Reviewerly/exHarmony huggingface dataset:
A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization

Over four decades, the majority addresses the problem of optical flow estimation using variational methods. With the advance of machine learning, some recent works have attempted to address the problem using convolutional neural network (CNN) and have showed promising results. FlowNet2, the state-of-the-art CNN, requires over 160M parameters to achieve accurate flow estimation. Our LiteFlowNet2 outperforms FlowNet2 on Sintel and KITTI benchmarks, while being 25.3 times smaller in the model size and 3.1 times faster in the running speed. LiteFlowNet2 is built on the foundation laid by conventional methods and resembles the corresponding roles as data fidelity and regularization in variational methods. We compute optical flow in a spatial-pyramid formulation as SPyNet but through a novel lightweight cascaded flow inference. It provides high flow estimation accuracy through early correction with seamless incorporation of descriptor matching. Flow regularization is used to ameliorate the issue of outliers and vague flow boundaries through feature-driven local convolutions. Our network also owns an effective structure for pyramidal feature extraction and embraces feature warping rather than image warping as practiced in FlowNet2 and SPyNet. Comparing to LiteFlowNet, LiteFlowNet2 improves the optical flow accuracy on Sintel Clean by 23.3%, Sintel Final by 12.8%, KITTI 2012 by 19.6%, and KITTI 2015 by 18.8%, while being 2.2 times faster. Our network protocol and trained models are made publicly available on https://github.com/twhui/LiteFlowNet2.
Document Image Dewarping using Deep Learning

The distorted images have been a major problem for Optical Character Recognition (OCR). In order to perform OCR on distorted images, dewarping has become a principal preprocessing step. This paper presents a new document dewarping method that removes curl and geometric distortion of modern and historical documents. Finally, the proposed method is evaluated and compared to the existing Computer Vision based method. Most of the traditional dewarping algorithms are created based on the text line feature extraction and segmentation. However, textual content extraction and segmentation can be sophisticated. Hence, the new technique is proposed, which doesn’t need any complicated methods to process the text lines. The proposed method is based on Deep Learning and it can be applied on all type of text documents and also documents with images and graphics. Moreover, there is no preprocessing required to apply this method on warped images. In the proposed system, the document distortion problem is treated as an image-to-image translation. The new method is implemented using a very powerful pix2pixhd network by utilizing Conditional Generative Adversarial Networks (CGAN). The network is trained on UW3 dataset by supplying distorted document as an input and cleaned image as the target. The generated images from the proposed method are cleanly dewarped and they are of high-resolution. Furthermore, these images can be used to perform OCR.
Deep Learning in State-of-the-Art Image Classification Exceeding 99% Accuracy

Automatic image recognition and classification is a field of science that became popular in the recent years. Free platforms as Google Collaboratory make machine learning experiments more available to perform for everyone. The current technology enables us to use image recognition in such domains as medicine, criminology, entertainment or trading. In our research we created a state-of-the-art image classifier based on convolutional neural network model to classify ten models of old polish cars. We elaborated eight step training to fine tune the neural network. As the first step the data augmentation and precomputed activations were enabled. After that we froze all the layers but the last one, found the proper learning rate and performed interactive training. Fine-tuning, proper training and appropriate data preparation brought great results. The accuracy of cars’ model recognition exceeded 99% with some room for improvement.
One-Stage Multi-Sensor Data Fusion Convolutional Neural Network for 3D Object Detection

Three-dimensional (3D) object detection has important applications in robotics, automatic loading, automatic driving and other scenarios. With the improvement of devices, people can collect multi-sensor/multimodal data from a variety of sensors such as Lidar and cameras. In order to make full use of various information advantages and improve the performance of object detection, we proposed a Complex-Retina network, a convolution neural network for 3D object detection based on multi-sensor data fusion. Firstly, a unified architecture with two feature extraction networks was designed, and the feature extraction of point clouds and images from different sensors realized synchronously. Then, we set a series of 3D anchors and projected them to the feature maps, which were cropped into 2D anchors with the same size and fused together. Finally, the object classification and 3D bounding box regression were carried out on the multipath of fully connected layers. The proposed network is a one-stage convolution neural network, which achieves the balance between the accuracy and speed of object detection. The experiments on KITTI datasets show that the proposed network is superior to the contrast algorithms in average precision (AP) and time consumption, which shows the effectiveness of the proposed network.
A Novel Framework for Node/Edge Attributed Graph Embedding

