Reviewerly/exHarmony huggingface dataset:
Damaged buildings recognition of post-earthquake high-resolution remote sensing images based on feature space and decision tree optimization

Multi-Agent system has broad application in real world, whose security performance, however, is barely considered. Reinforcement learning is one of the most important methods to resolve Multi-Agent problems. At present, certain progress has been made in applying Multi-Agent reinforcement learning to robot system, man-machine match, and automatic, etc. However, in the above area, an agent may fall into unsafe states where the agent may find it difficult to bypass obstacles, to receive information from other agents and so on. Ensuring the safety of Multi-Agent system is of great importance in the above areas where an agent may fall into dangerous states that are irreversible, causing great damage. To solve the safety problem, in this paper we introduce a Multi-Agent Cooperation Q-Learning Algorithm based on Constrained Markov Game. In this method, safety constraints are added to the set of actions, and each agent, when interacting with the environment to search for optimal values, should be restricted by the safety rules, so as to obtain an optimal policy that satisfies the security requirements. Since traditional Multi-Agent reinforcement learning algorithm is no more suitable for the proposed model in this paper, a new solution is introduced for calculating the global optimum state-action function that satisfies the safety constraints. We take advantage of the Lagrange multiplier method to determine the optimal action that can be performed in the current state based on the premise of linearizing constraint functions, under conditions that the state-action function and the constraint function are both differentiable, which not only improves the efficiency and accuracy of the algorithm, but also guarantees to obtain the global optimal solution. The experiments verify the effectiveness of the algorithm.
Generating Fluent Translations from Disfluent Text Without Access to Fluent References: IIT Bombay@IWSLT2020

Machine translation systems perform reasonably well when the input is well-formed speech or text.Conversational speech is spontaneous and inherently consists of many disfluencies.Producing fluent translations of disfluent source text would typically require parallel disfluent to fluent training data.However, fluent translations of spontaneous speech are an additional resource that is tedious to obtain.This work describes the submission of IIT Bombay to the Conversational Speech Translation challenge at IWSLT 2020.We specifically tackle the problem of disfluency removal in disfluent-to-fluent text-to-text translation assuming no access to fluent references during training.Common patterns of disfluency are extracted from disfluent references and a noise induction model is used to simulate them starting from a clean monolingual corpus.This synthetically constructed dataset is then considered as a proxy for labeled data during training.We also make use of additional fluent text in the target language to help generate fluent translations.This work uses no fluent references during training and beats a baseline model by a margin of 4.21 and 3.11 BLEU points where the baseline uses disfluent and fluent references, respectively.
Deep Detection of Anomalies in Static Attributed Graph

While online social media is one of the greatest innovations of modern man, it often gets used to perform a barrage of malicious activities which can be anomalous in nature. The area of anomaly detection deals with this challenging task. In this paper, we methodically investigate anomaly detection for the modern content driven attributed graphs. Since labeled graph data is not available for scientific research, we work with a synthetically generated dataset with an unsupervised learning approach to prove that both attribute as well as structure should be considered. We also investigate whether deep learning in this context brings an additional advantage in anomaly detection. We extend the recent work in this area, with an innovative combination of attributed graph embedding with graph convolution technique.
Applications of Natural Language Processing in Bilingual Language Teaching: An Indonesian-English Case Study

Multilingual corpora are difficult to compile and a classroom setting adds pedagogy to the mix of factors which make this data so rich and problematic to classify.In this paper, we set out methodological considerations of using automated speech recognition to build a corpus of teacher speech in an Indonesian language classroom.Our preliminary results (64% word error rate) suggest these tools have the potential to speed data collection in this context.We provide practical examples of our data structure, details of our piloted computer-assisted processes, and fine-grained error analysis.Our study is informed and directed by genuine research questions and discussion in both the education and computational linguistics fields.We highlight some of the benefits and risks of using these emerging technologies to analyze the complex work of language teachers and in education more generally.
Analysis of the Penn Korean Universal Dependency Treebank (PKT-UD): Manual Revision to Build Robust Parsing Model in Korean

Tae Hwan Oh, Ji Yoon Han, Hyonsu Choe, Seokwon Park, Han He, Jinho D. Choi, Na-Rae Han, Jena D. Hwang, Hansaem Kim. Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies. 2020.
Steps of Pre-processing for English to Mizo SMT System

