a classification mechanism based on them. The classifier has to distinguish maps constructed for time series belonging to different classes. The proposed classification procedure evaluates similarity of fuzzy cognitive maps, and it is done by comparing weight matrices based on the same set of concepts. A weight matrix describes relationships between concepts in a map. Concepts represent the underlying data, because they are extracted via a data-driven clustering procedure. Each data point of a time series is related to each concept, and we evaluate the strength of relationships with a membership function. This paper investigates performance of the proposed approach on a suite of real-world datasets. We compare classification accuracy of our method with 37 state-of-the-art time-series classification methods. Experiments show that the proposed method is performing well. In many cases, it is better than its competitors.
Solving NP-Hard Problems on Graphs by Reinforcement Learning without Domain Knowledge.

There have been increasing challenges to solve combinatorial optimization problems by machine learning. Khalil et al. proposed an end-to-end reinforcement learning framework, S2V-DQN, which automatically learns graph embeddings to construct solutions to a wide range of problems. To improve the generalization ability of their Q-learning method, we propose a novel learning strategy based on AlphaGo Zero which is a Go engine that achieved a superhuman level without the domain knowledge of the game. Our framework is redesigned for combinatorial problems, where the final reward might take any real number instead of a binary response, win/lose. In experiments conducted for five kinds of NP-hard problems including {\sc MinimumVertexCover} and {\sc MaxCut}, our method is shown to generalize better to various graphs than S2V-DQN. Furthermore, our method can be combined with recently-developed graph neural network (GNN) models such as the \emph{Graph Isomorphism Network}, resulting in even better performance. This experiment also gives an interesting insight into a suitable choice of GNN models for each task.
Humanoid Localization on Robocup Field using Corner Intersection and Geometric Distance Estimation

Corner detection is the most used feature in the object recognition process, as evidenced by the range of corner detection techniques available for various applications [2][7].Recently, various corner
Introduction to Machine Learning and Its Basic Application in Python

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Morellian Analysis for Browsers: Making Web Authentication Stronger with Canvas Fingerprinting

In this paper, we present the first fingerprinting-based authentication scheme that is not vulnerable to trivial replay attacks. Our proposed canvas-based fingerprinting technique utilizes one key characteristic: it is parameterized by a challenge, generated on the server side. We perform an in-depth analysis of all parameters that can be used to generate canvas challenges, and we show that it is possible to generate unique, unpredictable, and highly diverse canvas-generated images each time a user logs onto a service. With the analysis of images collected from more than 1.1 million devices in a real-world large-scale experiment, we evaluate our proposed scheme against a large set of attack scenarios and conclude that canvas fingerprinting is a suitable mechanism for stronger authentication on the web.
Rich QR Codes With Three-Layer Information Using Hamming Code

As a kind of machine readable symbol, the Quick Response (QR) code is widely used in all walks of life due to its large information capacity, strong error correction ability and fast read speed. However, the public encoding of the QR code makes its contents unsafe. In this paper, we introduce a novel rich QR code with three-layer information that utilizes the characteristics of the Hamming code and the error correction mechanism of the QR code to protect the secret information. The first layer information, that is, the public information, can be decoded by any standard QR reader. The XOR operation is performed on all shares to obtain the second layer information. Finally, the pixels of the same position in all shares are taken as a set of Hamming codes, respectively, and the third layer information is extracted by a matrix multiplication operation. Compared to other related schemes, the proposed scheme has the advantage of high information embedding efficiency and strong robustness against common image post-processing attacks.
Embedding Time Expressions for Deep Temporal Ordering Models

Data-driven models have demonstrated stateof-the-art performance in inferring the temporal ordering of events in text.However, these models often overlook explicit temporal signals, such as dates and time windows.Rule-based methods can be used to identify the temporal links between these time expressions (timexes), but they fail to capture timexes' interactions with events and are hard to integrate with the distributed representations of neural net models.In this paper, we introduce a framework to infuse temporal awareness into such models by learning a pre-trained model to embed timexes.We generate synthetic data consisting of pairs of timexes, then train a character LSTM to learn embeddings and classify the timexes' temporal relation.We evaluate the utility of these embeddings in the context of a strong neural model for event temporal ordering, and show a small increase in performance on the MATRES dataset and more substantial gains on an automatically collected dataset with more frequent event-timex interactions. 1
Bridging by Word: Image Grounded Vocabulary Construction for Visual Captioning

