Reviewerly/exHarmony huggingface dataset:
Review of Practices of Collecting and Annotating Texts in the Learner Corpus REALEC

REALEC, learner corpus released in the open access, had received 6,054 essays written in English by HSE undergraduate students in their English university-level examination by the year 2020. This paper reports on the data collection and manual annotation approaches for the texts of 2014–2019 and discusses the computer tools available for working with the corpus. This provides the basis for the ongoing development of automated annotation for the new portions of learner texts in the corpus. The observations in the first part were made on the reliability of the total of 134,608 error tags manually annotated across the texts in the corpus. Some examples are given in the paper to emphasize the role of the interference with learners’ L1 (Russian), one more direction of the future corpus research. A number of studies carried out by the research team working on the basis of the REALEC data are listed as examples of the research potential that the corpus has been providing.
Creating Structure in Web Archives with Collections: Different Concepts from Web Archivists

As web archives' holdings grow, archivists subdivide them into collections so they are easier to understand and manage. In this work, we review the collection structures of eight web archive platforms. We note a plethora of different approaches to web archive collection structures. Some web archive collections support sub-collections and some permit embargoes. Curatorial decisions may be attributed to a single organization or many. Archived web pages are known by many names: mementos, copies, captures, or snapshots. Some platforms restrict a memento to a single collection and others allow mementos to cross collections. Knowledge of collection structures has implications for many different applications and users. Visitors will need to understand how to navigate collections. Future archivists will need to understand what options are available for designing collections. Platform designers need it to know what possibilities exist. The developers of tools that consume collections need to understand collection structures so they can meet the needs of their users.
CDX Summary: Web Archival Collection Insights

Large web archival collections are often opaque about their holdings. We created an open-source tool called, CDX Summary, to generate statistical reports based on URIs, hosts, TLDs, paths, query parameters, status codes, media types, date and time, etc. present in the CDX index of a collection of WARC files. Our tool also surfaces a configurable number of potentially good random memento samples from the collection for visual inspection, quality assurance, representative thumbnails generation, etc. The tool generates both human and machine readable reports with varying levels of details for different use cases. Furthermore, we implemented a Web Component that can render generated JSON summaries in HTML documents. Early exploration of CDX insights on Wayback Machine collections helped us improve our crawl operations.
Taekwondo Trajectory Tracking Based on Multitarget Detection Algorithm

In order to further improve the effect of fuzzy PID controller applied to joint robot trajectory tracking control, a Taekwondo trajectory tracking method based on multitarget detection algorithm is proposed in this study. Firstly, a fuzzy PID controller for trajectory tracking of joint robot is designed. Secondly, considering the two optimization objectives of controller output torque and trajectory tracking control deviation, an improved multiobjective PSO algorithm is designed to optimize the membership function and fuzzy rules of fuzzy PID controller. Finally, the multiobjective PSO algorithm and the improved multiobjective PSO algorithm are used to optimize the trajectory tracking fuzzy PID controller. The vector sets of the two optimization objectives are obtained, and the optimization results are compared and analyzed. The simulation results show that the simulation step length is t = 0.01 s, and the total simulation time is 10 s. Using the controller, the simulation values and output torque values of each joint angle at each time can be obtained, which are used to calculate the two objective function values in the optimization algorithm. According to the process, the optimization algorithm parameters are set, and the control simulation system designed above is run to complete the improved multiobjective PSO optimization of the trajectory tracking fuzzy PID controller of the joint robot. Conclusion. The improved multiobjective PSO algorithm has better nondominated solution set, which verifies the effectiveness and superiority of the algorithm to optimize the robot trajectory tracking fuzzy PID controller.
Developing a Model for the Automated Identification and Extraction of Agricultural Terms from Unstructured Text

Text is the prevalent medium for conveying research findings and developments within and beyond the domain of agriculture. Mining information from text is important for the (research) community to keep track of the most recent developments and identify solutions to major agriculture-related challenges. The task of Named Entity Recognition (NER) can be a first step in such a context. The work presented in this paper relates to a custom NER model for the automated identification and extraction of agricultural terms from text, built on Python&rsquo;s spaCy library. The model has been trained on a manually annotated text corpus taken from the AGRIS database, and its performance depending on different model configurations is presented. We note that due to the domain ambiguity, inter-annotator agreement and model performance can be improved.
Virtual Representations for Cybertherapy

The development of serious games has enabled new challenges for the healthcare sector in psychological, cognitive, and motor rehabilitation. Thanks to virtual reality, stimulating and interactive experiences can be reproduced in a safe and controlled environment. This chapter illustrates the experimentation conducted in the hospital setting for the non-pharmacological treatment of cognitive disorders associated with dementia. The therapy aims to relax patients of the agitation cluster through a gaming approach through the immersion in multisensory and natural settings in which sound and visual stimuli are provided. The study is supported by a technological architecture including the virtual wall system for stereoscopic wall projection and rigid body tracking.
Research on consumers' personal information security and perception based on digital twins and Internet of Things