The idea behind this present paper is to construct the pre-processing techniques on Bilingual Parallel Corpus. The parallel corpus is a common essential resource for the application of NLP. The parallel corpus is a huge number of parallel sentences or text of two different languages. We propose a new framework for the pre-processing steps of the Bilingual Corpus of SMT System. The purpose of this study the virtue of the pre-processing methods which are mainly used for training the translation. The pre-processing technique contributes to a main part of the Statistical Machine Translation. In this paper, we survey the various steps of pre-processing methods used for the translation system and also how the complication of the translation method has succeeded in society so far. Experimental results of the pre-processing steps on Bible Domain ( https://www.bible.com/kjv ). ‘English-Mizo’ parallel corpus (on bible domain) are discussed and all the results of pre-processing steps are mentioned.
Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms

The statistical analysis of Randomized Numerical Linear Algebra (RandNLA) algorithms within the past few years has mostly focused on their performance as point estimators. However, this is insufficient for conducting statistical inference, e.g., constructing confidence intervals and hypothesis testing, since the distribution of the estimator is lacking. In this article, we develop an asymptotic analysis to derive the distribution of RandNLA sampling estimators for the least-squares problem. In particular, we derive the asymptotic distribution of a general sampling estimator with arbitrary sampling probabilities. The analysis is conducted in two complementary settings, i.e., when the objective of interest is to approximate the full sample estimator or is to infer the underlying ground truth model parameters. For each setting, we show that the sampling estimator is asymptotically normally distributed under mild regularity conditions. Moreover, the sampling estimator is asymptotically unbiased in both settings. Based on our asymptotic analysis, we use two criteria, the Asymptotic Mean Squared Error (AMSE) and the Expected Asymptotic Mean Squared Error (EAMSE), to identify optimal sampling probabilities. Several of these optimal sampling probability distributions are new to the literature, e.g., the root leverage sampling estimator and the predictor length sampling estimator. Our theoretical results clarify the role of leverage in the sampling process, and our empirical results demonstrate improvements over existing methods.
Sliced Iterative Generator.

We introduce the Sliced Iterative Generator (SIG), an iterative generative model that is a Normalizing Flow (NF), but shares the advantages of Generative Adversarial Networks (GANs). The model is based on iterative Optimal Transport of a series of 1D slices through the data space, matching on each slice the probability distribution function (PDF) of the samples to the data. To improve the efficiency, the directions of the orthogonal slices are chosen to maximize the PDF difference between the generated samples and the data using Wasserstein distance at each iteration. A patch based approach is adopted to model the images in a hierarchical way, enabling the model to scale well to high dimensions. Unlike GANs, SIG has a NF structure and allows efficient likelihood evaluations that can be used in downstream tasks. We show that SIG is capable of generating realistic, high dimensional samples of images, achieving state of the art FID scores on MNIST and Fashion MNIST without any dimensionality reduction. It also has good Out of Distribution detection properties using the likelihood. To the best of our knowledge, SIG is the first iterative (greedy) deep learning algorithm that is competitive with the state of the art non-iterative generators in high dimensions. While SIG has a deep neural network architecture, the approach deviates significantly from the current deep learning paradigm, as it does not use concepts such as mini-batching, stochastic gradient descent, gradient back-propagation through deep layers, or non-convex loss function optimization. SIG is very insensitive to hyper-parameter tuning, making it a useful generator tool for ML experts and non-experts alike.
RETRACTED: The Integration of Environmental Art Design Based on Artificial Intelligence

Abstract In recent years, artificial intelligence has gradually penetrated into people’s lives, and the emerging technological field of "artificial intelligence" has begun to be paid attention by more and more groups. The integration of artificial intelligence in environmental art design has also triggered human exploration of "smart home". The purpose of this article is to promote the development of environmental art design towards intelligentization by discussing the status quo of the development of the intelligent space environment. This article uses the network research method to investigate the intelligent construction of public libraries at and above the provincial level in China. In general, there are 28 libraries with self-service loan and 24-hour self-service library service facilities, and the construction of intelligent projects has reached 93.33%. Intelligentization has become the general trend.Analyze the smart home space and environmental art design, promote the development of environmental art design to be intelligent, and provide people with a better quality living environment.
An Overview of De-Identification Techniques and Their Standardization Directions