Graph embedding has attracted increasing attention due to its critical application in social network analysis. Most existing algorithms for graph embedding utilize only the topology information, while recently several methods are proposed to consider node content information. However, the copious information on edges has not been explored. In this paper, we study the problem of representation learning in node/edge attributed graph, which differs from normal attributed graph in that edges can also be contented with attributes. We propose GERI, which learns graph embedding with rich information in node/edge attributed graph through constructing a heterogeneous graph. GERI includes three steps: construct a heterogeneous graph, take a novel and biased random walk to explore the constructed heterogeneous graph and finally use modified heterogeneous skip-gram to learn embedding. Furthermore, we upgrade GERI to semi-supervised GERI (named SGERI) by incorporating label information on nodes. The effectiveness of our methods is demonstrated by extensive comparison experiments with strong baselines on various datasets.
Toward Securing Cloud-Based Data Analytics: A Discussion on Current Solutions and Open Issues

In the last few years, organizations and business professionals have realized the value of collaborative data analytics in supporting decision-making. Where several activities are performed on online data by different stakeholders, such as cleansing, aggregation, analysis, and visualization, cloud-based data analytics has become a favored choice for business professionals due to the elasticity, availability, scalability, and pay-as-you-go features offered by cloud computing. However, large amounts of data stored on the cloud are very sensitive (e.g., innovation, financial, legal, and customers' data), and so data privacy remains one of the top concerns for many reasons; mainly those relating to legal or competition issues. In this paper, we review the security and cryptographic mechanisms which aim to make data analytics secure in a cloud environment and discuss current research challenges.
A Finite‐Time Recurrent Neural Network for Computing Quadratic Minimization with Time‐Varying Coefficients

This paper proposes a Finite-time Zhang neural network (FTZNN) to solve time-varying quadratic minimization problems. Different from the original Zhang neural network (ZNN) that is specially designed to solve time-varying problems and possesses an exponential convergence property, the proposed neural network exploits a sign-bi-power activation function so that it can achieve the finite-time convergence. In addition, the upper bound of the finite convergence time for the FTZNN model is analytically estimated in theory. For comparative purposes, the original ZNN model is also presented to solve time-varying quadratic minimization problems. Numerical experiments are performed to evaluate and compare the performance of the original ZNN model and the FTZNN model. The results demonstrate that the FTZNN model is a more effective solution model for solving time-varying quadratic minimization problems.
A Comprehensive Survey on Artificial Bee Colony Algorithm as a Frontier in Swarm Intelligence

The nature is an intrinsic basis of idea for researchers continuously working in the area of optimization. The Artificial Bee Colony (ABC) algorithm imitates the foraging behavior of real honeybees and it is effectively used to solve multi-model and complex problems. Various strategies is developed on the behavior of honeybees but ABC is the most popular among all. The ABC algorithm is used to get rid of difficult real-world optimization problems that are not solvable by conventional methods. This paper presents a state-of-the-art study of ABC and its latest modifications with in-depth evaluation and analysis of recent popular variants of ABC.
Construction of noisy bound entangled states and the range criterion

In this work we consider bipartite noisy bound entangled states with positive partial transpose, that is, such a state can be written as a convex combination of an edge state and a separable state. In particular, we present schemes to construct distinct classes of noisy bound entangled states which satisfy the range criterion. As a consequence of the present study we also identify noisy bound entangled states which do not satisfy the range criterion. All of the present states are constituted by exploring different types of product bases.
Quantum-metrology estimation of spacetime parameters of the Earth outperforming classical precision

We consider quantum communication schemes where quantum optical signals are exchanged between a source on Earth and a satellite. The background curved spacetime affects the quantum state of the propagating photons. We employ quantum-metrology techniques to obtain optimal bounds for the precision of quantum measurements of relevant physical parameters encoded in the final state. We focus on satellites in low Earth orbits and we find that our scheme improves the precision of the measurement of the Schwarzschild radius obtained within previous studies. Therefore, our techniques can provide the theoretical tools for novel developments that can potentially outperform the state-of-the-art obtained through classical means. We also review the impact of the relativistic effects on a simple quantum key distribution protocol within satellite schemes and find that such effects can be greatly damaging if they are not properly accounted for.
Reasoning Visual Dialogs with Structural and Partial Observations

We propose a novel model to address the task of Visual Dialog which exhibits complex dialog structures. To obtain a reasonable answer based on the current question and the dialog history, the underlying semantic dependencies between dialog entities are essential. In this paper, we explicitly formalize this task as inference in a graphical model with partially observed nodes and unknown graph structures (relations in dialog). The given dialog entities are viewed as the observed nodes. The answer to a given question is represented by a node with missing value. We first introduce an Expectation Maximization algorithm to infer both the underlying dialog structures and the missing node values (desired answers). Based on this, we proceed to propose a differentiable graph neural network (GNN) solution that approximates this process. Experiment results on the VisDial and VisDial-Q datasets show that our model outperforms comparative methods. It is also observed that our method can infer the underlying dialog structure for better dialog reasoning.
Limitations of teleporting a qubit via a two-mode squeezed state