In order to solve the problem of personal information leakage caused by online shopping, the researchers propose a consumer personal information security and its perception based on digital twin and Internet of things. This paper takes the online shopping process as the main line, obtains the information security problems existing in each link through the analysis of each shopping link. Analyze the interactions and interactions between digital twins and the big data industry supporting them, cloud computing, artificial intelligence, and online commerce. The results show that among all respondents, the perception of personal information security in the process of online shopping is "uncertain", accounting for 40.78%. Secondly, the proportion of "agree" and "fully agree" information security is 38.47% and 9.54% respectively, while the proportion of "completely disagree" personal information security is only 1.47%, which shows that consumers are more confident in personal information security in online shopping, and also reflects consumers' lack of perception of existing threats in online shopping. This shows that a considerable number of consumers believe that the online shopping cart and order information are safe, showing their trust in the online shopping website.
Spam and Ham Classification by Multinomial Naïve Bayes Classification in Text Data

Attacking mobile phones is increasingly alarming, especially infringing through many applications. Attacking is being done through clicking hyperlinks, which are transmitted through messaging systems as content. The Mobile phones literally are being made un-operational through these kinds of cyber-attacks. This project aims to build machine learning model that learns to detect the attacking through malware caused within messaging that contains different kinds of sources that include text, video and audio. The model aims to learn the attacking caused through malware and then triggers an action that counters the attack based on its type.
Ensemble Method of Feature Selection Using Filter and Wrapper Techniques with Evolutionary Learning

The improvement in data collection and mining methods has expanded the range of dimensionality or features in the data, which brings about an obstacle to many existing feature selection methodologies. This paper brings forth a fresh feature selection methodology rooted in particle swarm optimization (PSO) as wrapper method and an ensemble method to merge the results of the different filter techniques (chi-square, F-regression, and mutual information) to find an optimal feature set that covers most of the key variables of the dataset. The local search is executed on the global best and makes use of a filter-based method, which then intends to take the advantage of the filter and wrapper methods. Our results exhibit that the proposed methodology can be successfully used to select fewer features and, at the same time, increase the classification efficiency over using all features. The proposed methodology also shows how well an evolutionary learning algorithm like the particle swarm optimizer can be used for search optimization of optimum features in the dataset.
A Novel Technique For Enhancing Underwater Visibility Using Non-Local Stretch Directional Gradient

Abstract Owing to the many effects of the underwater medium, underwater images are often captured with a hazy quality. These effects are caused by suspended particles in the image forming phase, which cause light absorption and scattering. On account of these pictures with fluctuating levels of dimness and colour variation, traditional algorithms have several disadvantages. Underwater image enhancement’s main goal is to increase the clarity and contrast of underwater photographs in order to solve these issues. Our paper describes a novel technique by combining several algorithms characteristics for achieving the following goals: i) better scattering, (ii) grey world assumption, (iii) non-Local stretch Directional Gradient process. For example, a better scattering model would be better at modelling hazy scenes and overcoming the image illumination dimming effect. Second, for uniform visibility recovery on the captured picture, an appropriate gray world-assumption is used. Finally, the Non-Local stretch and Directional gradient filter work on the edge details and overall dual tone enhancement required for a high-quality picture. Simulation experiments indicate that the proposed methodology produces improved visual effects and has a greater impact on consistency metrics than other methods.
An Application Study on the Electronic Micro-Lesson Based English Drama in English Teaching of Chinese Public Junior High Schools

At present, the situation of "dumb" English in public junior middle schools in China is still severe. In order to improve this situation, the micro-lessons of English drama were introduced into the extracurricular activities of 85 students in two classes of Grade 7 in a public middle school in Hangzhou, and a 10-week comparative experiment was carried out. The students in the experimental class, who were divided into two groups for a daily 1-hour role-play performance every afternoon concerning the micro-lessons of English drama, performed an English short play every 2-3 weeks and conducted online group PK. It is found that the spoken English performance of the experimental class with the micro-lesson practice mode of English drama is improved by 55%, while that of the control class with the conventional learning method is only improved by 1.38%. The practice method of English drama micro-lesson is very popular among students, which can stimulate students' interest and self-confidence in spoken English learning, effectively promote the improvement of oral English ability, and cultivate the spirit of teamwork. This result can provide a reference for the reform of English teaching in middle schools in China.
Untangling Composite Commits by Attributed Graph Clustering