De-identification[1]-[5], [30]-[71] is the process that organizations can use to remove personal information from data that they collect, use, archive, and share with other organizations. It is recognized as an important tool for organizations to balance requirements between the use of data and privacy protection of personal information. Its objective is to remove the association between a set of identifying attributes and the data principal where identifying attribute is attribute in a dataset that is able to contribute to uniquely identifying a data principal within a specific operational context and data principal is entity to which data relates. This paper provides an overview of de-identification techniques including the data release models. It also describes the current standardization activities by the standardization development organizations in terms of de-identification. It suggests future standardization directions including potential future work items.
Interpretation of Structural Preservation in Low-Dimensional Embeddings

Despite being commonly used in big-data analytics; the outcome of dimensionality reduction remains a black-box to most of its users. Understanding the quality of a low-dimensional embedding is important as not only it enables trust in the transformed data, but it can also help to select the most appropriate dimensionality reduction algorithm in a given scenario. As existing research primarily focuses on the visual exploration of embeddings, there is still a need for enhancing interpretability of such algorithms. To bridge this gap, we propose two novel interactive explanation techniques for low-dimensional embeddings obtained from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">any</i> dimensionality reduction algorithm. The first technique LAPS produces a local approximation of the neighborhood structure to generate interpretable explanations on the preserved locality for a single instance. The second method GAPS explains the retained global structure of a high-dimensional dataset in its embedding, by combining non-redundant local-approximations from a coarse discretization of the projection space. We demonstrate the applicability of the proposed techniques using 16 real-life tabular, text, image, and audio datasets. Our extensive experimental evaluation shows the utility of the proposed techniques in interpreting the quality of low-dimensional embeddings, as well as with selecting the most suitable dimensionality reduction algorithm for any given dataset.
Incremental learning for text categorization using rough set boundary based optimized Support Vector Neural Network

Purpose Text mining has been used for various knowledge discovery based applications, and thus, a lot of research has been contributed towards it. Latest trending research in the text mining is adopting the incremental learning data, as it is economical while dealing with large volume of information. Design/methodology/approach The primary intention of this research is to design and develop a technique for incremental text categorization using optimized Support Vector Neural Network (SVNN). The proposed technique involves four major steps, such as pre-processing, feature selection, classification and feature extraction. Initially, the data is pre-processed based on stop word removal and stemming. Then, the feature extraction is done by extracting semantic word-based features and Term Frequency and Inverse Document Frequency (TF-IDF). From the extracted features, the important features are selected using Bhattacharya distance measure and the features are subjected as the input to the proposed classifier. The proposed classifier performs incremental learning using SVNN, wherein the weights are bounded in a limit using rough set theory. Moreover, for the optimal selection of weights in SVNN, Moth Search (MS) algorithm is used. Thus, the proposed classifier, named Rough set MS-SVNN, performs the text categorization for the incremental data, given as the input. Findings For the experimentation, the 20 News group dataset, and the Reuters dataset are used. Simulation results indicate that the proposed Rough set based MS-SVNN has achieved 0.7743, 0.7774 and 0.7745 for the precision, recall and F -measure, respectively. Originality/value In this paper, an online incremental learner is developed for the text categorization. The text categorization is done by developing the Rough set MS-SVNN classifier, which classifies the incoming texts based on the boundary condition evaluated by the Rough set theory, and the optimal weights from the MS. The proposed online text categorization scheme has the basic steps, like pre-processing, feature extraction, feature selection and classification. The pre-processing is carried out to identify the unique words from the dataset, and the features like semantic word-based features and TF-IDF are obtained from the keyword set. Feature selection is done by setting a minimum Bhattacharya distance measure, and the selected features are provided to the proposed Rough set MS-SVNN for the classification.
Public Health and Privacy in the Pandemic

In deciding whether to use certain health information technology in a pandemic, policymakers should analyze and apply the following criteria, which have been derived from principles commonly cited in the public health ethics literature related to public health powers generally and applied to privacy: (1) necessity and effectiveness; (2) proportionality and minimal infringement; (3) purpose limitations; and (4) justice.
Flow Chart Generation-Based Source Code Similarity Detection Using Process Mining