Existing research for visual captioning usually employs a CNN-RNN architecture that combines a CNN for image encoding with a RNN for caption generation, where the vocabulary is constructed from the entire training dataset as the decoding space.Such approaches typically suffer from the problem of generating N-grams which occur frequently in the training set but are irrelevant to the given image.To tackle this problem, we propose to construct an image-grounded vocabulary that leverages image semantics for more effective caption generation.More concretely, a two-step approach is proposed to construct the vocabulary by incorporating both visual information and relationships among words.Two strategies are then explored to utilize the constructed vocabulary for caption generation.One constrains the generator to select words from the image-grounded vocabulary only and the other integrates the vocabulary information into the RNN cell during the caption generation process.Experimental results on two public datasets show the effectiveness of our framework compared to state-of-the-art models.Our code is available on Github 1 .
Bayes Test of Precision, Recall, and F1 Measure for Comparison of Two Natural Language Processing Models

Direct comparison on point estimation of the precision (P), recall (R), and F1 measure of two natural language processing (NLP) models on a common test corpus is unreasonable and results in less replicable conclusions due to a lack of a statistical test. However, the existing t-tests in cross-validation (CV) for model comparison are inappropriate because the distributions of P, R, F1 are skewed and an interval estimation of P, R, and F1 based on a t-test may exceed [0,1]. In this study, we propose to use a block-regularized 3×2 CV (3×2 BCV) in model comparison because it could regularize the difference in certain frequency distributions over linguistic units between training and validation sets and yield stable estimators of P, R, and F1. On the basis of the 3×2 BCV, we calibrate the posterior distributions of P, R, and F1 and derive an accurate interval estimation of P, R, and F1. Furthermore, we formulate the comparison into a hypothesis testing problem and propose a novel Bayes test. The test could directly compute the probabilities of the hypotheses on the basis of the posterior distributions and provide more informative decisions than the existing significance t-tests. Three experiments with regard to NLP chunking tasks are conducted, and the results illustrate the validity of the Bayes test.
Seeded Graph Matching via Large Neighborhood Statistics

We study a well known noisy model of the graph isomorphism problem. In this model, the goal is to perfectly recover the vertex correspondence between two edge-correlated graphs, with an initial seed set of correctly matched vertex pairs revealed as side information. Specifically, the model first generates a parent graph G0 from Erdős-Rényi random graph G(n, p) and then obtains two children graphs G1 and G2 by subsampling the edge set of G0 twice independently with probability s = Θ(1). The vertex correspondence between G1 and G2 is obscured by randomly permuting the vertex labels of G1 according to a latent permutation π*. Finally, for each i, π* (i) is revealed independently with probability α as seeds.In the sparse graph regime where np ≤ n∊ for any ∊ < 1/6, we give a polynomial-time algorithm which perfectly recovers π*, provided that nps2 – log n → +∞ and α ≥ n−1+3∊. This further leads to a subexponential-time, exp (nO(∊)), matching algorithm even without seeds. On the contrary, if nps2 – log n = O(1), then perfect recovery is information-theoretically impossible as long as α is bounded away from 1.In the dense graph regime, where np = bna, for fixed constants a, b ∊ (0, 1], we give a polynomial-time algorithm which succeeds when b = O(s) and a = Ω ((np)−[1/α] log n). In particular, when a = 1/k for an integer k ≥ 1, α = Ω(log n/n) suffices, yielding a quasi-polynomial-time nO(log n) algorithm matching the best known algorithm by Barak et al. for the problem of graph matching without seeds when k ≥ 153 and extending their result to new values of p for k = 2, …, 152.Unlike previous work on graph matching, which used small neighborhoods or small subgraphs with a logarithmic number of vertices in order to match vertices, our algorithms match vertices if their large neighborhoods have a significant overlap in the number of seeds.
Davydov-Ansatz for Landau-Zener-Stueckelberg-Majorana transitions in an environment: Tuning the survival probability via number state excitation

