Reviewerly/exHarmony huggingface dataset:
Learning 2D Morphological Network for Old Document Image Binarization

Document image binarization, especially old handwritten documents, is a very important yet challenging task. There are various bottlenecks for binarizing historical documents due to different types of degradation present imultaneously such as back impression, ink bleed through, faded colours, and wear and tear of the writing media. We consider these degradation as various types of noise in the document image. Here we have proposed a 2D morphological network which consists of basic morphological operation like dilation and erosion to perform our targeted task. The network also includes linear combination of output from dilation and erosion operations. The aforementioned 2D morphological network is applied for image binarization, where the structuring elements (SEs) and the weights of the linear combination layer are learned through back-propagation. The proposed network has been evaluated on DIBCO 2017 and H-DIBCO 2018 and ISI-Letter dataset. Our results show more convincing as compared to the results of other state-of-the-art methods. Though the network is developed for old handwritten documents, it may be tuned to work for image processing task. The source code can be found here https://github.com/ranjanZ/ICDAR_Binarization.
Japanese Character Segmentation for Historical Handwritten Official Documents Using Fully Convolutional Networks

This paper proposes a character segmentation method using a fully convolutional network (FCN) and a post-processing phase. The network is trained with five-channel images that indicate five kinds of zones within the bounding box for each character-the top half, bottom half, left half, right half, and center. The post-processing step reconstructs the bounding boxes for characters from the five-channel image of the FCN output. The proposed method possesses the following advantages: (1) It is possible to process input images including multiple text lines directly; in other words, a text line segmentation process is unnecessary. (2) It does not rely upon character recognition. (3) It is robust to variations in the sizes of characters and the gaps between characters and also to cursive characters or character overlap. In the experiment of character segmentation, the accuracy ratio was 95% for real images of historical handwritten official documents written in Japanese.
What Are You Offering?: An Overview of VODs and Recommender Systems in European Public Service Media

This article aims to give a general overview of the situation of recommender systems within European Public Service Media. As Video On Demand content consumptions increase worldwide, Public Service Media must step up their platforms with technologies that are able to compete with privately-owned giants of the industry and also deliver a technologically efficient solution to fulfill their public service commitments, and establish the standards by which the television of the future will be assessed. Through literature review and an empirical analysis of how VODs are implemented in European PSM, we will gauge the relevance of recommender systems within PSM, determine the number of them currently in use and describe them in relation with the messages on their VOD platforms.
A Visual Analytics Framework for Analyzing Parallel and Distributed Computing Applications

To optimize the performance and efficiency of HPC applications, programmers and analysts often need to collect various performance metrics for each computer at different time points as well as the communication data between the computers. This results in a complex dataset that consists of multivariate time-series and communication network data, which makes debugging and performance tuning of HPC applications challenging. Automated analytical methods based on statistical analysis and unsupervised learning are often insufficient to support such tasks without the background knowledge from the application programmers. To better explore and analyze a wide spectrum of HPC datasets, effective visual data analytics techniques are needed. In this paper, we present a visual analytics framework for analyzing HPC datasets produced by parallel discrete-event simulations (PDES). Our framework leverages automated time-series analysis methods and effective visualizations to analyze both multivariate time-series and communication network data. Through several case studies for analyzing the performance of PDES, we show that our visual analytics techniques and system can be effective in reasoning multiple performance metrics, temporal behaviors of the simulation, and the communication patterns.
From the Body Image to the Body Schema, From the Proximal to the Distal: Embodied Musical Activity Toward Learning Instrumental Musical Skills

A recent paradigm shift in music research has allowed scholars to examine the macro- and micro-processes taking place within musical performance and underlying cognitive processes. Tying in with phenomenological theories of embodied perception and cognition, this paper focuses on bodily musical activity relevant to the acquisition of instrumental musical skills - the process of learning music. Dynamic interaction with musical instruments, accompanied by the interplay of action and passion, involves body image and body schema, whose status oscillates in different phases of the acquisition of instrumental musical skills; this interaction allows humans to direct attention from their bodily states - the proximal - to the quality of musical sounds and a unity of musical experience - the distal. It is thus argued that shaping music by means of playing a musical instrument can be conceived of as an embodied process, of understanding the forms of one's own experience as related to the musical world that is created by one's bodily activity.
Hierarchical Argumentation Structure for Persuasive Argumentative Dialogue Generation

