Reviewerly/exHarmony huggingface dataset:
Automatic Valuation of Essay using Machine Learning

Giving ratings to essays by humans is laborious and prone to mistakes. Although so much exploration is done to evaluate an essay, not so much is done to analyze an essay using syntax, semantic and sentiment analysis. Existing systems use several supervised and unsupervised methodologies to assess text coherence. Typically, unsupervised approaches assess lexical cohesiveness or the frequency with which phrases in an essay appear. It checks grammar, coherence, and semantics in a phrase. In the Essay, we have covered graph- based linkages and statement’s polarity opinion. To construct Graph-based spatial links and unique characteristics. There is a semantic resemblance established between every statement of the essay. The graph-based representation aids in the discovery of patterns and correlations within a set of data. The design and Information of documents can be constitute using a graph representation of text. Subsequently, the structure depends on little features which give a system more predictive power. Because of very few features, we can resist noisy data, and remove redundancy. We had taken some datasets from Kaggle and populated some data by taking different Essays from the Internet. We predict the score using XGBoosthis electronic document is a "live" template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.
Markup language for chemical process control and simulation

Exothermic reactions can yield different products, depending upon the available heat energy, a consequence of the reaction rate. To control the yield of desired products, control of the rate of temperature rise is used. The acceptable range of temperature vs. time can be expressed in various ways, e. g., functions, individual temperature values. To afford humans this variety, and to facilitate the transfer of data among computational tools from multiple vendors, such as calorimetry and analysis tools, use of semantic labels, compatible with existing tools, has been implemented. An extension of XML to support expression of the range of acceptable temperature behavior vs. time as a reaction proceeds is described herein. A markup language organized to express the rich ontology of concepts used in process safety seems warranted, and though chemistry schema exist, none was found that specified the reactor temperature boundaries concept. We propose schema components for this concept.
IMLD: A Python-Based Interactive Machine Learning Demonstration

The related fields of machine learning and pattern recognition have enjoyed significant success in recent years due to the impact of deep learning algorithms [1]. Pattern recognition is the automatic recognition of regularities or trends in data. Machine learning, a closely related field that has evolved considerably in the past two decades, refers to the ability of a machine to learn and adapt to data, improving a system's ability to detect patterns and perform inference. These methods are ubiquitous in engineering today impacting diverse fields including signal and image processing, human language technology, bioinformatics, and finance. The ISIP Machine Learning Demo (IMLD) is a tool used to introduce the basics of machine learning using a highly interactive environment in which users can easily visualize the performance of an algorithm. IMLD was first developed as a Java applet in the late-1990's when Java applets were envisioned as the future of interactive computing [2] and there was an emphasis on web-based educational tools [3]. The Institute for Signal and Information Processing, then located at Mississippi State University, developed a suite of interactive demos to teach important concepts in signal processing [4].
Heterogeneous Graph Based Knowledge Tracing

Recent advances in on-line tutoring systems have brought on an increase in the research of Knowledge Tracing, which predicts the student's performance on coursework exercises over time. Previous researches, such as Bayesian Knowledge Tracing, Deep Knowledge Tracing (DKT) and qDKT, focused on either skill-level or question-level. As a result, those methods fail to take question-skill correlations into account. Inspired by Heterogeneous Graph Embedding (HGE), We propose a HGE-based knowledge tracing model. In this paper, a heterogeneous graph is built on skill information and question information, so as to capture the latent interactions between skill nodes and question nodes. In the proposed method, the knowledge tracing model can leverage more informations than previous methods. The experimental results show that the proposed method outperforms other state-of-the-art methods centered on either skills or questions.
Downgrading Backward Privacy of Searchable Encryption

Searchable encryption allows users to outsource ciphertext with search capability. Although many searchable encryption schemes aiming at providing high efficiency with provable security have been proposed, traditional searchable encryption schemes have been also exploited by numerous types of attacks causing sensitive information leakage among the ciphertexts and queries. In order to deter such leakage of linkable information between the ciphertexts and queries, forward and backward-private searchable encryption schemes have been proposed. Especially, backward privacy is divided into three categories (Type-I, II, III) on the basis of the security level, which are determined by the types of leaked information during the protocol execution. In this paper, we introduce a novel backward privacy downgrade attack on the existing searchable encryption schemes. In order to demonstrate the efficacy of our attack, we show the backward privacy of the previous scheme, MITRA, can be downgraded from Type-II to Type-III.
Hierarchical Permissioned Blockchain and Traceability Of Requirement Changes

