Reviewerly/exHarmony huggingface dataset:
A Novel Hard Mining Center-Triplet Loss for Person Re-identification

Recently, center loss and triplet loss have proved their effectiveness for person re-identification. However, they have difficulties in making optimizations of the intra/inter-class distance and the cost of computing and mining hard training samples simultaneously. To solve these problems, in this paper, we propose a hard mining center-triplet loss, a novel improved strategy of triplet loss. For one thing, it combines the advantages of center loss and triplet loss aiming at minimizing the intra-class distance and maximizing the inter-class distance. For another thing, it employs hard sample mining strategy on the level of center of class instead of individual sample to mine hard triplets with the purpose to reducing the number of hard triplets for training and further reducing the cost of computing. Finally, the results on two large-scale datasets Market1501 and DukeMTMC-reID show the robustness and efficiency of our method in making optimizations of these problems simultaneously and learning robust feature representation, which also demonstrate that our method outperforms most of existing loss function and achieves better performance for person re-identification.
Table Localization and Field Value Extraction in Piping and Instrumentation Diagram Images

Piping and Instrumentation Diagrams (P&IDs) are graph-based engineering drawings utilised in process engineering. These documents also contain additional information in tabular form. In this paper, the localisation and extraction of information of these tables are investigated. Documents used in this context are scanned raster version of P&IDs with tabular data inside a frame. The objective is to extract fields information from these tabular structures. This process is mainly divided into table localisation and then table field extraction from the segmented tables. The table localization task is achieved primarily with contour detection methods of computer vision. For the field-value extraction, a combination of rule-based keywords and navigation approach is used, utilising an Optical Character Recognition (OCR) for text extraction and regular expression for string comparison. This paper describes application of this extendable approach to the P&ID domain, where it achieved a promising result on a private dataset.
Reassembling Shredded Document Stripes Using Word-Path Metric and Greedy Composition Optimal Matching Solver

This paper develops a shredded document reassembly algorithm based on character/word detection. A new word compatibility estimation metric and a searching strategy called Greedy Composition and Optimal Matching (GCOM) are proposed to compose documents from their vertically shredded stripes. We reduce the stripe puzzle reassembly problem to the traveling salesman problem (TSP) on a sparse graph. The word-path compatibility metric takes advantages of the optical character recognition (OCR) to compute the compatibility score among a group of stripes. The global composition strategy, based on an integration of greedy composition and optimal matching, is proposed to search for a maximal Hamiltonian path and the final global reassembly. We demonstrate that our solver outperforms the state-of-the-art puzzle solvers on reassembling stripe shredded documents.
LTRC-MT Simple \&amp; Effective Hindi-English Neural Machine Translation Systems at WAT 2019

This paper describes the Neural Machine Translation systems of IIIT-Hyderabad (LTRC-MT) for WAT 2019 Hindi-English shared task.We experimented with both Recurrent Neural Networks & Transformer architectures.We also show the results of our experiments of training NMT models using additional data via backtranslation.
A Spatio-temporal Recommender System for On-demand Cinemas

On-demand cinemas are a new type of offline entertainment venues which have shown the rapid expansion in the recent years. Recommending movies of interest to the potential audiences in on-demand cinemas is keen but challenging because the recommendation scenario is totally different from all the existing recommendation applications including online video recommendation, offline item recommendation and group recommendation. In this paper, we propose a novel spatio-temporal approach called Pegasus. Because of the specific characteristics of on-demand cinema recommendation, Pegasus exploits the POI (Point of Interest) information around cinemas and the content descriptions of movies, apart from the historical movie consumption records of cinemas. Pegasus explores the temporal dynamics and spatial influences rooted in audience behaviors, and captures the similarities between cinemas, the changes of audience crowds, time-varying features and regional disparities of movie popularity. It offers an effective and explainable way to recommend movies to on-demand cinemas. The corresponding Pegasus system has been deployed in some pilot on-demand cinemas. Based on the real-world data from on-demand cinemas, extensive experiments as well as pilot tests are conducted. Both experimental results and post-deployment feedback show that Pegasus is effective.
An exploration of automated narrative analysis via machine learning

