Reviewerly/exHarmony huggingface dataset:
Custom OCR for Identity Documents:OCRXNet

Recent advancements in the area of Optical Character Recognition (OCR) using deep learning techniques made it possible to use for real world applications with good accuracy. In this paper we present a system named as OCRXNet. OCRXNetv1, OCRXNetv2 and OCRXNetv3 are proposed and compared on different identity documents. Image processing methods and various text detectors have been used to identify best fitted process for custom ocr of identity documents. We also introduced the end to end pipeline to implement OCR for various use cases.
Renegotiation and Recursion in Bitcoin Contracts

BitML is a process calculus to express smart contracts that can be run on Bitcoin. One of its current limitations is that, once a contract has been stipulated, the participants cannot renegotiate its terms: this prevents expressing common financial contracts, where funds have to be added by participants at run-time. In this paper, we extend BitML with a new primitive for contract renegotiation. At the same time, the new primitive can be used to write recursive contracts, which was not possible in the original BitML. We show that, despite the increased expressiveness, it is still possible to execute BitML on standard Bitcoin, preserving the security guarantees of BitML.
Hierarchical Multiagent Reinforcement Learning for Maritime Traffic Management

Increasing global maritime traffic coupled with rapid digitization and automation in shipping mandate developing next generation maritime traffic management systems to mitigate congestion, increase safety of navigation, and avoid collisions in busy and geographically constrained ports (such as Singapore's). To achieve these objectives, we model the maritime traffic as a large multiagent system with individual vessels as agents, and VTS (Vessel Traffic Service) authority as a regulatory agent. We develop a hierarchical reinforcement learning approach where vessels first select a high level action based on the underlying traffic flow, and then select the low level action that determines their future speed. We exploit the nature of collective interactions among agents to develop a policy gradient approach that can scale up to large real world problems. We also develop an effective multiagent credit assignment scheme that significantly improves the convergence of policy gradient. Extensive empirical results on synthetic and real world data from one of the busiest port in the world show that our approach consistently performs significantly better than the previous best approach.
The Integration of Marine Biodiversity Information Resources Based on Big Data Technology

Meng, Y.; Chen, Y.; Zhu, F., and Tian, E., 2020. The integration of marine biodiversity information resources based on big data technology. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 806–811. Coconut Creek (Florida), ISSN 0749-0208.With the rapid development of the Internet of Things (IOT) technology, the data reserve of marine biological species diversity is gradually attracting people's attention. Therefore, based on big data technology and Hadoop architecture, this paper designs a marine life species information retrieval system using distributed BP neural network algorithm. The design idea is to optimize the BP neural network algorithm on the basis of the Map Reduce framework and the BP neural network in parallel, and to design the architecture of the marine organism species information system. Finally, after the building of the experimental environment based on the Hadoop, three sets of test data are selected to compare the optimized algorithm with the traditional BP neural network algorithm. The results show that the recall rate and accuracy of water quality classification between traditional and optimized algorithm are almost the same, indicating that the water quality classification ability of distributed BP neural network is pretty strong. The algorithm can be well applied in the research work of marine species diversity. Meanwhile, it can also help the management and data mining analysis of marine species.
Binocular Vision Navigation Method of Marine Garbage Cleaning Robot in Unknown Dynamic Scene

Zhang, C., 2020. Binocular vision navigation method of marine garbage cleaning robot in unknown dynamic scene. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 864–867. Coconut Creek (Florida), ISSN 0749-0208.When the current method was adopted for the vision navigation of marine garbage cleaning robot, the results of target garbage location were quite different from actual results, and the time of controlling the robot to the target location was too long, resulting in low positioning accuracy and poor path planning effect. Therefore, a method of binocular vision navigation of marine garbage cleaning robot in unknown dynamic scene was put forward. The camera was calibrated to extract the feature points in target image. Moreover, the moving path of robot was planned by dynamic obstacle avoidance algorithm based on escape velocity. In the unknown dynamic scene, the navigation of marine garbage cleaning robot was realized. Experimental results show that the proposed method has high positioning accuracy and good path planning effect.
Novel Graph Model for Solving Collision-Free Multiple-Vehicle Traveling Salesman Problem Using Ant Colony Optimization