We theoretically investigate transitions in a two-level system, which are induced by a sweep through an avoided crossing in the presence of coupling to a single, excited bosonic mode. This allows us to propose an initial number-state bosonic excitation as a new possible control parameter for the survival probability at long times. The expansion of number states in terms of coherent states centered around points on a circle in phase space makes a multi-Davydov-Ansatz the method of choice to perform the required numerical calculations. It is revealed that the starting time of the transition greatly affects the final transition probabilities. In addition, we found that the mixing angle, which is tuning between the diagonal and off-diagonal coupling, is decisive for the ability to control the transition via number state excitation. For a mixing angle of π/4, we found the maximal effect of number state excitation on the transition probability.
E3: Entailment-driven Extracting and Editing for Conversational Machine Reading

Conversational machine reading systems help users answer high-level questions (e.g.determine if they qualify for particular government benefits) when they do not know the exact rules by which the determination is made (e.g.whether they need certain income levels or veteran status).The key challenge is that these rules are only provided in the form of a procedural text (e.g.guidelines from government website) which the system must read to figure out what to ask the user.We present a new conversational machine reading model that jointly extracts a set of decision rules from the procedural text while reasoning about which are entailed by the conversational history and which still need to be edited to create questions for the user.On the recently introduced ShARC conversational machine reading dataset, our Entailment-driven Extract and Edit network (E 3 ) achieves a new state-of-theart, outperforming existing systems as well as a new BERT-based baseline.In addition, by explicitly highlighting which information still needs to be gathered, E 3 provides a more explainable alternative to prior work.We release source code for our models and experiments at https://github.com/vzhong/e3.
Replay attack detection with auditory filter-based relative phase features

There are many studies on detecting human speech from artificially generated speech and automatic speaker verification (ASV) that aim to detect and identify whether the given speech belongs to a given speaker. Recent studies demonstrate the success of the relative phase (RP) feature in speaker recognition/verification and the detection of synthesized speech and converted speech. However, there are few studies that focus on the RP feature for replay attack detection. In this paper, we improve the discriminating ability of the RP feature by proposing two new auditory filter-based RP features for replay attack detection. The key idea is to integrate the advantage of RP-based features in signal representation with the advantage of two auditory filter-based RP features. For the first proposed feature, we apply a Mel-filter bank to convert the signal representation of conventional RP information from a linear scale to a Mel scale, where the modified representation is called the Mel-scale RP feature. For the other proposed feature, a gammatone filter bank is applied to scale the RP information, where the scaled RP feature is called the gammatone-scale RP feature. These two proposed phase-based features are implemented to achieve better performance than a conventional RP feature because of the scale resolution and. In addition to the use of individual Mel/gammatone-scale RP features, a combination of the scores of these proposed RP features and a standard magnitude-based feature, that is, the constant Q transform cepstral coefficient (CQCC), is also applied to further improve the reliable detection decision. The effectiveness of the proposed Mel-scale RP feature, gammatone-scale RP feature, and their combination are evaluated using the ASVspoof 2017 dataset. On the evaluation dataset, our proposed methods demonstrate significant improvement over the existing feature and baseline CQCC feature. The combination of the CQCC and gammatone-scale RP provides the best performance compared with an individual baseline feature and other combination methods.
Sentence-Level Agreement for Neural Machine Translation

The training objective of neural machine translation (NMT) is to minimize the loss between the words in the translated sentences and those in the references. In NMT, there is a natural correspondence between the source sentence and the target sentence. However, this relationship has only been represented using the entire neural network and the training objective is computed in word-level. In this paper, we propose a sentence-level agreement module to directly minimize the difference between the representation of source and target sentence. The proposed agreement module can be integrated into NMT as an additional training objective function and can also be used to enhance the representation of the source sentences. Empirical results on the NIST Chinese-to-English and WMT English-to-German tasks show the proposed agreement module can significantly improve the NMT performance.
MIRO360: A Tool for Subjective Assessment of 360 Degree Video for ITU-T P.360-VR

This paper describes MIRO360, a tool which implements the diverse set of methodologies that are being evaluated by ITU-T Study Group 12 and the Video Quality Experts Group in the development of the future ITU-T Recommendation P.360-VR for the subjective assessment of 360 video with Head Mounted Displays (HMDs). MIRO360 is an application for Android-based HMDs, with a flexible configuration that allows the simple creation of tests for short sequences (either single or double reference), as well as for long ones, including in-sequence continuous or discrete evaluation, as well as simulator sickness and spatial presence.