The accuracy of four machine learning methods in predicting narrative macrostructure scores was compared to scores obtained by human raters utilizing a criterion-referenced progress monitoring rubric. The machine learning methods that were explored covered methods that utilized hand-engineered features, as well as those that learn directly from the raw text. The predictive models were trained on a corpus of 414 narratives from a normative sample of school-aged children (5;0-9;11) who were given a standardized measure of narrative proficiency. Performance was measured using Quadratic Weighted Kappa, a metric of inter-rater reliability. The results indicated that one model, BERT, not only achieved significantly higher scoring accuracy than the other methods, but was consistent with scores obtained by human raters using a valid and reliable rubric. The findings from this study suggest that a machine learning method, specifically, BERT, shows promise as a way to automate the scoring of narrative macrostructure for potential use in clinical practice.
Adaptively Scheduled Multitask Learning: The Case of Low-Resource Neural Machine Translation

Neural Machine Translation (NMT), a datahungry technology, suffers from the lack of bilingual data in low-resource scenarios.Multitask learning (MTL) can alleviate this issue by injecting inductive biases into NMT, using auxiliary syntactic and semantic tasks.However, an effective training schedule is required to balance the importance of tasks to get the best use of the training signal.The role of training schedule becomes even more crucial in biased-MTL where the goal is to improve one (or a subset) of tasks the most, e.g.translation quality.Current approaches for biased-MTL are based on brittle handengineered heuristics that require trial and error, and should be (re-)designed for each learning scenario.To the best of our knowledge, ours is the first work on adaptively and dynamically changing the training schedule in biased-MTL.We propose a rigorous approach for automatically reweighing the training data of the main and auxiliary tasks throughout the training process based on their contributions to the generalisability of the main NMT task.Our experiments on translating from English to Vietnamese/Turkish/Spanish show improvements of up to +1.2 BLEU points, compared to strong baselines.Additionally, our analyses shed light on the dynamic of needs throughout the training of NMT: from syntax to semantic.
Few-shot hypercolumn-based mitochondria segmentation in cardiac and outer hair cells in focused ion beam-scanning electron microscopy (FIB-SEM) data

We present a novel AI-based approach to the few-shot automated segmentation of mitochondria in large-scale electron microscopy images. Our framework leverages convolutional features from a pre-trained deep multilayer convolutional neural network, such as VGG-16. We then train a binary gradient boosting classifier on the resulting high-dimensional feature hypercolumns. We extract VGG-16 features from the first four convolutional blocks and apply bilinear upsampling to resize the obtained maps to the input image size. This procedure yields a 2688-dimensional feature hypercolumn for each pixel in a 224 × 224 input image. We then apply L1-regularized logistic regression for supervised active feature selection to reduce dependencies among the features, to reduce overfitting, as well as to speed-up gradient boosting-based training. During inference we block process 1728 × 2022 large microscopy images. Our experiments show that in such a formulation of transfer learning our processing pipeline is able to achieve high-accuracy results on very challenging datasets containing a large number of irregularly shaped mitochondria in cardiac and outer hair cells. Our proposed few-shot training approach gives competitive performance with the state-of-the-art using far less training data.
A Novel Three-Layer QR Code Based on Secret Sharing Scheme and Liner Code

Quick Response (QR) code, a machine-readable symbol, is widely employed in all walks of life due to its large information capacity, strong error correction ability, and fast reading speed. However, anyone with a standard decoder could obtain stored information. In this paper, utilizing the characteristics of the Hamming code, wet paper code, and the recognition mechanism of the QR code, we introduce a high-capacity QR code with three-layer information to protect the sensitive information. In the proposed scheme, we utilize the XOR-based secret-sharing algorithm to embed the second-layer information on the column vector of the constructed random matrix block. Then, without affecting the embedding result of the second layer information, the matrix block elements are reused again, and the Hamming code is constructed with the column vector. Based on the error correction mechanism of the Hamming code, the third layer of information is embedded on the column vector and encoded by wet paper coding to realize the blind extraction. Finally, based on the recognition mechanism of the QR code, the random matrix block containing the secret information is fused with the carrier QR code, and the public information of the carrier QR code is used as the first-layer information. Compared with other schemes, the proposed scheme has the advantages of high information payload, low computational complexity, and strong robustness.
Domain independent redundancy elimination based on flow vectors for static video summarization

Video summarization aims to find a compact representation of input videos. The method finds out interesting parts of the video by discarding the remaining parts of the video. The abstracts thus generated enhances browsing and retrieval of video data. The quality of summaries generated by video summarization algorithms can be improved if the redundant frames in the input video are taken care of before summarization. This paper presents a novel domain-independent method for redundancy elimination from input videos before summarization maintaining keyframes in the original video. The frames of input video are first presampled by selecting two frames in one second. The flow vectors between consecutive frames are computed using SIFT Flow algorithm. The magnitude of flow vectors at each pixel position of the frame are summed up to find the displacement magnitude between the consecutive frames. The redundant frames are filtered out based on local averaging of the displacement values. The evaluation of the method is done using two standard datasets namely VSUMM and OVP. The results demonstrate that an average reduction rate of 97.64% is achieved consistently on videos of all categories. The method also gives superior results compared to other state-of-the-art redundancy elimination methods for video summarization.
Contextual Text Denoising with Masked Language Model