Requirement elicitation plays crucial part in success rates of a software project. Now a lot of project management methods are moving towards agile development which allows changes in requirement specification in any phase of project lifecycle. In a project there are many stakeholders. But not all of them has the same relevancy to the software requirements. Sometimes some requirement changes happen with wrong stakeholder source. These changes cause problem later. Such as unused function or missing an important function. This problem increases unnecessary cost for the project. Furthermore, change is inevitable in agile development. With frequent changes, it is difficult to track all the changes made. On the other hand, blockchain gives an immutable, traceable, decentralized platform where data can be added through consensus. In a hierarchical permissioned blockchain, the level of permission can be defined for each group or individual nodes through smart contract. Therefore, a hierarchical permissioned blockchain for stakeholder permission to make changes and traceability of mid-development requirement change is proposed. Here, it is proposed that there should be hierarchy among the stakeholders and levels of permission they have to change one or more part of the requirements according to the project needs. This control may be implemented with smart contracts and if needed, with intelligent agents as well. Also, with the immutability of blockchain and smart contracts and external databases, traceability will also be ensured.
Spatially Constrained GAN for Face and Fashion Synthesis

Image synthesis has raised tremendous attention in both academic and industrial areas, especially for conditional and target-oriented image synthesis, such as criminal portrait and fashion design. The current studies have achieved encouraging results along this direction, but they mostly focus on class labels where spatial contents are randomly generated from latent vectors. Some recent studies have explored spatial constraints for generative models guided by semantic segmentation, but most of them are designed for scene generation and lack random variation. Such methods are not suitable for face or fashion image synthesis, where different images may share the same semantics. Different from all the current methods, we decouple the image synthesis task into three independent dimensions and propose a novel Spatially Constrained Generative Adversarial Network (SCGAN) to model it. SCGAN uses a simple yet effective way to decouple spatial constraints and attribute conditions from latent vectors, and treat them as additional controllable signals via a segmentor and a specially designed generator. Other unregulated contents are left to be generated from latent vectors. Experimentally, we provide both qualitative and quantitative results on CelebA and DeepFashion datasets to demonstrate that the proposed SCGAN is very effective in synthesizing spatially controllable and attribute-specific images with high visual quality and large variations. Our code is provided at https://github.com/jackyjsy/SCGAN.
Kinship recognition: how far are we from viable solutions in smart environments?

Automated kinship recognition from face is a relatively recent problem that is mainly studied by the application of Deep Learning techniques. Despite the impact that an accurate kinship recognition algorithm can reach in a controlled environment, its applicability in smart environments is limited due to the degradation of performances. In this study we investigate the limitations of recent approaches that lead to a difficult applicability in a real case use. We present several tests on Siamese Neural Networks (SNN) based on a VGGFace architecture to solve both the kinship-vs-not-kinship recognition and the kind-of-kinship recognition. To perform our tests we used two popular kinship recognition Datasets that are Faces in the Wild and KinFace-II, respectively. To examine the behavior of the SNNs in a real scenario, we applied them, properly trained on the above mentioned datasets, to a popular TV show in which the aim is to discover kinship in a set of people. The weaknesses demonstrated in those tests have confirmed that the recent literature and algorithm to solve the kinship recognition problem are still far to achieve the high performances required in a smart environment.
Study of the effect of quantum noise on the accuracy of the Schrödinger equation simulation on a quantum computer using the Zalka-Wiesner method

The study of the effect of quantum noise on the accuracy of modeling quantum systems on a quantum computer using the Zalka-Wiesner method is carried out. The efficiency of the developed methods and algorithms is demonstrated by the example of solving the nonstationary Schrödinger equation with allowance for quantum noise for a particle moving in the Pöschl–Teller potential. The analysis and prediction of accuracy for the Zalka-Wiesner method are carried out taking into account the complexity of the quantum system and the strength of quantum noise. In our opinion, the considered problem provides a useful test for assessing the quality and efficiency of quantum computing devices on various physical platforms currently being developed. The obtained results are essential for the development of high-precision methods for controlling the technologies of quantum computations.
Distributed Estimation of Sparse Inverse Covariances

Learning the relationships between various entities from time-series data is essential in many applications. Gaussian graphical models have been studied to infer these relationships. However, existing algorithms process data in a batch at a central location, limiting their applications in scenarios where data is gathered by different agents. In this paper, we propose a distributed sparse inverse covariance algorithm to learn the network structure (i.e., dependencies among observed entities) in real-time from data collected by distributed agents. Our approach is built on an online graphical alternating minimization algorithm, augmented with a consensus term that allows agents to learn the desired structure cooperatively. We allow the system designer to select the number of communication rounds and optimization steps per data point. We characterize the rate of convergence of our algorithm and provide simulations on synthetic datasets.
Covert queueing problem with a Markovian statistic