Argumentation is a process of reaching a consensus through premises and rebuttals. If an artificial dialogue system can perform argumentation, it can improve users' decisions and ability to negotiate with the others. Previously, researchers have studied argumentative dialogue systems through a structured database regarding argumentation structure and evaluated the logical consistency of the dialogue. However, these systems could not change its response based on the user's agreement or disagreement to its last utterance. Furthermore, the persuasiveness of the generated dialogue has not been evaluated. In this study, a method is proposed to generate persuasive arguments through a hierarchical argumentation structure that considers human agreement and disagreement. Persuasiveness is evaluated through a crowd sourcing platform wherein participants' written impressions of shown dialogue texts are scored via a third person Likert scale evaluation. The proposed method was compared to the baseline method wherein argument response texts were generated without consideration of the user's agreement or disagreement. Experiment results suggest that the proposed method can generate a more persuasive dialogue than the baseline method. Further analysis implied that perceived persuasiveness was induced by evaluations of the behavior of the dialogue system, which was inherent in the hierarchical argumentation structure.
A Simplistic Global Median Filtering Forensics Based on Frequency Domain Analysis of Image Residuals

Sophisticated image forgeries introduce digital image forensics as an active area of research. In this area, many researchers have addressed the problem of median filtering forensics. Existing median filtering detectors are adequate to classify median filtered images in uncompressed mode and in compressed mode at high-quality factors. Despite that, the field is lacking a robust method to detect median filtering in low-resolution images compressed with low-quality factors. In this article, a novel feature set (four feature dimensions), based on first-order statistics of frequency contents of median filtered residuals (MFRs) of original and median filtered images, has been proposed. The proposed feature set outperforms handcrafted features-based state-of-the-art detectors in terms of feature set dimensions and detection results obtained for low-resolution images at all quality factors. Also, results reveal the efficacy of proposed method over deep-learning-based median filtering detector. Comprehensive results expose the efficacy of the proposed detector to detect median filtering against other similar manipulations. Additionally, generalization ability test on cross-database images support the cross-validation results on four different databases. Thus, our proposed detector meets the current challenges in the field, to a great extent.
Serverless Architecture for Big Data Analytics

Big data is a phrase that describes the large quantity of data, it would be structure, semi structure and unstructured. In the present industry data is indispensable for the business organization. The Big Data initiatives and technologies are used to analyze this massive amount of data for gaining insights which may help in making strategic decisions. For example, data size is increasing day by day like petabyte, Exabyte, zettabyte, yottabyte and more. That is why it is going to tough and complex to manage this large scale of data. In practical, there are many challenges to process and compute this data like server management, storage, clustering, algorithm deployment, etc. As everything is done by manually, so it is hard to design the exact architecture for data analysis in the Cloud. Serverless computing is a mechanism to provide pay-per-use backend services to clients. A serverless provider lets users create and deploy code without worrying about operating and managing servers. In this paper, we present serverless architecture for big data analytics, also we show how to implement, maintain, and governance of a serverless big data application on AWS (Amazon Web Service). In addition to it, we will demonstrate the difference between traditional data analytics and big data analytics in a serverless system.
A Quality and Time Assessment of Binarization Algorithms

Binarization algorithms are an important step in most document analysis and recognition applications. Many aspects of the document affect the performance of binarization algorithms, such as paper texture and color, noises such as the back-to-front interference, stains, and even the type and color of the ink. This work focuses on determining how each document characteristic impacts the time to process and the quality of the binarized image. This paper assesses thirty of the most widely used document binarization algorithms.
Privacy Preserving Deep Learning with Distributed Encoders

In this paper, we propose a distributed machine learning framework for training and inference in machine learning models using distributed data while preserving privacy of the data owner. In the training mode, we deploy an encoder on the end-user device which extracts high level features from input data. The extracted features along with the corresponding annotation are sent to a centralized machine learning server. In the inference mode, the users submit the extracted features from encoder instead of the original data for inference to the server. This approach enables users to contributed in training a machine learning model and use inference services without sharing their original data with the server or a third party. We have studied this approach on MNIST, Fashion, SVHN and CIFAR-10 datasets. The results show high classification accuracy of neural networks, trained with encoded features, and high encryption performance of the encoders.
Self-Testing of Symmetric Three-Qubit States