Recently, with the help of deep learning models, significant advances have been made in different Natural Language Processing (NLP) tasks.Unfortunately, state-of-the-art models are vulnerable to noisy texts.We propose a new contextual text denoising algorithm based on the ready-to-use masked language model.The proposed algorithm does not require retraining of the model and can be integrated into any NLP system without additional training on paired cleaning training data.We evaluate our method under synthetic noise and natural noise and show that the proposed algorithm can use context information to correct noise text and improve the performance of noisy inputs in several downstream tasks.
COMPUTER-AIDED HAZOP STUDIES: KNOWLEDGE REPRESENTATION AND ALGORITHMIC HAZARD IDENTIFICATION

Safety assessments are conducted to identify and assess the risks that arise from processes, process plants or technical systems in general.This includes the identification of potential hazards posed by plants.One recognized and generally accepted method for this is the hazard and operability (HAZOP) method.It is a human-centered process that is time-and labor-intensive.In the presented research approach, the structure of a computer-aided HAZOP system is described.The identification of hazards and malfunctions within technical systems is knowledge-intensive.Within this research approach, it transpired that the semantically correct and detailed modeling of deviation cause and effect relationships in the form of ontologies are of particular importance to draw correct conclusions.Thus, the guiding principles of a knowledge representation framework are described from a process safety perspective, and serve as a basis for the automatic identification of hazards.An integral understanding of the process, process plant and involved substances requires extensive knowledge.The way in which this knowledge is used and the search for hazards is conducted has an influence on the completeness of the results.Within this approach, the hazard/malfunction identification is conducted on different layers of abstraction to improve the efficiency of the search algorithm.The proposed methodology is applied within a case study to a technical system that consists of a compressor, vessel and valve.The first results demonstrate that the proposed method is well-suited to understand and identify the context of hazards and malfunctions.Thus, a system for computer-aided HAZOP studies can be used to assist HAZOP conductors in performing hazard analysis while increasing the speed of safety assessments and serving as a decision support system.
A light weight encryption over big data in information stockpiling on cloud

&lt;div&gt;&lt;p class="abstract"&gt;Data is growing exponentially in the fast Changing World of Information and Communications Technology. Information from sensors, cell phones, social networking sites, logical information and ventures all are adding to this gigantic blast in the information. One of the best mainstream utilities available for dealing with the colossal measure of data is the Hadoop community. Enterprises are progressively depending on Hadoop for preparing their essential information. In any case, Hadoop is still developing. There is much powerlessness found in Hadoop, which can scrutinize the security of the sensitive data that undertakings have entrusted on it. In this paper, security issues related to the system have been discussed. Besides, we have attempted to give a short overview of the currently accessible arrangements and their constraints. Towards the end, a novel strategy, which can be utilized to kill the detected vulnerabilities in the structure, has been introduced. In the cutting edge period, data security has moved towards becoming a basic need for every single person. Be that as it may, not every person can bear the cost of the specific circulations given by various sellers to their Hadoop group. This paper displays an effective system that anybody can use with their Hadoop to secure Data.&lt;/p&gt;&lt;/div&gt;
Dynamic Release of Big Location Data Based on Adaptive Sampling and Differential Privacy

Data releasing is a key part bridging between the collection of big data and their applications.Traditional methods release the static version of dataset or publish the snapshot with a fixed sampling interval, which cannot meet the dynamic query requirements and query precision for big data.Moreover, the quality of published data cannot reflect the characteristics of the dynamic changes of big data, which often leads to subsequent data analysis and mining errors.This paper proposes an adaptive sampling mechanism and privacy protection method for the release of big location data.In order to reflect the dynamic change of data in time, we design an adaptive sampling mechanism based on the proportional-integral-derivative (PID) controller according to the temporal and spatial correlation of the location data.To ensure the privacy of published data, we propose a heuristic quad-tree partitioning method as well as a corresponding privacy budget allocation strategy.Experiments and analysis prove that the adaptive sampling mechanism proposed in this paper can effectively track the trend of dynamic changes of data, and the designed differential privacy method can improve the accuracy of counting query and enhance the availability of published data under the premise of certain privacy intensity.The proposed methods can also be readily extended to other areas of big data release applications.
Robust Interacting Multiple Model Filter Based on Student’s t-Distribution for Heavy-Tailed Measurement Noises