In this paper, a novel graph model to figure Collision-Free Multiple Traveling Salesman Problem (CFMTSP) is proposed. In this problem, a group of vehicles start from different nodes in an undirected graph and must visit each node in the graph, following the well-known Traveling Salesman Problem (TSP) fashion without any collision. This paper’s main objective is to obtain free-collision routes for each vehicle while minimizing the traveling time of the slowest vehicle. This problem can be approached by applying speed to each vehicle, and a novel augmented graph model can perform it. This approach accommodates not only the position of nodes and inter-node distances, but also the speed of all the vehicles is proposed. The proposed augmented graph should be able to be used to perform optimal trajectories, i.e., routes and speeds, for all vehicles. An ant colony optimization (ACO) algorithm is used on the proposed augmented graph. Simulations show that the algorithm can satisfy the main objective. Considered factors, such as limitation of the mission successfulness, i.e., the inter-vehicle arrival time on a node, the number of vehicles, and the numbers of vehicles and edges of the graph are also discussed.
Demodulating interferograms with non-uniform amplitude variations for precision non-contact optical profilometry

In this paper, we propose a fringe analysis method to address the problem of phase retrieval from interferogram corrupted with fringe amplitude non-uniformity, and show its practical application for nanoscale surface profilometry. The proposed method relies on the implementation of second order optimization combined with total variation regularization using graphic processing unit based computing. The capability of the proposed method is demonstrated via numerical simulations by inducing different amplitude non-uniformity patterns in the interferograms and through experimental non-invasive surface topography using diffraction phase microscopy.
Prediction of Defect Prone Software Modules using MLP based Ensemble Techniques

Prediction of defect prone software modules is now considered as an important activity of software quality assurance.This approach uses the software metrics to predict whether the developed module is defective or not.This research presents MLP based ensemble classification framework to predict the defect prone software modules.The framework predicts the defective modules by using three dimensions: 1) Tuned MLP, 2) Tuned MLP with Bagging 3) Tuned MLP with Boosting.In first dimension only the MLP is used for the classification after optimization.In second dimension, the optimized MLP is integrated with bagging technique.In third dimension, the optimized MLP is integrated with boosting technique.Four publically available cleaned NASA MDP datasets are used for the implementation of proposed framework and the performance is evaluated by using F-measure, Accuracy, Roc Area and MCC.The performance of the proposed framework is compared with ten widely used supervised classification techniques by using Scott-Knott ESD test and the results reflects the high performance of the proposed framework.
Affective emotion classification using feature vector of image based on visual concepts

Nowadays, deep learning technique becomes the most popular fast-growing machine learning method in an Artificial Neural Network. The Convolution Neural Network (CNN) is one of the deep learning architecture that has been applied in the field of image analysis and image classification. In this paper, we proposed a novel emotion learning model with a deep learning network. The aim of the learning model is to reduce the affective gap, that extracts the objects and background features of an image semantically, such as high-level and low-level features. These extracted features accompanied with few others and it is more effective in emotion prediction model based on visual concepts of image, that leads to better emotion recognition performance. For training and testing, the experiment is conducted on IAPS (International Affective Picture System) dataset, the Artistic Photos, and the Emotion-Image dataset. An experimental result shows that the proposed model combines visual-content and low-level features of the image that provides promising results for Affective Emotion Classification task.
Methods to quality assure, plot, summarize, interpolate, and extend groundwater-level information—examples for the Mississippi River Valley alluvial aquifer

Large-scale computational investigations of groundwater levels are proposed to accelerate science delivery through a workflow spanning database assembly, statistics, and information synthesis and packaging. A water-availability study of the Mississippi River alluvial plain, and particularly the Mississippi River Valley alluvial aquifer (MRVA), is ongoing. Software (visGWDBmrva) has been released as part of the study that demonstrates groundwater informatics for the aquifer. Considerable water-level data collected by multiple agencies over a seven-state area exist (18,903 wells; 287,272 measurements [April 22, 2019]). Data and metadata quality assurance methods, basic statistics, hydrograph visualization, outlier identification, hypothesis testing, and time-series modeling are described. Two approaches (generalized additive models [GAMs] and support vector machines [SVMs]) are used for data interpolation and extension to monthly water-level estimates. Numerical congruence between GAM and SVM estimates will be useful to limit inclusion of monthly estimates from subsequent science activities.
Forensic Authentication of WhatsApp Messenger Using the Information Retrieval Approach

