Reviewerly/exHarmony huggingface dataset:
Neural Structured Learning

We present Neural Structured Learning (NSL) in TensorFlow [2], a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph, or implicit, either induced by adversarial perturbation or inferred using techniques like embedding learning. NSL is open-sourced as part of the TensorFlow [3] ecosystem and is widely used in Google across many products and services. In this tutorial, we provide an overview of the NSL framework including various libraries, tools, and APIs as well as demonstrate the practical use of NSL in different applications. The NSL website is hosted at www.tensorflow.org/neural_structured_learning, which includes details about the theoretical foundations of the technology, extensive API documentation, and hands-on tutorials.
Lyric Video Analysis Using Text Detection and Tracking

We attempt to recognize and track lyric words in lyric videos. Lyric video is a music video showing the lyric words of a song. The main characteristic of lyric videos is that the lyric words are shown at frames synchronously with the music. The difficulty of recognizing and tracking the lyric words is that (1) the words are often decorated and geometrically distorted and (2) the words move arbitrarily and drastically in the video frame. The purpose of this paper is to analyze the motion of the lyric words in lyric videos, as the first step of automatic lyric video generation. In order to analyze the motion of lyric words, we first apply a state-of-the-art scene text detector and recognizer to each video frame. Then, lyric-frame matching is performed to establish the optimal correspondence between lyric words and the frames. After fixing the motion trajectories of individual lyric words from correspondence, we analyze the trajectories of the lyric words by k-medoids clustering and dynamic time warping (DTW).
Using Machine Learning for Analyzing Sentiment Orientations Toward Eight Countries

By using machine learning technique, this article presents sentiment and concept analyses on 48,043 articles published in The Economist from 1991 through 2016. The Economist is one of the world’s most influential political and economic magazines. The article analyzes and compares the magazine’s sentiment orientations toward the Group of Seven’s ingroup member countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) and its outgroup member country China. The sentiment analyses are performed on and compared between different periods of Clinton’s, Bush’s, and Obama’s administrations in the United States; Major’s, Blair’s, Brown’s, and Cameron’s cabinets in the United Kingdom; and Kohl’s, Schröder’s, and Merkel’s in Germany. The relationship between China hosting the Olympic Games or its growing economic power and the magazine’s sentiment orientations toward the country is examined. The concept analysis on the articles with extreme positivity or negativity shows that there is no difference between the ingroup and outgroup members in the topics covered in The Economist.
Feasibility study on carbonated water for Doppler imaging in medical ultrasonic applications

A medium other than the blood-mimicking fluid generally used for a Doppler ultrasound that allows the appearance of blood-like responses in ultrasound images is investigated in this study. To create the media used in this study, sugar, salt, and starch (1 g each) were diluted in 50 cc of water, and along with distilled water, cooking oil, and carbonated water (50 cc each) without dilution, were then individually added into six latex balloons. A water tank of 18 (W) × 30 (L) × 10 (H) cm3 was filled with water, and all six balloons containing a prepared fluid were fixed three each on opposite walls of the tank 3 cm below the water surface. To obtain a proper Doppler ultrasound, the gain was set to 54, 60, and 66, and the color Doppler was measured. A qualitative evaluation was conducted through blind tests by 15 radiologists, and the coefficient of variation (COV), natural image quality evaluator (NIQE), and blind/referenceless image spatial quality evaluator (BRISQUE) were used for a quantitative evaluation. In the evaluations, carbonated water was evaluated as achieving a clear flow (p < 0.05) and a high value in all cases with a change in gain. This study investigated the Doppler sensitivity using sugar water, salt water, starch, cooking oil, distilled water, and carbonated water, and confirmed that carbonated water achieves the highest Doppler sensitivity. The results of this study can be utilized as basic data in various future studies on Doppler sensitivity using carbonated water.
Multi-Label and Concatenated Neural Block Decoders

There has been a growing interest in designing neural-network based decoders (or neural decoders in short) for communication systems. In the prior work, we cast the problem of decoding an (n, k) block code as a single-label classification problem, and it is shown that the performance of such single-label neural decoders closely approaches that of the corresponding maximum likelihood soft-decision (ML-SD) decoders. The main issue is that the number of output nodes of single-label neural decoders increases exponentially with k, making it prohibitive to decode a code with medium or large dimension. To address this issue, we first explore a multi-label classification based neural decoder for block codes, in which the number of output nodes increases linearly with k. The complexity of the multi-label neural decoder is lower, but the performance is still close to that of the ML-SD decoder. We also consider concatenating a high-rate short-length outer code with the original code as the inner code. The proposed concatenated decoding architecture consists of a multi-label neural decoder for the inner code and a single label neural decoder for the outer code. The results demonstrate that the concatenated decoding approach leads to better bit and block error performance as compared to a benchmark soft-decision decoder. We note that the overall size of the concatenated neural decoder is close to that of the single-label neural decoder.
Hybrid kernel approach to Gaussian process modeling with colored noises

