Reviewerly/exHarmony huggingface dataset:
Video Copy Detection Using Spatio-Temporal CNN Features

To protect the copyright of digital videos, video copy detection has become a hot topic in the field of digital copyright protection. Since a video sequence generally contains a large amount of data, to achieve efficient and effective copy detection, the key issue is to extract compact and discriminative video features. To this end, we propose a video copy detection scheme using spatio-temporal convolutional neural network (CNN) features. First, we divide each video sequence into multiple video clips and sample the frames of each video clip. Second, the sampled frames of each video clip are fed into a pre-trained CNN model to generate the corresponding convolutional feature maps (CFMs). Third, based on the generated CFMs, we extract the CNN features on the spatial and temporal domains of each video clip, i.e., the spatio-temporal CNN features. Finally, video copy detection is efficiently and effectively implemented based on the extracted spatio-temporal CNN features. The experiments on the commonly used video dataset, i.e., TRECVID 2008, demonstrate that the proposed method performs well in aspects of both accuracy and efficiency and shows superiority to several other copy detection methods using the state-of-the-art features.
A Review of Quantum Computer Energy Efficiency

Energy efficiency is crucial to protecting our planet from pollution and global warming disasters. Quantum computers offer a great opportunity for energy saving and fast computations. However, designing quantum computers faces numerous challenges. This paper presents a concise overview of quantum computer energy efficiency, and challenges.
Remarks on fixed point assertions in digital topology, 4

We continue the work of [4, 2, 3], in which we discuss published assertions that are incorrect or incorrectly proven; that are severely limited or reduce to triviality; or that we improve upon.
Automatic Graphics Program Generation Using Attention-Based Hierarchical Decoder

Recent progress on deep learning has made it possible to automatically transform the screenshot of Graphic User Interface (GUI) into code by using the encoder-decoder framework. While the commonly adopted image encoder (e.g., CNN network), might be capable of extracting image features to the desired level, interpreting these abstract image features into hundreds of tokens of code puts a particular challenge on the decoding power of the RNN-based code generator. Considering the code used for describing GUI is usually hierarchically structured, we propose a new attention-based hierarchical code generation model, which can describe GUI images in a finer level of details, while also being able to generate hierarchically structured code in consistency with the hierarchical layout of the graphic elements in the GUI. Our model follows the encoder-decoder framework, all the components of which can be trained jointly in an end-to-end manner. The experimental results show that our method outperforms other current state-of-the-art methods on both a publicly available GUI-code dataset as well as a dataset established by our own.
Pyramid U-Network for Skeleton Extraction From Shape Points

The knowledge about the skeleton of a given geometric shape has many practical applications such as shape animation, shape comparison, shape recognition, and estimating structural strength. Skeleton extraction becomes a more challenging problem when the topology is represented in point cloud domain. In this paper, we present the network architecture, PSPU-SkelNet, for TeamPH which ranked 3rd in Point SkelNetOn 2019 challenge. PSPU-SkelNet is a pyramid of three U-Nets that predicts the skeleton from a given shape point cloud. PSPU-SkelNet achieves a Chamfer Distance (CD) of 2.9105 on the final test dataset. The code of PSPU SkelNet is available at https://github.com/roatienza/skelnet.
A Rank-1 Sketch for Matrix Multiplicative Weights

We show that a simple randomized sketch of the matrix multiplicative weight (MMW) update enjoys (in expectation) the same regret bounds as MMW, up to a small constant factor. Unlike MMW, where every step requires full matrix exponentiation, our steps require only a single product of the form $e^A b$, which the Lanczos method approximates efficiently. Our key technique is to view the sketch as a $\textit{randomized mirror projection}$, and perform mirror descent analysis on the $\textit{expected projection}$. Our sketch solves the online eigenvector problem, improving the best known complexity bounds by $\Omega(\log^5 n)$. We also apply this sketch to semidefinite programming in saddle-point form, yielding a simple primal-dual scheme with guarantees matching the best in the literature.
Rigid Medical Image Registration Using Learning-Based Interest Points and Features