The development of telecommunications has increased very rapidly since the internet-based instant messaging service has spread rapidly to Indonesia. WhatsApp is the most popular instant messaging application compared to other instant messaging services, according to the statista website users of WhatsApp services in 2018 showed significant growth by gathering 1.5 billion monthly active users or monthly active users (MAU). That number increased 14 percent compared to MAU WhatsApp in July 2017 which amounted to 1.3 billion. Daily active users aka DAU are in the range of one billion. WhatsApp handles more than 60 billion message exchanges between users around the world. This growth is predicted to continue to increase, along with the wider internet penetration. Along with WhatsApp updates with various features embedded in this application including Web-based Whatsapp for computers, this feature makes it easier for users to share data and can be synchronized with their smartphone or user's computer. Besides the positive side found in the application, WhatsApp also provides a security gap for user privacy, one of which is tapping conversations involving both smartphone and computer devices. The handling of crimes involving digital devices needs to be emphasized so that they can help the judicial process of the effects they have caused Mobile Forensics Investigation also took part in suppressing the misuse of WhatsApp's instant messaging service features, including investigating the handling of cases of WhatsApp conversations through a series of standard steps according to digital forensics procedures. Exploration of evidence (digital evidence) WhatsApp conversations will be a reference to the crime of telecommunication tapping which will then be carried out forensic investigation report involving evidence of the smartphone and computer of the victim.Keywords: Authentication, Mobile Forensics, Instant Messenger, and WhatsApp Messenger.
In the Wild: From ML Models to Pragmatic ML Systems

Enabling robust intelligence in the wild entails learning systems that offer uninterrupted inference while affording sustained learning from varying amounts of data and supervision. Such ML systems must be able to cope with the openness and variability inherent to the real world. The machine learning community has organically broken down this challenging task into manageable sub tasks such as supervised, few-shot, continual, and self-supervised learning; each affording distinct challenges and a unique set of methods. Notwithstanding this remarkable progress, the simplified and isolated nature of these experimental setups has resulted in methods that excel in their specific settings, but struggle to generalize beyond them. To foster research towards more general ML systems, we present a new learning and evaluation framework - I{N} TH{E} WIL{D} (NED). NED naturally integrates the objectives of previous frameworks while removing many of the overly strong assumptions such as predefined training and test phases, sufficient labeled data for every class, and the closed-world assumption. In NED, a learner faces a stream of data and must make sequential predictions while choosing how to update itself, adapt quickly to novel classes, and deal with changing data distributions; while optimizing for the total amount of compute. We present novel insights from NED that contradict the findings of less realistic or smaller-scale experiments which emphasizes the need to move towards more pragmatic setups. For example, we show that meta-training causes larger networks to overfit in a way that supervised training does not, few-shot methods break down outside of their narrow experimental setting, and self-supervised method MoCo performs significantly worse when the downstream task contains new and old classes. Additionally, we present two new pragmatic methods (Exemplar Tuning and Minimum Distance Thresholding) that significantly outperform all other methods evaluated in NED.
Adding features from the mathematical model of breast cancer to predict the tumour size

In this study, we combine a theoretical mathematical model with machine learning (ML) to predict tumour sizes in breast cancer. Our study is based on clinical data from 1869 women of various ages with breast cancer. To accurately predict tumour size for each woman individually, we solved our customized mathematical model for each woman, then added the solution vector of the dynamic variables in the model (in machine learning language, these are called features) to the clinical data and used a variety of machine learning algorithms. We compared the results obtained with and without the mathematical model and showed that by adding specific features from the mathematical model we were able to better predict tumour size for each woman.
Multistage Deep Neural Network Framework for People Detection and Localization Using Fusion of Visible and Thermal Images

In Computer vision object detection and classification are active fields of research. Applications of object detection and classification include a diverse range of fields such as surveillance, autonomous cars and robotic vision. Many intelligent systems are built by researchers to achieve the accuracy of human perception but could not quite achieve it yet. Convolutional Neural Networks (CNN) and Deep Learning architectures are used to achieve human like perception for object detection and scene identification. We are proposing a novel method by combining previously used techniques. We are proposing a model which takes multi-spectral images, fuses them together, drops the useless images and then provides semantic segmentation for each object (person) present in the image. In our proposed methodology we are using CNN for fusion of Visible and thermal images and Deep Learning architectures for classification and localization. Fusion of visible and thermal images is carried out to combine informative features of both images into one image. For fusion we are using Encoder-decoder architecture. Fused image is then fed into Resnet-152 architecture for classification of images. Images obtained from Resnet-152 are then fed into Mask-RCNN for localization of persons. Mask-RCNN uses Resnet-101 architecture for localization of objects. From the results it can be clearly seen that Fused model for object localization outperforms the Visible model and gives promising results for person detection for surveillance purposes. Our proposed model gives the Miss Rate of 5.25% which is much better than the previous state of the art method applied on KAIST dataset.
Load Balancing Algorithm in Cloud Computing Using Mutation Based PSO Algorithm