Traditional Gaussian Process Regression (TGPR) models typically assume independent identically distributed (i.i.d.) noises for all observations. However, applications with colored noises/disturbances frequently arise in modeling of complex processes. In this work, first, we consider to model noise by an ARMA time series model and determine its covariance for subsequent Gaussian Process (GP) modeling. Then a novel approach based on hybrid kernels is proposed, thereby avoiding parametric modeling of the colored noises. Moreover, all hyper-parameters are estimated simultaneously by using a particle swarm optimization (PSO) algorithm. Finally, a synthetic data, a simulated example, as well as a polyester polymerization process are used to demonstrate the effectiveness of the proposed approach.
Mining Large Quasi-cliques with Quality Guarantees from Vertex Neighborhoods

Mining dense subgraphs is an important primitive across a spectrum of graph-mining tasks. In this work, we formally establish that two recurring characteristics of real-world graphs, namely heavy-tailed degree distributions and large clustering coefficients, imply the existence of substantially large vertex neighborhoods with high edge-density. This observation suggests a very simple approach for extracting large quasi-cliques: simply scan the vertex neighborhoods, compute the clustering coefficient of each vertex, and output the best such subgraph. The implementation of such a method requires counting the triangles in a graph, which is a well-studied problem in graph mining. When empirically tested across a number of real-world graphs, this approach reveals a surprise: vertex neighborhoods include maximal cliques of non-trivial sizes, and the density of the best neighborhood often compares favorably to subgraphs produced by dedicated algorithms for maximizing subgraph density. For graphs with small clustering coefficients, we demonstrate that small vertex neighborhoods can be refined using a local-search method to grow larger cliques and near-cliques. Our results indicate that contrary to worst-case theoretical results, mining cliques and quasi-cliques of non-trivial sizes from real-world graphs is often not a difficult problem, and provides motivation for further work geared towards a better explanation of these empirical successes.
Performance Analysis of Image Coding methods using Wavelet and Noise Shaping

The application of 2-D dual-tree-discrete-wavelet-transform (DD-WT) is explored which is oriented and superfluous over Discrete-wavelet-transform (D-WT) for image coding which is being critically sampled transform. Iterative-projection-based-noise-shaping (IPBNS) algorithm has been applied for image coding which is been investigated through DD-WTs. Image originality is being kept reserved by compensation of small coefficients by modification of large coefficient in DD-WT; since IPBNS is one of sparsifying method. Results are investigated and confirmed that DD-WT along with IPBNS algorithm for image coding is far more efficient than the performance observed with D-WT and D-WT with noise shaping.
Enhancing Image Representations for Occluded Face Recognition via Reference Conditioned Low-Rank projection

Deep learning in face recognition is widely explored in recent times due to its ability to produce state-of-the-art results and availability of large public datasets. While recent deep learning approaches involving margin loss based image representations produce 99% accuracy across benchmarks, none of these studies focus explicitly on occluded face verification. Further, in real world scenarios, there is a need for efficient methods that cater to the cases of occlusion of faces with hats, scarves, goggle or sometimes exaggerated facial expression. Moreover, with face verification gathering traction in mainstream real-time embedded applications of surveillance, the proposed approaches need to be highly accurate. In this paper, we revisit the same through a large-scale study involving multiple synthetically created goggle-occluded face datasets using multiple state-of-the-art face representations. Through this study, we identify that occlusion in faces results in non-isotropic face representations in feature space which results in a drop in performance. Therefore, we propose an approach to enhance existing face representations by learning reference conditioned Low-Rank projections (RCLP), which can create isotropic representations thereby improving face recognition. We benchmark the developed approach over synthetically goggled versions of LFW, CFP-FP, ATT, FEI, Georgia Tech and Essex University face databases with representations from ResNet-ArcFace, VGGFace, MobilefaceNet-ArcFace LightCNN resulting in a total of 100 + experiments where we achieve improvements in the accuracy-rate across all with a maximum of 4% on FEI dataset. Finally, to validate the approach in a realistic scenario, we additionally present results over our internal face verification dataset of 1k images and confirm that the proposed approach only shows positive results without degrading existing baseline performance.
Performance Analysis of Blockchain-Based Systems for Industry Applications