For image-guided radiation therapy, radiosurgery, minimally invasive surgery, endoscopy and interventional radiology, one of the important techniques is medical image registration. In our study, we propose a learning-based approach named “FIP-CNNF” for rigid registration of medical image. Firstly, the pixel-level interest points are computed by the full convolution network (FCN) with self-supervise. Secondly, feature detection, descriptor and matching are trained by convolution neural network (CNN). Thirdly, random sample consensus (Ransac) is used to filter outliers, and the transformation parameters are found with the most inliers by iteratively fitting transforms. In addition, we propose “TrFIP-CNNF” which uses transfer learning and fine-tuning to boost performance of FIP-CNNF. The experiment is done with the dataset of nasopharyngeal carcinoma which is collected from West China Hospital. For the CT-CT and MR-MR image registration, TrFIP-CNNF performs better than scale invariant feature transform (SIFT) and FIP-CNNF slightly. For the CT-MR image registration, the precision, recall and target registration error (TRE) of the TrFIP-CNNF are much better than those of SIFT and FIP-CNNF, and even several times better than those of SIFT. The promising results are achieved by TrFIP-CNNF especially in the multimodal medical image registration, which demonstrates that a feasible approach can be built to improve image registration by using FCN interest points and CNN features.
Hill Climbing on Value Estimates for Search-control in Dyna

Dyna is an architecture for model based reinforcement learning (RL), where simulated experience from a model is used to update policies or value functions. A key component of Dyna is search control, the mechanism to generate the state and action from which the agent queries the model, which remains largely unexplored. In this work, we propose to generate such states by using the trajectory obtained from Hill Climbing (HC) the current estimate of the value function. This has the effect of propagating value from high value regions and of preemptively updating value estimates of the regions that the agent is likely to visit next. We derive a noisy projected natural gradient algorithm for hill climbing, and highlight a connection to Langevin dynamics. We provide an empirical demonstration on four classical domains that our algorithm, HC Dyna, can obtain significant sample efficiency improvements. We study the properties of different sampling distributions for search control, and find that there appears to be a benefit specifically from using the samples generated by climbing on current value estimates from low value to high value region.
Market Design for a Blockchain-Based Financial System

We develop a theory of long-run equilibrium in blockchain-based financial systems. Our theory elucidates the key market design features that separate proof of work and proof of stake approaches in the long run. Under proof of work, wasteful computation is used to secure the system, and users' utility in equilibrium is determined by the threat of a fork. Under proof of stake, by contrast, users' utility in equilibrium is generally above the fork threat level because custodians can use relational contracts to incentivize a higher quality of service. Relational contracts under proof of stake rely only on local institutions - but combining them with cryptography can create a platform for formal global contracts.
Preserving Command Line Workflow for a Package Management System Using ASCII DAG Visualization

Package managers provide ease of access to applications by removing the time-consuming and sometimes completely prohibitive barrier of successfully building, installing, and maintaining the software for a system. A package dependency contains dependencies between all packages required to build and run the target software. Package management system developers, package maintainers, and users may consult the dependency graph when a simple listing is insufficient for their analyses. However, users working in a remote command line environment must disrupt their workflow to visualize dependency graphs in graphical programs, possibly needing to move files between devices or incur forwarding lag. Such is the case for users of Spack, an open source package management system originally developed to ease the complex builds required by supercomputing environments. To preserve the command line workflow of Spack, we develop an interactive ASCII visualization for its dependency graphs. Through interviews with Spack maintainers, we identify user goals and corresponding visual tasks for dependency graphs. We evaluate the use of our visualization through a command line-centered study, comparing it to the system's two existing approaches. We observe that despite the limitations of the ASCII representation, our visualization is preferred by participants when approached from a command line interface workflow.
Low-Dose CT Image Denoising Model Based on Sparse Representation by Stationarily Classified Sub-Dictionaries

Low-dose computed tomography (LDCT) technique is an important imaging modality, but LDCT images are always severely degraded by mottle noise and streak artifacts.The recently proposed nonlocally centralized sparse representation (NCSR) algorithm has good performance in natural image denoising, but it suffers from residual streak artifacts and can't preserve edges structure information well when implemented in LDCT image denoising.In addition, it has high computational complexity.To address this problem, in this paper, we propose an improved model, i.e.SNCSR model, based on the stationary PCA sub-dictionaries, nonlocally centralized sparse representation and relative total variation.In the SNCSR model, in order to learn more accurate sub-dictionaries, the LDCT image is preprocessed by the improved total variation (ITV) model in which the weighted coefficient of the regularization term is constructed depending on a clipped and normalized local activity.In addition, the maximum eigenvalue of the gradient covariance matrix of the image patch is used to distinguish edge structure information from background region so that the restored image can be represented more sparsely.Moreover, unlike the NCSR model that needs to learn sub-dictionaries in each outer loop, the proposed model learns stationary sub-dictionaries only once before iteration starts, which shorten the computation time significantly.At last, the relative total variation (RTV) algorithm is applied to further reduce the residual artifacts in the recovered image more thoroughly.The experiments are performed on the simulated pelvis phantom, the actual thoracic phantom and the clinical abdominal data.Compared with several other competitive denoising algorithms, both subjective visual effect and objective evaluation criteria show that the proposed SNCSR model has lower computational complexity and can improve LDCT images quality more effectively.INDEX TERMS Low-dose computed tomography (LDCT) image denoising, sparse representation, stationary sub-dictionaries, the maximum eigenvalue of the gradient covariance matrix, total variation (TV), the clipped and normalized local activity.
BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters.