Self-testing refers to a device-independent way to uniquely identify an unknown quantum device based only on the observed statistics. Earlier results on self-testing of multipartite state were restricted either to Dicke states or Graph states. In this paper, we propose self-testing schemes for a large family of symmetric three-qubit states, namely the superposition of W state and GHZ state. We first propose and analytically prove a self-testing criterion for the special symmetric state with equal coefficients of the canonical bases, by designing subsystem self-testing of partially and maximally entangled state simultaneously. Then we demonstrate for the general case, the states can be self-tested numerically by the swap method combining semidefinite programming (SDP) in high precision.
Robust Line Detection of Synthetic Aperture Radar Images Based on Vector Radon Transformation

Line detection on synthetic aperture radar (SAR) images is still challenging due to the existence of strong speckles. This article proposes a novel line detection method for SAR images based on vector Radon transform (VRT). First, the ratio of exponentially weighted averages operator is determined to calculate the edge map. Then, the VRT is proposed to transform the edge map into the parameter map and decompose the parameter map along the angle of projection into two mutually perpendicular components, i.e., parallel component and vertical component, which strips off part of the random edge information and ensures the sharpness of the peaks (local extremum). Peaks are detected in the parallel component. Finally, the intersection analysis technique is adopted to remove the redundant lines. Experiments are carried out with six amplitude-format SAR images of various bands, resolutions, and polarizations from Chinese airborne SAR systems, GaoFen-3 (GF-3) satellite, and TerraSAR-X satellite. The results demonstrate the outperformance of the proposed method. Furthermore, a road edge detection scheme is designed based on the proposed line detection method. The experimental results of the road edge detection further confirm the effectiveness of the proposed method and show its potential for practical use.
Optimal temporal logic planning with cascading soft constraints

In this paper, we address the problem of temporal logic planning given both hard specifications of the robot's mission and soft preferences on the plans that achieve the mission. In particular, we consider a problem whose inputs are a transition system, a linear temporal logic (LTL) formula specifying the robot's mission, and an ordered sequence of formulas expressed in linear dynamic logic over finite traces (LDL <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</sub> ) specifying the user's preferences for how the mission should be completed. The planner's objective is to synthesize, on this transition system, an infinite trajectory that best fits the user's preferences over finite prefixes of that trajectory while nonetheless satisfying the overall objective. We describe an algorithm for this problem that constructs, from the inputs, a product automaton -which is, in fact, a special kind of state-weighted Büchi automaton- over which an optimal trajectory is synthesized. This synthesis problem is solved via reduction to the minimax path problem in vertex weighted graphs, which can be solved by variants of the standard algorithms for computing shortest paths in a graph or by algorithms for the all-pairs bottleneck paths problem on vertex-weighted graphs. We show the applicability of the approach via some case studies, for which we present results computed by an implementation.
Towards More Realistic Human-Robot Conversation: A Seq2Seq-based Body Gesture Interaction System

This paper presents a novel system that enables intelligent robots to exhibit realistic body gestures while communicating with humans. The proposed system consists of a listening model and a speaking model used in corresponding conversational phases. Both models are adapted from the sequence-to-sequence (seq2seq) architecture to synthesize body gestures represented by the movements of twelve upper-body keypoints. All the extracted 2D keypoints are firstly 3D-transformed, then rotated and normalized to discard irrelevant information. Substantial videos of human conversations from Youtube are collected and preprocessed to train the listening and speaking models separately, after which the two models are evaluated using metrics of mean squared error (MSE) and cosine similarity on the test dataset. The tuned system is implemented to drive a virtual avatar as well as Pepper, a physical humanoid robot, to demonstrate the improvement on conversational interaction abilities of our method in practice.
Speeding-up the Handwritten Signature Segmentation Process through an Optimized Fully Convolutional Neural Network

The handwritten signature is the most used method of identity authentication. Due to their nature, signatures can be used as an agreement in many types of documentation with legal repercussions. The validation of the firmed signature is used to prevent frauds, fake documents, and identity checking. However, working with automated signature verification is a challenging task because it can appear in any part of documents with complex backgrounds, with logos, handwritten texts, and many different patterns. Besides, the application needs to consider a real-time response. In this paper, we propose an optimized architecture of a fully convolutional neural network based on the U-Net architecture for handwritten signature segmentation. Furthermore, we used data augmentation in order to increase the diversity of the available dataset and prevent the overfitting problem when training the proposed model. We conducted experiments with DSSigDataset, and we used four different data augmentation techniques to increase the dataset size. The experimental results show that our proposed approach speed-up the handwritten signature segmentation task, at the same time, achieving higher accuracy and lower variance than previous works.
Power-Aware FPGA Mapping of Convolutional Neural Networks