In maneuvering target tracking applications, the performance of the traditional interacting multiple model (IMM) filter deteriorates seriously under heavy-tailed measurement noises which are induced by outliers. A robust IMM filter utilizing Student’s t-distribution is proposed to handle the heavy-tailed measurement noises in this paper. The measurement noises are treated as Student’s t-distribution, whose degrees of freedom (dof) and scale matrix are assumed to be governed by gamma and inverse Wishart distributions, respectively. The mixing distributions of the target state, dof, and scale matrix are achieved through the interacting strategy of IMM filter. These mixing distributions are used for the initialization of time prediction. The posterior distributions of the target state, dof, and scale matrix conditioned on each mode are obtained by employing variational Bayesian approach. Then, the target state, dof, and scale matrix parameters are jointly estimated. A variational method is also given to estimate the mode probability. The unscented transform is utilized to solve the nonlinear estimation problem. Simulation results show that the proposed filter improves the estimation accuracy of target state and mode probability over existing filters under heavy-tailed measurement noises.
Enhancing Prediction Models for One-Year Mortality in Patients with Acute Myocardial Infarction and Post Myocardial Infarction Syndrome.

Predicting the risk of mortality for patients with acute myocardial infarction (AMI) using electronic health records (EHRs) data can help identify risky patients who might need more tailored care. In our previous work, we built computational models to predict one-year mortality of patients admitted to an intensive care unit (ICU) with AMI or post myocardial infarction syndrome. Our prior work only used the structured clinical data from MIMIC-III, a publicly available ICU clinical database. In this study, we enhanced our work by adding the word embedding features from free-text discharge summaries. Using a richer set of features resulted in significant improvement in the performance of our deep learning models. The average accuracy of our deep learning models was 92.89% and the average F-measure was 0.928. We further reported the impact of different combinations of features extracted from structured and/or unstructured data on the performance of the deep learning models.
MarlRank

When estimating the relevancy between a query and a document, ranking models largely neglect the mutual information among documents. A common wisdom is that if two documents are similar in terms of the same query, they are more likely to have similar relevance score. To mitigate this problem, in this paper, we propose a multi-agent reinforced ranking model, named MarlRank. In particular, by considering each document as an agent, we formulate the ranking process as a multi-agent Markov Decision Process (MDP), where the mutual interactions among documents are incorporated in the ranking process. To compute the ranking list, each document predicts its relevance to a query considering not only its own query-document features but also its similar documents' features and actions. By defining reward as a function of NDCG, we can optimize our model directly on the ranking performance measure. Our experimental results on two LETOR benchmark datasets show that our model has significant performance gains over the state-of-art baselines. We also find that the NDCG shows an overall increasing trend along with the step of interactions, which demonstrates that the mutual information among documents helps improve the ranking performance.
Analysing the Impact of Supervised Machine Learning on Automatic Term Extraction: HAMLET vs TermoStat

Traditional approaches to automatic term extraction do not rely on machine learning (ML) and select the top n ranked candidate terms or candidate terms above a certain predefined cut-off point, based on a limited number of linguistic and statistical clues.However, supervised ML approaches are gaining interest.Relatively little is known about the impact of these supervised methodologies; evaluations are often limited to precision, and sometimes recall and f1-scores, without information about the nature of the extracted candidate terms.Therefore, the current paper presents a detailed and elaborate analysis and comparison of a traditional, state-ofthe-art system (TermoStat) and a new, supervised ML approach (HAMLET), using the results obtained for the same, manually annotated, Dutch corpus about dressage.
UZH@CRAFT-ST: a Sequence-labeling Approach to Concept Recognition

As our submission to the CRAFT shared task 2019, we present two neural approaches to concept recognition.We propose two different systems for joint named entity recognition (NER) and normalization (NEN), both of which model the task as a sequence labeling problem.Our first system is a BiLSTM network with two separate outputs for NER and NEN trained from scratch, whereas the second system is an instance of BioBERT fine-tuned on the concept-recognition task.We exploit two strategies for extending concept coverage, ontology pretraining and backoff with a dictionary lookup.Our results show that the backoff strategy effectively tackles the problem of unseen concepts, addressing a major limitation of the chosen design.In the cross-system comparison, BioBERT proves to be a strong basis for creating a concept-recognition system, although some entity types are predicted more accurately by the BiLSTM-based system.