Blockchain is the most prominent phenomenon of the technological world and provides a foundation for future ecosystems to build trust less nodes in a distributed system. However, the security and privacy provided by the blockchain-based systems in industrial and business applications are not yet matured. Therefore, the security and privacy of the domains are under future researches. This paper presents and highlights the security and privacy feature in Blockchain-based systems, specifically in supply chain management, trade digitization, and smart factories. We referenced some real-life use cases of Blockchain in the supply chain, letter of credits, trade digitization, and in Smart factories. We focus on recent challenges while adding security in these systems with the blockchain and suggests some solutions to these challenges. The existing framework and merging a private block with a robust privacy guideline, following confidentiality, integrity, and availability (CIA). The traditional blockchain smart factory includes sensing layer, management hub layer, storage, firmware, and application layer, therefore, incorporating the Hyperledger Fabric with our new secure communication and storage architecture will enhance security and privacy in blockchain-based smart factories.
Extending the Knowledge Discovery Metamodel to Support Tasks and Presentation Behaviors of Design Knowledge

Currently, the main objective of all programmers and designers is to render interactive systems usable and effective. So, users can complete their tasks and achieve their goals. To ensure that, programmers and designers require good understanding of system characteristics and functionality. This work focused on an approach to automate the process of extracting the system information. The approach is based on the ADM initiative as the best solution for system's evolution. The OMG ADM Task Force defines a set of standards to facilitate that, like Knowledge Discovery Metamodel, captures design knowledge needed for the construction of future user interfaces. Actually, KDM allows abstract structural and semantical aspect representation. However, no support exists for expressing behavior of system content, interaction, user control, and activities of the front-end applications. The authors hope to alleviate this lack by extending KDM model to fulfill the needs of complete abstract model construction.
Towards using differentially private synthetic data for machine learning in collaborative data science projects

As organisations increasingly embrace data science to extract additional value from the data they hold, understanding how ethical and secure data sharing practices effect the utility of models is necessary. For organisations taking first steps towards data science applications, collaborations may involve third parties which intend to design and train models for the data owner to use. However, the disclosure of bulk data sets presents risks in terms of privacy and security.
The Courtade-Kumar Most Informative Boolean Function Conjecture and a Symmetrized Li-Médard Conjecture are Equivalent

We consider the Courtade-Kumar most informative Boolean function conjecture for balanced functions, as well as a conjecture by Li and Médard that dictatorship functions also maximize the L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α</sup> norm of T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> f for 1 ≤ α ≤ 2 where T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> is the noise operator and f is a balanced Boolean function. By using a result due to Laguerre from the 1880's, we are able to bound how many times an L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α</sup> -norm related quantity can cross zero as a function of α, and show that these two conjectures are essentially equivalent.
Modified Two-Filter Smoothing Method for Complex Nonlinear Target Tracking

To improve target state estimate accuracy, two-filter smoothing method is generally utilized. With the method, it needs to acquire the inverse function of target dynamic motion. However, it is difficult to obtain the inverse function when the target motion is complex nonlinear. Though the inverse function can be obtained in some cases, it may leads to incorrect backward transition of target state. In this paper, a new state space model is presented for the backward smoothing by introducing static equation and pseudo-measurement. With the proposed model, the inverse function is avoided to be calculated. Meanwhile, under the new model, all filtering algorithms based on state space model can be utilized for backward smoothing. Taking EKF as an example, the EKF implementation algorithm for two-filter smoothing is derived with the proposed model. The effectiveness and superiority of the proposed technique are validated by the results of the corresponding numerical simulations.
A Novel Large-scale Model for Real Time Sentiment Analysis Using Online Shop Reviews and Comments

Sentiment analysis concept is referring to natural language processing in lots of domains in order to find which kind of subjective data or information it expresses. Sentiment analysis recently used as a method in online shops to identify about type of buyers review and comment and an impression about services and products. In this research, we propose an adjustable sentiment analysis algorithm for real time analysis on user generated data on products in online shops with a UI that run on local shops as a friendly tool. The proposed model builds a dynamic dictionary from buyers comment and reviews gathered from online shops firstly using selected set of admin-based features extracted from a specific product (or top of a products in a category), then classifying these preprocessed data under predefined classes. According to best knowledge of authors the proposed method introduces new features vectors that strongly increase accuracy and trustworthiness in analyzing online shop reviews and comments with a low time overhead. Our extensive simulation result especially combination with an online shop for real data shows the improved accuracy and fine tuning of the polarity rank for online shops manager.
Properties of minimal charts and their applications VII: Charts of type (2,3,2)