Source code similarity detection has extensive applications in computer programming teaching and software intellectual property protection. In the teaching of computer programming courses, students may utilize some complex source code obfuscation techniques, e.g., opaque predicates, loop unrolling, and function inlining and outlining, to reduce the similarity between code fragments and avoid the plagiarism detection. Existing source code similarity detection approaches only consider static features of source code, making it difficult to cope with more complex code obfuscation techniques. In this paper, we propose a novel source code similarity detection approach by considering the dynamic features at runtime of source code using process mining. More specifically, given two pieces of source code, their running logs are obtained by source code instrumentation and execution. Next, process mining is used to obtain the flow charts of the two pieces of source code by analyzing their collected running logs. Finally, similarity of the two pieces of source code is measured by computing the similarity of these two flow charts. Experimental results show that the proposed approach can deal with more complex obfuscation techniques including opaque predicates and loop unrolling as well as function inlining and outlining, which cannot be handled by existing work properly. Therefore, we argue that our approach can defeat commonly used code obfuscation techniques more effectively for source code similarity detection than the existing state-of-the-art approaches.
Using Block-Based Programming and Sunburst Branching to Plan and Generate Crisis Training Simulations

Simulation-based exercises for crisis response are difficult to plan. We suggest an intuitive planning interface where exercise managers can use block-based programming to create machine-readable training vignettes. An answer set programming module interprets these vignettes and generates all possible actions and causal relations, which are then visualised for the exercise manager in a sunburst diagram. This allows exercise managers to create training vignettes using visual techniques and to see the possible results and causal relations of the trainees’ actions. This facilitates using exercise results to adjust further vignette design.
Image Steganography Method to Achieve Confidentiality Using CAPTCHA for Authentication

Steganography is a data hiding technique, which is generally used to hide the data within a file to avoid detection. It is used in the police department, detective investigation, and medical fields as well as in many more fields. Various techniques have been proposed over the years for Image Steganography and also attackers or hackers have developed many decoding tools to break these techniques to retrieve data. In this paper, CAPTCHA codes are used to ensure that the receiver is the intended receiver and not any machine. Here a randomized CAPTCHA code is created to provide additional security to communicate with the authenticated user and used Image Steganography to achieve confidentiality. For achieving secret and reliable communication, encryption and decryption mechanism is performed; hence a machine cannot decode it using any predefined algorithm. Once a secure connection has been established with the intended receiver, the original message is transmitted using the LSB algorithm, which uses the RGB color spectrum to hide the image data ensuring additional encryption.
Cinema as a Transmitter of Content: Perceptions of Future Spanish Teachers for Motivating Learning

In recent decades, the diverse changes produced have accelerated the relationships regulated by media. Cinema was able to bring together moving image and sound for the first time, and as a result of its audiovisual nature, it is a particularly suitable resource for motivation in education. In this light, the teacher’s perception for its application in its initial formal stage is highly relevant. The main objective of our research, therefore, has been to analyze the perceptions of cinema as a didactic resource for the transmission of content in preschool and primary education by students who are studying to become teachers themselves. The sample was composed of 4659 students from Spanish universities, both public and private. In addition to this, the PECID questionnaire was elaborated ad hoc and a comparative ex post facto design was adopted. The result showed that over 87% of students recognized the diverse educational potentialities of cinema, with motivation being an important factor. Furthermore, significant differences were found in perceptions according to different factors such as the type of teacher training degree, the Autonomous Community in which the student studied, as well as film consumption habits. On the basis of these results, future application of cinema needs to be applied using the different potentialities detected in our study.
Improving model drift for robust object tracking

Discriminative correlation filters show excellent performance in object tracking. However, in complex scenes, the apparent characteristics of the tracked target are variable, which makes it easy to pollute the model and cause the model drift. In this paper, considering that the secondary peak has a greater impact on the model update, we propose a method for detecting the primary and secondary peaks of the response map. Secondly, a novel confidence function which uses the adaptive update discriminant mechanism is proposed, which yield good robustness. Thirdly, we propose a robust tracker with correlation filters, which uses hand-crafted features and can improve model drift in complex scenes. Finally, in order to cope with the current trackers’ multi-feature response merge, we propose a simple exponential adaptive merge approach. Extensive experiments are performed on OTB2013, OTB100 and TC128 datasets. Our approach performs superiorly against several state-of-the-art trackers while runs in real-time.
Blockchain Technology Benefit in Tackling Online Shopping Transaction Revocation Issue

Online shopping interoperability is focused a long-time ago in data exchange between the shop and the logistics companies. For the payment system, it also communicates well with the banking system. However, revocation transaction issue is still difficult to be handled while enabling interoperability because it involves fairness of related parties and difficulty in maintaining the transaction process till the end. In this paper, we present how blockchain technology might help solving the problem through four aspects: (1) digital access rules, (2) data immutability, (3) package tracking and update, and (4) easy payment management. We then propose payment distribution formulas to address the revocation issue.