With an unprecedented accuracy in numerous AI tasks, convolutional neural networks (CNNs) are rapidly deployed on power-limited mobile and embedded applications. Existing mapping approaches focus on achieving high performance without explicit consideration of power consumption, leading to suboptimal solutions when power is considered in a subsequent stage. In this context, there is an emerging need for power-aware methodologies for the design of custom CNN engines. In this work, a methodology is presented for modelling the power consumption of FPGA-based CNN accelerators using a high-level description of modules, together with a power-centric search strategy for exploring power-performance trade-offs within the CNN-to-FPGA design space. By integrating into an existing CNN-to-FPGA toolflow, the proposed power estimation method can yield a prediction accuracy of 93.4% for total system power consumption. Furthermore, it is demonstrated that the associated power-oriented exploration approach can generate CNN accelerators with a 20.1% power reduction over a purely throughput-driven design for AlexNet, maintaining the design's throughput.
Digital image forensic approach based on the second-order statistical analysis of CFA artifacts

The truthfulness of digital images can be evaluated by investigating the CFA artifacts introduced due to the interpolation process of the image acquisition phase. In this paper, an image tampering detection technique is proposed by exposing the CFA artifacts in difference domain through the higher-order statistical analysis based on the Markov transition probability matrix (MTPM). Firstly, the given image is re-interpolated with most commonly used four Bayer CFA patterns. The re-interpolation process is performed by using bilinear interpolation scheme for simplicity purpose. Then, the difference between the given image and its re-interpolated versions is evaluated to analyze the CFA inconsistencies. The target difference image is selected corresponding to the maximum sum which is further processed to evaluate the MTPM based second-order statistical feature. The recommended approach is assessed on different images from UCID dataset and various social networking websites based on scalar-based and SVM/machine learning based forensic detectors. The experiment results confirm that the projected method offers improved efficiency in comparison to the existing techniques based on different forgery scenarios.
The intelligent analysis system and remote sensing images segmentation engineering by using methods of advanced machine learning and neural network modeling

Abstract The article describes the main stages of the development of an intelligent system for analysis and segmentation of remote sensing data using deep machine learning and neural network modeling. In particular, the environment for creating an experimental software module was chosen, an experiment on training a neural network was implemented and the results of the training sample were evaluated.
Human Activity Recognition using DTW Algorithm

Human activity recognition is now a well-known field of Human Computer Interaction (HCI) because of its capability to provide personalized support using different applications. For the purpose of recognizing human activities, we selected three activities (running, walking, and steady state, e.g., sitting and lying). We used the Dynamic Time Warping (DTW) algorithm as a classifier to learn and detect activities. Due to its inherent nature, DTW can provide satisfactory accuracy even with very few training samples. Using smartphone’s gyroscope and accelerometer sensors, we recorded user data during various activities. To encounter personal traits, we made sure the users were of different age, height and gender. With the help of DTW as a real time classifier, we then identify the activities against matching templates. The obtained results showed sufficient accuracy, showing the effectiveness of the approach.
Ising Dropout with Node Grouping for Training and Compression of Deep Neural Networks

Dropout is a popular regularization method to reduce over-fitting while training deep neural networks and compress the inference model. In this paper, we propose Ising dropout with node grouping, which represents a deep multilayer perceptron (MLP) neural network as a graph with fixed grouped nodes and uses the Ising energy to drop group of nodes. This method is an extension to our proposed Ising dropout method, which had the limit of solving the Ising energy model for MLPs with limited graph order. The proposed fixed grouping method enables applying drop-out to deep MLPs with any order. Performance of this method is evaluated on handwritten digits (MNIST), Fashion-MNIST, Free Spoken Digit Dataset (FSDD), and Street View House Numbers (SVHN) datasets and compared with the standard dropout and standout methods. Preliminary results show that the proposed approach can keep the classification performance competitive to the original network while eliminating optimization of unnecessary network parameters in each training cycle. This method can compress the inference model significantly while maintaining the classification performance.