Cloud computing is a prominent technology that uses dynamic allocation technique to assigns tasks to virtual machines (VM). As per the usage, users are charged by the cloud service provider. There are various challenges that a Cloud service provider (CSP) faces, out of which load balancing being one of the significant challenge. Many algorithms have been proposed till now for load balancing algorithm, where each one focuses on the different parameters. However these proposed approached in the literature experiences various issues such as poor speed for convergence, untimely convergence, the first random chosen solution. None of the algorithms has proven to be completely sufficient. To solve the problems associated with existing meta-heuristic techniques, paper discuss a load balancing approach that is based on mutative Particle Swarm optimization. The load on the data centers are balanced by the help of proposed algorithm and parameters such as makespan are minimized while improving the overall fitness function of algorithm.
Early Sepsis Prediction Using Ensemble Learning with Features Extracted from LSTM Recurrent Neural Network

Early prediction of sepsis can help to identify potential risks in time and help take necessary measures to prevent more dangerous situations from occurring.In PhysioNet/Computing in Cardiology Challenge 2019, we integrate Long Short Term Memory (LSTM) recurrent neural network and ensemble learning to achieve early sepsis prediction.Specifically, we tackle the problem of class imbalance and data missing firstly, and then we manually extract features according to the prior knowledge from the medical field.In addition, we regard the prediction of sepsis as a time series prediction problem and pre-train LSTM-based models as feature extractors to obtain the "deep" features on time series that might be related to the onset of sepsis.Manual features and "deep" features are then used to train prediction models under the framework of ensemble learning, including Extreme Gradient Boosting (XGBoost) and Gradient Boosting Decision Tree (GBDT) regressor.The final normalized utility score our team (UCAS_DataMiner) have obtained was 0.313 on full hidden test set.
Incorporating representation learning and multihead attention to improve biomedical cross-sentence n-ary relation extraction

Most biomedical information extraction focuses on binary relations within single sentences. However, extracting n-ary relations that span multiple sentences is in huge demand. At present, in the cross-sentence n-ary relation extraction task, the mainstream method not only relies heavily on syntactic parsing but also ignores prior knowledge.In this paper, we propose a novel cross-sentence n-ary relation extraction method that utilizes the multihead attention and knowledge representation that is learned from the knowledge graph. Our model is built on self-attention, which can directly capture the relations between two words regardless of their syntactic relation. In addition, our method makes use of entity and relation information from the knowledge base to impose assistance while predicting the relation. Experiments on n-ary relation extraction show that combining context and knowledge representations can significantly improve the n-ary relation extraction performance. Meanwhile, we achieve comparable results with state-of-the-art methods.We explored a novel method for cross-sentence n-ary relation extraction. Unlike previous approaches, our methods operate directly on the sequence and learn how to model the internal structures of sentences. In addition, we introduce the knowledge representations learned from the knowledge graph into the cross-sentence n-ary relation extraction. Experiments based on knowledge representation learning show that entities and relations can be extracted in the knowledge graph, and coding this knowledge can provide consistent benefits.
An Automated Unified Framework for Video Deraining and Simultaneous Moving Object Detection in Surveillance Environments

In many instances, outdoor surveillance systems suffer from atrocious weather conditions such as rain, since images or videos captured by such vision systems in rainy days may undergo severe visual dilapidations.This can cause a glitch in those algorithms which are further used for object detection and tracking.Therefore, an ancillary video processing algorithm namely, video deraining is necessary prior to the implementation of object detection and tracking.This indicates the requirement of a time-consuming and complicated two-step process for the detection of moving objects in a rainy environment.This paper proposes an automated single-stage formulation for the conventional three-stage procedure such as rain steak removal, original data recovery and moving object detection as simultaneous operation in the tensor framework.The brilliance of this work is confined in the efficient formulation of an operator termed as Slice Rotational Total Variation (SRTV), to unify the different rain patterns into a common pattern so that any form of rain pattern can be effectively removed from the rainy data in a visually appealing manner by preserving the important details of the data.In this paper, SRTV regularization and tensor low-rank minimization are utilized for the effective deraining as well as efficient retrieval of clean background.Besides, l 1 norm and Tensor Total Variation (TTV) regularizers together with SRTV regularizer are employed for the faithful detection of derained moving objects.The experimental results show that the proposed method outperforms the state of the art methods in terms of deraining capability and accurate moving object detection.