Hyperparameter optimization and neural architecture search can become prohibitively expensive for regular black-box Bayesian optimization because the training and evaluation of a single model can easily take several hours. To overcome this, we introduce a comprehensive tool suite for effective multi-fidelity Bayesian optimization and the analysis of its runs. The suite, written in Python, provides a simple way to specify complex design spaces, a robust and efficient combination of Bayesian optimization and HyperBand, and a comprehensive analysis of the optimization process and its outcomes.
HyperKG: Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion

Learning embeddings of entities and relations existing in knowledge bases allows the discovery of hidden patterns in data. In this work, we examine the geometrical space's contribution to the task of knowledge base completion. We focus on the family of translational models, whose performance has been lagging, and propose a model, dubbed HyperKG, which exploits the hyperbolic space in order to better reflect the topological properties of knowledge bases. We investigate the type of regularities that our model can capture and we show that it is a prominent candidate for effectively representing a subset of Datalog rules. We empirically show, using a variety of link prediction datasets, that hyperbolic space allows to narrow down significantly the performance gap between translational and bilinear models.
Integration of a Secure Physical Element as a Trusted Oracle in a Hyperledger Blockchain

Blockchain frameworks enable the immutable storage of data. A still open practical question is the so called “oracle” problem, i.e. the way how real world data is actually transferred into and out of a blockchain while preserving its integrity. We present a case study that demonstrates how to use an existing industrial strength secure element for cryptographic software protection (Wibu CmDongle / the “dongle”) to function as such a hardware-based oracle for the Hyperledger blockchain framework. Our scenario is that of a dentist having leased a 3D printer. This printer is initially supplied with an amount of x printing units. With each print action the local unit counter on the attached dongle is decreased and in parallel a unit counter is maintained in the Hyperledger-based blockchain. Once a threshold is met, the printer will stop working (by means of the cryptographically protected invocation of the local print method). The blockchain is configured in such a way that chaincode is executed to increase the units again automatically (and essentially trigger any payment processes). Once this has happened, the new unit counter value will be passed from the blockchain to the local dongle and thus allow for further execution of print jobs.
Classical capacity of generalized Pauli channels

Abstract Finding the classical capacity of a quantum channel is not easy, yet we are able to analytically calculate this capacity for new channels. We analyze the bounds of the Holevo capacity and classical capacity for the generalized Pauli channels. In particular, by generalizing earlier results for the Weyl channels, we obtain the lower and upper bounds of the Holevo capacity and show that, if these bounds coincide, the Holevo capacity is weakly additive. We also prove the weak additivity of the lower bound. Two new examples of the generalized Pauli channels with known classical capacity are presented. Finally, we relate the change rate of the classical capacity to the P-divisibility of Pauli channels.
Using fine-tuned conditional probabilities for data transformation of nominal attributes

Most of existing machine learning algorithms do not natively support nominal attributes, so it is essential to develop the data transformation of nominal attributes into high-quality numeric ones. Conditional Probability Transformation (CPT), using conditional probability terms to transform categories in nominal attributes, is competitive with state-of-the-art transformation methods such as One-Hot Encoding (OHE) and Separability Split Value Transformation (SSVT). However, it may be difficult to accurately estimate conditional probability terms when training data is insufficient or there exist strong dependencies among its attributes. Inspired by the fine-tuning method for improving conditional probability terms in distance measures, we proposed a Fine-Tuned Conditional Probability Transformation (FTCPT). In addition, we proposed an Improved SSV (ISSV) based on fine-tuned conditional probability terms, and used our Modified MIC-based Feature Selection method to further improve the performance of FTCPT. Experiment results show that the proposed methods can improve the quality of data transformation, thereby contribute to improving the classification performance of subsequent machine learning algorithm.
TPACK: Technological, Pedagogical and Content Model Necessary to Improve the Educational Process on Mathematics through a Web Application?

