Reviewerly/exHarmony huggingface dataset:
A Linear-Time n 0.4 -Approximation for Longest Common Subsequence.

We consider the classic problem of computing the Longest Common Subsequence (LCS) of two strings of length n. While a simple quadratic algorithm has been known for the problem for more than 40 years, no faster algorithm has been found despite an extensive effort. The lack of progress on the problem has recently been explained by Abboud, Backurs, and Vassilevska Williams [FOCS'15] and Bringmann and Kunnemann [FOCS'15] who proved that there is no subquadratic algorithm unless the Strong Exponential Time Hypothesis fails. This major roadblock for getting faster exact algorithms has led the community to look for subquadratic approximation algorithms for the problem.Yet, unlike the edit distance problem for which a constant-factor approximation in almost-linear time is known, very little progress has been made on LCS, making it a notoriously difficult problem also in the realm of approximation. For the general setting (where we make no assumption on the length of the optimum solution or the alphabet size), only a naive O(n^{e/2})-approximation algorithm with running time O(n^{2-e}) has been known, for any constant 0 < e ≤ 1. Recently, a breakthrough result by Hajiaghayi, Seddighin, Seddighin, and Sun [SODA'19] provided a linear-time algorithm that yields a O(n^{0.497956})-approximation in expectation; improving upon the naive O(√n)-approximation for the first time. In this paper, we provide an algorithm that in time O(n^{2-e}) computes an O(n^{2e/5})-approximation with high probability, for any 0 < e ≤ 1. Our result (1) gives an O(n^{0.4})-approximation in linear time, improving upon the bound of Hajiaghayi, Seddighin, Seddighin, and Sun, (2) provides an algorithm whose approximation scales with any subquadratic running time O(n^{2-e}), improving upon the naive bound of O(n^{e/2}) for any e, and (3) instead of only in expectation, succeeds with high probability.
Speech Rehabilitation System for Hearing Impaired Children Based on Virtual Reality Technology

In the field of public welfare, the rehabilitation of hearing-impaired children is widely concerned by the society. Virtual reality technology integrates simulation and electronic information technology, and uses computer to create a simulation environment to immerse users in the environment. This paper introduces a speech rehabilitation system for hearing-impaired children based on virtual reality technology. The system can not only help hearing-impaired children with hearing rehabilitation training, but also reduce the difficulty of rehabilitation training. The system uses virtual 3d animation to reproduce the common communication scenes in daily life, and applies the dialogue in the scene and the pronunciation actions of monosyllabic words in Chinese pinyin to the language rehabilitation training of deaf children. The experimental results show that the systematic simulated pronunciation can effectively simulate the movement of the vocal organs, and can improve the language ability of hearing impaired children in the training process, which has high application value.
Implementation of hybrid cryptographic schemes in a cloud environment for enhanced medical data security

Nowadays, several security architectures in cloud computing were employed in several applications, but they failed to secure the cloud data entirely. The current approaches use the ensemble algorithm for the decryption and encryption purpose to enhance the security technique. The input medical dataset is usually raw and might contain redundant packets and missing values. Initially, the data is preprocessed by means of the normalization technique. By using Enhanced Principal Component Analysis (EPCA) method, various attributes of the data can be obtained. After the extraction process, the classification mechanism is carried out for recognizing the attacks. The attack is predicted and is classified by means of the Adaptive AlexNet CNN classifier algorithm. A hybrid cryptographic technique in a cloud environment for improving the security rate and providing privacy preservation of the medical data in the cloud environment is presented. The proposed work mainly concentrates mostly on implementing hybrid cryptographic schemes which include AES algorithm, enhanced honeypot algorithm, SHA3 hashing and OTP in the cloud environment. It enhances the security of the data to a great extent. Thus, the presented technique is secured effectively that makes the intruders difficult to access the system as they need to attain control over servers.
Language neutrality of the LLAMA test explored: The case of agglutinative languages and multiple writing systems