Recently, a teleportation scheme using a two-mode squeezed state to teleport a photonic qubit, so called a “hybrid” approach, has been suggested and experimentally demonstrated as a candidate to overcome the limitations of all-optical quantum information processing. We find, however, that there exists the upper bound of fidelity when teleporting a photonic qubit via a two-mode squeezed channel under a lossy environment. The increase of photon loss decreases this bound, and teleportation better than this limit is impossible even when the squeezing degree of the teleportation channel becomes infinity. Our result indicates that the hybrid scheme can be valid for fault-tolerant quantum computing only when the photon loss rate can be suppressed under a certain limit.
InMAS: Deep Learning for Designing Intelligent Making System

Deep learning is one of the notable solutions when developing intelligent making systems (InMASs) for students' test papers and assignments to replace the workload of the teachers and educators. This paper recommends a design method of InMAS based on the You Only Look Once (YOLOv3) algorithm. Such a method can be used in carrying out experiments on algorithm problems and creating two dedicated datasets. The first is for localization and the second is for recognition. The YOLOv3 network is employed to identify the location and extraction of each mathematical problem in every image. In the recognition part, because of the low recognition rate of traditional optical character recognition (OCR) on the handwritten characters, the YOLOv3 network is widely used to identify the characters in each arithmetic problem. In the final step, the numerical operation is held on the output characters. The template matching approach is used to evaluate the arithmetic problems with the wrong operation in the original pictures. The experimental results show that the accuracy of localization is near to 1. The proposed method carries out favorably against Baidu OCR based on recognition accuracy, showing a high accuracy of 97.15%.
A course on Geographic Data Science

This paper presents a computational learning module on Geographic Data Science (GDS).
Innovations in intellectual property rights management

Purpose The purpose of this paper is to evaluate innovations in intellectual property rights (IPR) databases, techniques and software tools, with an emphasis on selected new developments and their contribution towards achieving advantages for IPR management (IPRM) and wider social benefits. Several industry buzzwords are addressed, such as IPR-linked open data (IPR LOD) databases, blockchain and IPR-related techniques, acknowledged for their contribution in moving towards artificial intelligence (AI) in IPRM. Design/methodology/approach The evaluation, following an original framework developed by the authors, is based on a literature review, web analysis and interviews carried out with some of the top experts from IPR-savvy multinational companies. Findings The paper presents the patent databases landscape, classifying patent offices according to the format of data provided and depicting the state-of-art in the IPR LOD. An examination of existing IPR tools shows that they are not yet fully developed, with limited usability for IPRM. After reviewing the techniques, it is clear that the current state-of-the-art is insufficient to fully address AI in IPR. Uses of blockchain in IPR show that they are yet to be fully exploited on a larger scale. Originality/value A critical analysis of IPR tools, techniques and blockchain allows for the state-of-art to be assessed, and for their current and potential value with regard to the development of the economy and wider society to be considered. The paper also provides a novel classification of patent offices and an original IPR-linked open data landscape.
RedisGraph GraphBLAS Enabled Graph Database

RedisGraph is a Redis module developed by Redis Labs to add graph database functionality to the Redis database. RedisGraph represents connected data as adjacency matrices. By representing the data as sparse matrices and employing the power of GraphBLAS (a highly optimized library for sparse matrix operations), RedisGraph delivers a fast and efficient way to store, manage and process graphs. Initial benchmarks indicate that RedisGraph is significantly faster than comparable graph databases.
Verifying Strong Equivalence of Programs in the Input Language of gringo

The semantics of the input language of the ASP grounder gringo uses a translation that converts a logic program, which may contain variables and arithmetic operations, into a set of infinitary propositional formulas. In this note, we show that the result of that translation can be replaced in some cases by a finite set of first-order sentences. The translator anthem constructs that set of sentences and converts it to a format that can be processed by automated reasoning tools. anthem, in combination with the first-order theorem prover vampire, allows us to verify the strong equivalence of programs in the language of gringo.
Listwise View Ranking for Image Cropping