Let Γ be a chart, and we denote by Γm the union of all the edges of label m. A chart Γ is of type (2,3,2) if there exists a label m such that w(Γ)=7, w(Γm∩Γm+1)=2, w(Γm+1∩Γm+2)=3, and w(Γm+2∩Γm+3)=2 where w(G) is the number of white vertices in G. In this paper, we prove that there is no minimal chart of type (2,3,2).
Auxiliary Edge Detection for Semantic Image Segmentation

Semantic segmentation is a challenging task which can be formulated as a pixel-wise classification problem. Most FCN-based methods of semantic segmentation apply simple bilinear up-sampling to recover the final pixel-wise prediction, which may lead to misclassification near the object edges. To solve this problem, we focus on the supplementary spatial details of semantic segmentation using edge information. We present an approach to incorporate the relevant auxiliary edge information to semantic segmentation features. By applying the explicit supervision of semantic boundary using intermediate features, the multi-tasks network learns features with strong inter-class distinctive ability. The attention-based feature fusion module fuses the high-resolution edge features with wide-receptive-field semantic features to sufficiently leverage the complementary information. Experiments on the Cityscapes dataset show the effectiveness of fusing intermediate edge information.
On the decoding of Barnes-Wall lattices

We present new efficient recursive decoders for the Barnes-Wall lattices based on their squaring construction. The analysis of the new decoders reveals a quasi-quadratic complexity in the lattice dimension. The error rate is shown to be close to the universal lower bound in dimensions 64 and 128.
DAWE: A Double Attention-Based Word Embedding Model with Sememe Structure Information

Word embedding is an important reference for natural language processing tasks, which can generate distribution presentations of words based on many text data. Recent evidence demonstrates that introducing sememe knowledge is a promising strategy to improve the performance of word embedding. However, previous works ignored the structure information of sememe knowledges. To fill the gap, this study implicitly synthesized the structural feature of sememes into word embedding models based on an attention mechanism. Specifically, we propose a novel double attention word-based embedding (DAWE) model that encodes the characteristics of sememes into words by a “double attention” strategy. DAWE is integrated with two specific word training models through context-aware semantic matching techniques. The experimental results show that, in word similarity task and word analogy reasoning task, the performance of word embedding can be effectively improved by synthesizing the structural information of sememe knowledge. The case study also verifies the power of DAWE model in word sense disambiguation task. Furthermore, the DAWE model is a general framework for encoding sememes into words, which can be integrated into other existing word embedding models to provide more options for various natural language processing downstream tasks.
An Efficient Hybridization of Artificial Bee Colony with Levy distribution for Rare Itemsets Association Rule Mining

Traditional association mining models depend on a support-confidence framework, allows the generation of frequent rules depending upon frequent itemsets identified. But, the infrequent itemsets called as rare itemsets are generally ignored by those techniques regularly hold meaningful information in certain reallife applications. In this paper, a new artificial bee colony (ABC) with Levy distribution called the ABC-L algorithm for the optimization of rare association rules. In the ABC algorithm, the exploration capability of the onlooker bee phase can be improved by the use of Levy distribution. The ABC-L algorithm is employed on the rules generated from the Apriori Rare (AR) algorithm to optimize the rare association results. The presented ARABC-L method is simulated using a set of three realtime synthetic datasets of T20D10000k, T100D10000k, T1000D10000k are used. The simulation results ensured that the rule generation process by the presented ABC-L is simple and comprehensible.
What is that Building?

The paper describes Streetside Building Search-Retrieve System (SBSRS) - a system for recognizing buildings from steetside images. SBSRS powers several distinct applications: 1) it improves map-search by enriching its streetview service with semantic information, such as location, business name, open hours, etc.; 2) it enables search by image and location - a novel form of visual image search where both visual and location signals are used to identify the most relevant result to a query image of a building.
Provable Efficient Skeleton Learning of Encodable Discrete Bayes Nets in Poly-Time and Sample Complexity

In this paper, we study the problem of skeleton learning for Bayesian networks from data. In particular, we focus on nodes taking discrete values, and the learning of all the edges while disregarding their directionality, i.e., the skeleton of the Bayesian network. The problem of learning the structure of a Bayesian network is NP-hard in general. However, we show that under certain conditions we can recover the true skeleton with sufficient number of samples. We develop a mathematical model which does not assume any specific conditional probability distributions for the nodes. We use a primal-dual witness construction to prove that, under some technical conditions on the interaction between node pairs, we can do exact recovery of the parents and children of a node by performing group ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">12</sub> -regularized multivariate regression. Thus, we recover the true Bayesian network skeleton. If degree of a node is bounded then the sample complexity of our proposed approach grows logarithmically with respect to the number of nodes in the Bayesian network. Furthermore, our method runs in polynomial time.