The ability to learn a foreign language, language aptitude, is known to differ between individuals. To better understand second-language learning, language aptitude tests, tapping into the different components of second-language learning aptitude, are widely used. For valid conclusions on comparisons of learners with different language backgrounds, it is crucial that such tests be language neutral. Several studies have investigated the language neutrality of the freely available LLAMA tests (Granena, 2013; Rogers et al., 2016, 2017). So far, comparing a number of L1 backgrounds, including those using different writing systems such as Arabic and Mandarin, no significant differences between participants have been found. However, until now, neither participants with agglutinative language backgrounds nor with first-language backgrounds that use multiple writing systems have been included. Therefore, this study selected participants from three different first-language backgrounds: Dutch (non-agglutinative, phonogram/Latin alphabet), Hungarian (agglutinative, phonogram/Latin alphabet), and Japanese (agglutinative, phonogram/syllabic alphabet and logogram/Japanese kanji). The participants performed three subsets of the LLAMA test. Significant differences between the groups were found on two of these tests: The ability to implicitly recognize sounds (LLAMA_D subtest) and inductive grammar learning ability (LLAMA_F), but no differences were found on vocabulary learning ability (LLAMA_B). Additionally, for LLAMA_B, the number of languages learnt was a significant covariate, confirming earlier findings that some subtests seem to be linked to language learning experience. We discuss the implications of our findings on the validity of the LLAMA_D and LLAMA_F subtests.
Context-Aware Neural Machine Translation for Korean Honorific Expressions

Neural machine translation (NMT) is one of the text generation tasks which has achieved significant improvement with the rise of deep neural networks. However, language-specific problems such as handling the translation of honorifics received little attention. In this paper, we propose a context-aware NMT to promote translation improvements of Korean honorifics. By exploiting the information such as the relationship between speakers from the surrounding sentences, our proposed model effectively manages the use of honorific expressions. Specifically, we utilize a novel encoder architecture that can represent the contextual information of the given input sentences. Furthermore, a context-aware post-editing (CAPE) technique is adopted to refine a set of inconsistent sentence-level honorific translations. To demonstrate the efficacy of the proposed method, honorific-labeled test data is required. Thus, we also design a heuristic that labels Korean sentences to distinguish between honorific and non-honorific styles. Experimental results show that our proposed method outperforms sentence-level NMT baselines both in overall translation quality and honorific translations.
Intelligent Chatbot Using GNMT, SEQ-2-SEQ Techniques

Dialogue generation and intelligent chatbot development is a fairly new avenue in the field of artificial intelligence and machine learning. Chatbot development is a popular field in natural language processing. Nowadays a large amount of research projects are currently underway to make efficient and reliable chatbots using machine learning, artificial intelligence and natural language processing. Chatbots or dialogue generation agents are mainly used by businesses like online stores or startups, financial institutions like banks, credit card companies and govt organisations. There are many different types of chatbots available differing in frameworks such as code based or interface based. But these chatbots lack in genuine dialogue generation and seem rather bland and unintuitive. Some of the popular intelligent chatbots or you can say personal assistants include google assistant, Siri by apple and cortana by microsoft. The functioning of these chatbots is constrained and limited and they are not aimed to hold real world conversations. In this project endeavour we have tried to develop an intelligent chatbot using state of the art GNMT(Google neural machine Translation) model which is built upon seq-to-seq modelling having encoder-decoder architecture. This encoder decoder discussed here uses recurrent neural networks along with bi directional LSTM(Long short term memory) which is one of the hot areas.
Multiperson Interactive Activity Recognition Based on Interaction Relation Model

Multiperson activity recognition is a pivotal branch as well as a challenging topic of human action recognition research. This paper adopts a hybrid learning model to the spatio-temporal relationship and occlusion relationship among multiple people. Initially, this paper builds up an active multiperson interaction relationship estimation framework model to capture interpersonal spatio-temporal relation. This model incorporates the interaction relationship estimation framework with the multiperson relationship network. On this ground, it automatically learns from the human-computer interaction dataset in an end-to-end manner and performs reasoning with standard matrix operations. Secondly, this paper proposed an adaptive occlusion state behavior recognition method derived from the semantic knowledge model to ravel out the concern of occlusion and self-occlusion in human action recognition. Then, Petri Nets are used to recognize multiperson interactive actions. This model has been through extensive experiments on the TV interaction dataset, Vlog dataset, AVA dataset, and MLB-YouTube dataset, experimental results have proved that the recognition performance of this model is superior than the other available models. This paper prospects and summarizes the estimation framework of the interaction relationship and occlusion semantic-knowledge relationship. Experimental results suggest that the proposed method in the paper could capture the discriminative relation information for multiperson interactive activity recognition, which further validates the efficiency of the hybrid learning model.
A method for the analysis of the interaction between users and objects in 3D navigational space

Along with the improvement of eye-tracking technology, more and more distinct field of researches have introduced movements of the eye in relation to the head to understand user behavior. Most of current researches focus on the perception process of single 2-dimensional images by fixed eye-tracking devices or the head-mount devices. A method of applying eye-tracking on the analysis of the interaction between users and objects in 3D navigational space is proposed in this article. It aims to understand the visual stimulation of 3D objects and the user’s spatial navigational reactions while receiving the stimulation, and proposes the concept of 3D object attention heat map. It also proposes to construct a computational visual attention model for different geometric featured 3D objects by applying the method of feature curves. The VR results of this study also provide future assistance in the incoming immersive world. This study sets to promote eye-tracking from the mainstream of 2D field to 3D spaces and points to a deeper understanding between human and artificial product or natural objects. It would also serve an important role in the field of human-computer interaction, product usability, aids devices for cognition degenerative individuals, and even the field of visual recognition of daily human behavior.
Fast and more accurate incremental-decremental principal component analysis algorithm for online learning of face recognition