This quantitative research aims to analyze the design and implementation of the Web Application on the educational process of the Linear Function (WALF) considering the TPACK (Technological Pedagogical and Content Knowledge) model and data science. The sample consists of 45 students who studied the Basic Math course at a Mexican university during the 2015 school year. The TPACK model allows the planning and organization of WALF through technological knowledge (HTML and PHP languages), content knowledge (formulas on the linear function and slope) and pedagogical knowledge (data simulation). The results of machine learning (linear regression) with 50%, 60% and 70% of training indicate that the contents of WALF influence the assimilation of knowledge about the identification and evaluation of the linear function. Data science identifies 2 predictive models on the use of WALF in the field of mathematics by means of the decision tree technique. Finally, the TPACK model facilitates the implementation of technological tools and construction of educational virtual spaces through technological, content and pedagogical knowledge.
Domain Impersonation is Feasible: A Study of CA Domain Validation Vulnerabilities

Web security relies on the assumption that certificate authorities (CAs) issue certificates to rightful domain owners only.However, we show that CAs expose vulnerabilities which allow an attacker to obtain certificates from major CAs for domains he does not own.We present a measurement method that allows us to check CAs for a list of technical weaknesses during their domain validation procedures.Our results show that all tested CAs are vulnerable in one or even multiple ways, because they rely on a combination of insecure protocols like DNS and HTTP and do not implement existing secure alternatives like DNSSEC and TLS.We have validated our methodology experimentally and disclosed these vulnerabilities to CAs.Based upon our findings we provide recommendations to domain owners and CAs to close this fundamental weakness in web security.
Saliency Methods for Explaining Adversarial Attacks

The classification decisions of neural networks can be misled by small imperceptible perturbations. This work aims to explain the misled classifications using saliency methods. The idea behind saliency methods is to explain the classification decisions of neural networks by creating so-called saliency maps. Unfortunately, a number of recent publications have shown that many of the proposed saliency methods do not provide insightful explanations. A prominent example is Guided Backpropagation (GuidedBP), which simply performs (partial) image recovery. However, our numerical analysis shows the saliency maps created by GuidedBP do indeed contain class-discriminative information. We propose a simple and efficient way to enhance the saliency maps. The proposed enhanced GuidedBP shows the state-of-the-art performance to explain adversary classifications.
Single image shadow removal by optimization using non-shadow anchor values

Shadow removal has evolved as a pre-processing step for various computer vision tasks. Several studies have been carried out over the past two decades to eliminate shadows from videos and images. Accurate shadow detection is an open problem because it is often considered difficult to interpret whether the darkness of a surface is contributed by a shadow incident on it or not. This paper introduces a color-model based technique to remove shadows from images. We formulate shadow removal as an optimization problem that minimizes the dissimilarities between a shadow area and its non-shadow counterpart. To achieve this, we map each shadow region to a set of non-shadow pixels, and compute an anchor value from the non-shadow pixels. The shadow region is then modified using a factor computed from the anchor value using particle swarm optimization. We demonstrate the efficiency of our technique on indoor shadows, outdoor shadows, soft shadows, and document shadows, both qualitatively and quantitatively.
Mawdoo3 AI at MADAR Shared Task: Arabic Tweet Dialect Identification

Arabic dialect identification is an inherently complex problem, as Arabic dialect taxonomy is convoluted and aims to dissect a continuous space rather than a discrete one.In this work, we present machine and deep learning approaches to predict 21 fine-grained dialects form a set of given tweets per user.We adopted numerous feature extraction methods most of which showed improvement in the final model, such as word embedding, Tf-idf, and other tweet features.Our results show that a simple LinearSVC can outperform any complex deep learning model given a set of curated features.With a relatively complex user voting mechanism, we were able to achieve a Macro-Averaged F1-score of 71.84% on MADAR shared subtask-2.Our best submitted model ranked second out of all participating teams.
Tibetan Multi-dialect Speech and Dialect Identity Recognition

Tibetan language has very limited resource for conventional automatic speech recognition so far. It lacks of enough data, sub-word unit, lexicons and word inventories for some dialects. And speech content recognition and dial... | Find, read and cite all the research you need on Tech Science Press
A High-Quality Multilingual Dataset for Structured Documentation Translation

This paper presents a high-quality multilingual dataset for the documentation domain to advance research on localization of structured text. Unlike widely-used datasets for translation of plain text, we collect XML-structured parallel text segments from the online documentation for an enterprise software platform. These Web pages have been professionally translated from English into 16 languages and maintained by domain experts, and around 100,000 text segments are available for each language pair. We build and evaluate translation models for seven target languages from English, with several different copy mechanisms and an XML-constrained beam search. We also experiment with a non-English pair to show that our dataset has the potential to explicitly enable 17 × 16 translation settings. Our experiments show that learning to translate with the XML tags improves translation accuracy, and the beam search accurately generates XML structures. We also discuss trade-offs of using the copy mechanisms by focusing on translation of numerical words and named entities. We further provide a detailed human analysis of gaps between the model output and human translations for real-world applications, including suitability for post-editing.
Quantum convolutional data-syndrome codes

We consider performance of a simple quantum convolutional code in a fault-tolerant regime using several syndrome measurement/decoding strategies and three different error models, including the circuit model.