Rank-based learning with the deep neural network has been widely used for image cropping. However, the performance of ranking-based methods is often poor and this is mainly due to two reasons: 1) image cropping is a listwise ranking task rather than a pairwise comparison and 2) the rescaling caused by pooling layer and the deformation in view generation damage the performance of composition learning. In this paper, we develop a novel model to overcome these problems. To address the first problem, we formulate the image cropping as a listwise ranking problem to find the best view composition. For the second problem, a refined view sampling (called RoIRefine) is proposed to extract refined feature maps for candidate view generation. Given a series of candidate views, the proposed model learns the Top-1 probability distribution of views and picks up the best one. By integrating refined sampling and listwise ranking, the proposed network called the listwise view ranking network (LVRN) achieves the state-of-the-art performance both in accuracy and speed.
Communication in laboratory experiments

In this chapter we present a first survey of laboratory studies using communication.We first discuss a number of issues about the implementation of communication in the laboratory.In particular we discuss variations in the channels, the structure and the content analysis of communication.Second, we survey a number of studies in which the effects of free-form communication are compared to those with more restricted communication.We finish with a brief reference to the question why communication matters, to what the mechanisms through which communication affects behavior are.
Towards a Framework for Classifying Chatbots

From sophisticated personal voice assistants like Siri or Alexa to simplistic keyword-based search bots, today, the label “chatbot” is used broadly for all kinds of systems that use natural language as input. However, the systems summarized under this term are so diverse, that they often have very little in common with regard to technology, usage, and their theoretical background. In order to make such systems more comparable, we propose a framework that classifies chatbots based on six categories, which allow a meaningful comparison based on features which are relevant for developers, scientists, and users. Ultimately, we hope to support the scientific discourse, as well as the development of chatbots, by providing an instrument to classify and analyze different groups of chatbot systems regarding their requirements, possible evaluation strategies, available toolsets, and other common features.
Focused multidimensional scaling: interactive visualization for exploration of high-dimensional data

Visualization is an important tool for generating meaning from scientific data, but the visualization of structures in high-dimensional data (such as from high-throughput assays) presents unique challenges. Dimension reduction methods are key in solving this challenge, but these methods can be misleading- especially when apparent clustering in the dimension-reducing representation is used as the basis for reasoning about relationships within the data.We present two interactive visualization tools, distnet and focusedMDS, that help in assessing the validity of a dimension-reducing plot and in interactively exploring relationships between objects in the data. The distnet tool is used to examine discrepancies between the placement of points in a two dimensional visualization and the points' actual similarities in feature space. The focusedMDS tool is an intuitive, interactive multidimensional scaling tool that is useful for exploring the relationships of one particular data point to the others, that might be useful in a personalized medicine framework.We introduce here two freely available tools for visually exploring and verifying the validity of dimension-reducing visualizations and biological information gained from these. The use of such tools can confirm that conclusions drawn from dimension-reducing visualizations are not simply artifacts of the visualization method, but are real biological insights.
HEVC double compression detection under different bitrates based on TU partition type

During the process of authenticating the integrity of digital videos, double compression is an important evidence. High-efficiency video coding (HEVC) is the latest coding standard proposed in 2013 and has shown superior performance over its predecessors. It brings in several novel syntactic units, such as coding tree unit (CTU), prediction unit (PU), and transform unit (TU). Few methods have been reported to detect HEVC double compression by utilizing such new characteristics. In this paper, a novel scheme based on TU is proposed to detect double compression of HEVC videos. The histogram of each TU partition type in the first I/P frame of all GOPs is calculated. The feature set is fed into SVM to classify single and double compressed videos. Experimental results have demonstrated the effectiveness of TU-based feature (accuracy in [0.84, 0.97]) that bears very low dimension (10-D). And it is also shown that TU-based feature set can be combined with other feature sets to boost their performance.
Deep Learning for Detecting Breast Cancer Metastases on WSI

Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols have to focus equally on efficiency and accuracy. In this paper, we proposed an improved Deep Learning based classification pipeline for detection of cancer metastases from histological images. The pipeline consists of five stages: 1. Region of Interest (ROI) detection with Image processing. 2. Tiling ROI. 3. Deep Convolutional Neural Network (CNN) for tile-based classification. 4. Building tumor probability heat-maps. 5. Post-processing of heat-maps for slide-based classification. Our system achieved the final AUC score of 90.23% which beats the winning method of Camelyon-16 grand challenge. Compared with common methods which pay attention to the training process, we lay emphasis on the data preprocessing and data quality. In order to reduce the patches without cells, we combined opening and closing operation with Otsu algorithm together. In addition, the hard negative method was also been used to remove false positives and balance positive and negative samples. Our method yields progressive sensitivity on the challenging task of detecting small tumors in gigapixel pathology slides. Moreover, we could improve accuracy and consistency of evaluating breast cancer cases, and potentially improve patient outcomes.