Principal component analysis (PCA) has been successfully employed for face recognition. However, if the training process occurs frequently, owing to the update or downdate of the face images used for training, batch PCA becomes prohibitively expensive to recalculate. To overcome this limitation, incremental principal component analysis (IPCA) and decremental principal component analysis (DPCA) can be utilized as a good alternative to PCA because it reuses their previous results for its updates. Many IPCA or DPCA algorithms have been proposed; however, inaccurate tracking of the mean values of the face image data accumulates decomposition errors, which results in poor performance compared with batch PCA. We proposed faster and more accurate algorithms for IPCA and DPCA that maintain accurate decomposition results. The experimental results reveal that the proposed algorithms produce eigenvectors that are significantly close to the eigenvectors of batch PCA and exhibit faster execution speed for face recognition.
Decentralized Open Platform for Vaccination—A German Example: COVID-19-Vacc

The COVID-19 pandemic has massively impacted the health of many people worldwide and poses significant challenges for our social, economic, and political life. Global vaccination should help the world overcome the pandemic and return to a “normal” life. In Germany, the Federal Ministry of Health presented its “National Vaccination Strategy COVID-19”, which describes the primary actors, elements, and activities required for the immunization of the German population. However, the implementation is challenging due to the federal organization of the German state in sixteen federal states. While essential processes such as vaccination rate monitoring and surveillance are planned centrally, the sixteen federal states are responsible for implementing the vaccination strategy in a decentralized manner. Furthermore, the European General Data Protection Regulation (EU-GDPR) imposes strict rules for processing and exchanging personal data. However, Germany is only a case in point. Governmental decisions always need to be implemented by regional and/or local actors, the number of which varies greatly depending on the country. This work addresses these challenges by proposing the COVID-19-Vacc Platform—an open and decentralized digital platform focused on vaccinations as a matter of example. The proposed platform model connects various actors and enables them to involve, conduct, and track the vaccination process while meeting all necessary data protection and security requirements defined by EU-GDPR. Using the DMS Reference Model as the theoretical framework, the blueprint of the COVID-19-Vacc Platform is developed, outlining the platform’s ecosystem structure, its interactions process model, and the service stack, defining how the proposed platform works on the operational level. Our COVID-19-Vacc Platform may help facilitate a fast and EU-GDPR compliant implementation of COVID-19 vaccination strategies. Beyond that, the proposed open and decentralized platform model might facilitate international interconnectivity and therefore the management of emerging global pandemics or other global health-related crisis.
An Efficient Video Steganography Scheme for Data Protection in H.265/HEVC

An efficient and novelty video steganography scheme with adopting averting intra prediction distortion drift technique is proposed in this paper for the enhanced protection of crucial data related to H.265/HEVC digital video. With adoption of intra prediction modes selection and DCT/DST coefficients correlation, the intra prediction distortion drift can be totally prevented in intra frame (I frame). With adoption of selection of prediction patterns of multiple adjacent prediction blocks, the intra prediction distortion drift in inter frames (B frame or P frame) can be totally prevented. Compared to the previous related works, the proposed scheme applied the averting intra prediction distortion drift technique not only intra but also inter frames, and further improve the visual quality (imperceptibility) of carrier video about H.265/HEVC. Moreover, larger embedding capacity can also be achieved rather than embedding only manipulated on I frames. The experimental results have been proven the superiority of efficiency and performance about the proposed scheme.
Devops Methods for Automation of Server Management using Ansible

Cloud has become an advanced technology in the modern field of information technology and the need to meet the growing demand of customers. Pressure on information technology organizations is constantly increasing to apply for a client in a private cloud. This change has already occurred as a large number of customers have begun direct contact with cloud vendors through this support. In addition, DevOps teams are at a very focused level now because they are responsible for automation and universal delivery and client programming. This paper focuses on the automation of customer application from the provision of environment to the delivery of the system.
Gradient Boosting with XGBoost and LightGBM

In this chapter, you will discover the gradient boosting model. In the previous chapter, you discovered the idea behind ensemble methods. As a recap, ensemble methods make powerful predictions by combining predictions of numerous small, less performant models.
Holistic Persian Handwritten Word Recognition Using Convolutional Neural Network