Based on the covert communication framework, we consider a covert queueing problem that has a Markovian statistic. Willie jobs arrive according to a Poisson process and require service from server Bob. Bob does not have a queue for jobs to wait and hence when the server is busy, arriving Willie jobs are lost. Willie and Bob enter a contract under which Bob should only serve Willie jobs. As part of the usage statistic, for a sequence of N consecutive jobs that arrived, Bob informs Willie whether each job was served or lost (this is the Markovian statistic). Bob is assumed to be violating the contract and admitting non-Willie (Nillie) jobs according to a Poisson process. For such a setting, we identify the hypothesis testing to be performed (given the Markovian data) by Willie to detect the presence or absence of Nillie jobs. We also characterize the upper bound on arrival rate of Nillie jobs such that the error in the hypothesis testing of Willie is arbitrarily large, ensuring covertness in admitting Nillie jobs.
Face Aging using Generative Adversarial Networks

The goal of face aging is to change the face appearance according to different ages. To be specific, it is required to change the skin and the deeper soft tissues of the face. There are lots of different methods to solve the face aging task. In this paper, we mainly focus on applying Generative Adversarial Networks to change the face appearance. We compare and analyze the results of different face aging methods based on various Generative Adversarial Networks.
Image Tagging by Fine-tuning Class Semantics Using Text Data from Web Scraping

The image tagging task aims to assign relevant known tags to an image. It is an active research topic in computer vision and machine learning because of the diversity of its applications in semantic search and image retrieval. Earlier efforts on image tagging address this problem as a multi-level classification problem using visual features from images and semantic word vectors of tags. In most cases, a pre-trained language model like word2vec or Globe is used to obtain those word vectors. Because of using a pre-trained language model, an image tagging approach cannot scale itself to the context of the targeted application. This paper fine-tunes a language (BERT) model using text descriptions obtained from web (Wikipedia) scraping to learn a rich distributed representation of tags. Then, we employ word vectors of tags extracted from finetuned language (BERT) model to solve the image tagging task. Our method is more specialized to the particular application by incorporating context information between targeted tags and images. As a result, word vectors obtained from the fine-tuned model perform better than those from pre-trained language models. We evaluate our method on the widely used NUS-WIDE dataset and achieve competitive results compared with state-of-the-art methods.
Comparison of GLCM based Hand Gesture Recognition Systems using Multiple Classifiers

Automatic human gesture recognition system serves the purpose of human-computer interaction (HCI). In this paper, we propose a hand gesture recognition method based on multiple classifiers through feature extraction. For this, we have employed two datasets of hand gesture images. On images, different pre-processing, segmentation and morphological operations have been performed. Gray Level Co-occurrence Matrix (GLCM) based statistical features are then extracted. The hand gesture recognition is then performed by using KNN classifier, decision trees (DT), and Support Vector Machines (SVM). The proposed methodology is effective as it gives high recognition accuracies on both datasets. This study also pays heed to compare the accuracy of the three machine learning methods.
Quantum measurements and high-precision control of quantum states

The report presents a general approach for estimating quantum information technologies by means of fuzzy quantum measurements. The developed methods are used for precision reconstruction of quantum states under conditions of significant influence of decoherence and quantum noise. Monitoring of the amount of information about quantum states and their parameters, including the loss of information caused by the effect of quantum noise, is considered. Examples of the developed approach as applied to readout errors and phase relaxation are presented.
Exploring Multi-Layer Convolutional Neural Networks for Railway Safety Text Classification

With the rapid development of China High-Speed Rail, massive text data related to railway safety has been accumulated. When analyzing and understanding this data, classifying railway accident report text is essential and tedious work. Usually, such classification tasks are manually done by experts and workers in the railway safety department. Traditional data mining algorithms have been applied in these tasks to classify the text automatically. However, due to the complexity of the text data, classification algorithms sometimes fail and have insufficient learning ability. Meanwhile, the rise of machine learning enables us to deal with these complex problems effectively. In this paper, we propose an end-to-end multi-layer convolutional neural networks model to classify the railway safety-related text. We update the CNN part of the traditional model by increasing layers and adding a multi-height convolutional kernel. Additionally, we develop a data-preprocessing strategy to obtain the neat input data and reduce the complexity of the task. Experiments show that our proposed method achieves competitive performance and is suitable for railway safety-related text classification problems.
Designing and Piloting of a Mobile Learning Curriculum for Quality Point-Of-Care Diagnostics Services in Rural Clinics of KwaZulu-Natal, South Africa

The use of mobile technology has been reported to help improve access to education for people in remote areas. However, there is limited evidence of its adoption in resource-limited settings. The aim of this study was to utilize stakeholder generated ideas to design and pilot a mobile learning curriculum, with the purpose of facilitating training to improve the quality of point-of-care diagnostics services in KwaZulu-Natal (KZN) rural clinics.Nominal Group Technique was employed to enable collaboration with stakeholders in designing and piloting of a POC diagnostics curriculum. Stakeholders were selected from 11 KZN districts to participate in a clinic-based piloting of the curriculum using an online application. The application was designed in collaboration with a teacher training institute in Durban. Moodle was used as an established reliable online learning management system. During piloting, quantitative and qualitative data were generated and analyzed using descriptive statistics and content analysis.Guided by the Nominal Group Technique results, five delivery modes for curriculum content through Mobile Learning were generated. An interactive course page was created on the Moodle site, titled: Quality HIV Point of Care Diagnostics Curriculum Delivery for Nurses in Rural Areas. The course content consisted of three teaching units, activities, an online quiz and an online survey. An analytic-algorithm built into the online course enabled monitoring of participation and assessment outcomes automatically. At piloting, 64% of the invited representative clinics were able to access the course, with 47% meeting the course completion requirements. All the participants achieved the set pass mark of 75% with an average of 87%. The activity completion report showed that topics presented through images, videos and simple text were accessed more than those presented as attachments of national documents. Despite poor network coverage and limited access to mobile technology, exacerbated by Covid-19 related restrictions, Point of care diagnostics Mobile Learning curriculum was well-received in participating rural clinics. Recommendations relating to course improvement and access, included extending collaboration with specialists in eHealth systems development and with South African cell phone network providers.
Design of Identity Authentication Scheme for Dynamic Service Command System Based on SM2 Algorithm and Blockchain Technology

To ensure the information security of the dynamic service command system and prevent all kinds of illegal access requests, solve the privacy leakage problem of the command and decision makers in the identification process, it is necessary to conduct strong and secure identity authentication for the personnel who access the dynamic service command system to ensure the system For the security. An identity authentication scheme based on national secret algorithm and blockchain technology is proposed. The scheme creates a user’s authentication block through a consensus mechanism, and uses a dynamic password to combine face recognition with a QR code for security authentication of identity. At the same time, the effectiveness and security of the scheme are studied and analyzed, which shows that the scheme can ensure the privacy of users while providing efficient identity authentication.
Introduction

Abstract This editorial introduction establishes the theoretical basis for the special issue Formality and Informality in Online Performances with a focus on (in)formality, Goffman’s (1959) notion of front– and backstage and the internet as a stage, as well as the performance of self. Further attention is given to notions of self-branding, micro-celebrity, and self-disclosure. Explicit discussion of the linguistic construction of formality and informality in performative online settings has been rare and the articles in this issue aim at investigating the complex interplay between context, (in)formality, and performance on different digital platforms (ranging from rather traditional web forums to YouTube, Reddit, Instagram, and WeChat Moments). The contributions thus shed light on some of the intricate performative processes which are at the heart of online communication and offer manifold perspectives on (in)formality online.
Template Attack Assisted Linear Cryptanalysis on Outer Rounds Protected DES Implementations

Abstract In practice, when the security of a block cipher implementation is considered, the leakages related to the outer rounds encryptions can be used by side channel attacks (SCA) to recover the secret key. Therefore, the outer rounds of block cipher implementations should be protected. However, in order to lower the implementation price, the inner rounds of block cipher implementations may be unprotected. In light of this, the security of an outer rounds protected DES implementation is considered. In detail, template attack (TA), which is information theoretically the strongest SCA style, can be used to obtain the inner round output. Then, linear cryptanalysis (LC) can be used to recover the secret key. Finally, the optimal key enumeration algorithm can be used to optimize the efficiency of TA assisted LC. We evaluate the efficiency of TA assisted LC in simulated scenarios where a three outer rounds protected DES implementation is targeted. The evaluation results show that when 800 correct samples are available and the number of key enumeration is $2^{10}$, the efficiency of TA assisted LC can reach 83% of success rate. Overall, an efficient combination attack style that can be used to accurately evaluate the security of an outer rounds protected DES implementation is proposed.