Due to the cursive-ness and high variability of Persian scripts, the segmentation of handwritten words into sub-words is still a challenging task. These issues could be addressed in a holistic approach by sidestepping segmentation at the character level. In this paper, an end-to-end holistic method based on deep convolutional neural network is proposed to recognize off-line Persian handwritten words. The proposed model uses only five convolutional layers and two fully connected layers for classifying word images effectively, which can lead to a substantial reduction in parameters. The effect of various pooling strategies is also investigated in this paper. The primary goal of this article is to ignore handcrafted feature extraction and attain a generalized and stable word recognition system. The presented model is assessed using two famous handwritten Persian word databases called Sadri and IRANSHAHR. The recognition accuracies were obtained at 98.6% and 94.6%, on Sadri and IRANSHAHR datasets respectively, and outperformed the state-of-the-art methods.
Evaluation of User Experience in Translator Applications (Banjar-Indonesian and Indonesian-Banjar) Based on Mobile Augmented Reality Technology using the UX Honeycomb Method

The translator application from Banjar Language to Indonesian is called BandoAR and vice versa from Indonesian to Banjar Language which is called NdoBAR. Both applications utilize Mobile Augmented Reality technology with the Optical Character Recognition (OCR) Method for word recognition in the detected image. This application is recommended and used to help tourists visiting the city of Banjarmasin to be able to understand the language used by local people as a means of communication and for the general public who want to know the peculiarities of the Banjar language itself. The purpose of this study was to evaluate the user experience of the BandoAR and NdoBAR applications. The method used is UX Honeycomb, which has seven aspects to assess the user experience of an application. A total of 50 respondents were presented to assess the two applications. The results showed that the BandoAR application had an average UX Honeycomb value of 4.91 with a Very Strongly Agree predicate and for the NdoBAR application, an average value of UX Honeycomb was 4.89 with a Very Strongly Agree predicate. Both applications have fulfilled aspects of the user experience. However, we need some fixes for the shortcomings of both apps, to continue to improve interactions, better user experience, and other smart capabilities.
HNHN: Hypergraph Networks With Hyperedge Neurons

Hypergraphs provide a natural representation for many real world datasets. We propose a novel framework, HNHN, for hypergraph representation learning. HNHN is a hypergraph convolution network with nonlinear activation functions applied to both hypernodes and hyperedges, combined with a normalization scheme that can flexibly adjust the importance of high-cardinality hyperedges and high-degree vertices depending on the dataset. We demonstrate improved performance of HNHN in both classification accuracy and speed on real world datasets when compared to state of the art methods.
Subject Review: Cloud Computing using RSA Algorithm

Because of the advantages of resource sharing in an efficient and effective way, practical data storage mechanisms, and resources delivered on demand whenever they are needed in an adequate suitable environment, cloud computing plays an indisputable role in today's software.The data available in the cloud is going to be frequently shared via various customers under different cloud categorizations like public cloud, private cloud, hybrid cloud, and so on, as stated via the National Institute of Standards and Technology (NIST).Generally, access control can be defined as a method of confirming authorized users for access to cloud data and resource sharing.Cloud computing, in the opinion of researchers concerned with cloud data security, offers a high level of data mobility, storage, availability, data integrity, backup and recovery, confidentiality and privacy.Researchers studying cloud storage are focusing on data security, whereas also examining the influence of suggested algorithms on the performance of cloud.This work attempts to discuss and review a number of approaches for using RSA in cloud computing, as well as to compare such approaches.
Security and Privacy Concerns for Blockchain While Handling Healthcare Data

Electronic health record (EHR) is a patient’s medical record that can be shared among multiple healthcare facilities, labs, and clinics to diagnose a disease. While sharing the EHR data, security and privacy become a matter of concern as the data are stored centrally. Also, the recent trend of storing EHR data on cloud has gained popularity as the data can be accessed anytime and anywhere. However, centralization of data in the cloud suffers from security and privacy issues. Blockchain technology is a decentralized approach in which blocks are linked together to create a chain of blocks. By design, blockchain is secure as the blocks in the blockchain are practically impossible to tamper with and provides privacy by supporting pseudonymity. Blockchain technology has a huge potential to secure healthcare data and preservation of patient privacy within the cloud environment. Many approaches have been proposed in the last few years to employ the blockchain technology for ensuring security and privacy of data in e-health systems. In this chapter, we present a systematic review of the literature of blockchain-based approaches for e-health systems with focus on e-health system security and privacy of patients’ records. Different research opportunities and practical challenges are identified while using the blockchain technology for ensuring security and privacy in e-health systems.