During software development, it is considered to be a best practice if each commit represents one distinct concern, such as fixing a bug or adding a new feature. However, developers may not always follow this practice and sometimes tangle multiple concerns into a single composite commit. This makes automatic commit untangling a necessary task, and recent approaches mainly untangle commits via applying graph clustering on the code dependency graph. In this paper, we propose a new commit untangling approach, ComUnt, to decompose the composite commits into atomic ones. Different from existing approaches, ComUnt is built upon the observation that both the textual content of code statements and the dependencies between code statements contain useful semantic information so as to better comprehend the committed code changes. Based on this observation, ComUnt first constructs an attributed graph for each commit, where code statements and various code dependencies are modeled as nodes and edges, respectively, and the textual body of code statements are maintained as node attributes. It then conducts attributed graph clustering on the constructed graph. The used attributed graph clustering algorithm can simultaneously encode both graph structure and node attributes so as to better separate the code changes into clusters with distinct concerns. We evaluate our approach on nine C# projects, and the experimental result shows that ComUnt improves the state-of-the-art by 7.8% in terms of untangling accuracy, and meanwhile it is more than 6 times faster.
Incorporating Pre-trained Transformer Models into TextCNN for Sentiment Analysis on Software Engineering Texts

Software information sites (e.g., Jira, Stack Overflow) are now wide-ly used in software development. These online platforms for collaborative development preserve a large amount of Software Engineering (SE) texts. These texts enable researchers to detect developers’ attitudes toward their daily development by analyzing the sentiments expressed in the texts. Unfortunately, recent works reported that neither off-the-shelf tools nor SE-specified tools for sentiment analysis on SE texts can provide satisfying and reliable results. In this paper, we propose to incorporate pre-trained transformer models into the sentence-classification oriented deep learning framework named TextCNN to better capture the unique expression of sentiments in SE texts. Specifically, we introduce an optimized BERT model named RoBERTa as the word embedding layer of TextCNN, along with additional residual connections between RoBERTa and TextCNN for better cooperation in our training framework. An empirical evaluation based on four datasets from different software information sites shows that our training framework can achieve overall better accuracy and generalizability than the four baselines.
Spoken Language Identification of Indian Languages in Adversarial Synthetic and Noisy Attacking Environments

During the COVID19 epidemic, the need for contact-less biometric ASV systems is at an all-time high. As a result, voice technology based ASV systems are in more demand. In current age of Artificial Intelligence, a variety of spoofing attacks poses a significant front end threat to these systems. Such units are also constantly exposed to noise settings. In these adversarial environments, a spoken language identification module in multi-lingual systems should provide better performance. As a result, the goal of this research is to resolve the ambiguity in identifying spoken language in noisy and synthetic voice spoofing attacks. In this paper, fused CQCC-MFCC feature set is combined with a Convolutional Neural Network(CNN) for increasing performance of language detection in artificial voice attack in a noisy environment. Nine Indian languages considered here are Bengali,Gujarati,Hindi,Malayalam, Manipuri,Odia,Rajasthani,Tamil and Telugu. It was found to have a 97 percent accuracy on the INDIC TTS Database. The use of CQCC in tandem with MFCC improves accuracy by 1 % as compared to using only MFCC features.
Analysis of Histopathological images: An Overview

Histopathology is the study of change in tissues and cells affected by the disease and finding the root cause of the disease. Over recent years there is huge improvement in image analysis algorithms as well as in the computation power. In this paper we will review various techniques given by different authors for histopathological image analysis.Also we will cover the various methods for image preprocessing, segmentation, feature extraction and classification which are basic steps of histopathological image analysis.
Browser Extension For A Safe Browsing Experience

Due to the rise of the internet a business model known as online advertising has seen unprecedented success. However, it has also become a prime method through which criminals can scam people. Often times even legitimate websites contain advertisements that are linked to scam websites since they are not verified by the website’s owners. Scammers have become quite creative with their attacks, using various unorthodox and inconspicuous methods such as I-frames, Favicons, Proxy servers, Domains, etc. Many modern Anti-viruses are paid services and hence not a feasible option for most users in 3rd world countries. Often people don’t possess devices that have enough RAM to even run such software efficiently leaving them without any options. This project aims to create a Browser extension that will be able to distinguish between safe and unsafe websites by utilizing Machine Learning algorithms. This system is lightweight and free thus fulfilling the needs of most people looking for a cheap and reliable security solution and allowing people to surf the internet easily and safely. The system will scan all the intermittent URL clicks as well, not just the main website thus providing an even greater degree of security.
Sentence Similarity - A State of Art Approaches

This paper draws up a summary of the different approaches to sentence similarity methods. Sentence similarity has a important place in different applications of natural language processing. Plagiarism check, question answering, information retrieval, text summarization, text classification are some of the natural language processes where sentence similarity finds its applications. The existing works on text similarity have been discussed by approaching through 3 methods: String-based, Knowledge-based, and Corpus-based similarity. Relatedness between short texts is measured by each approach based on a specific perspective. Moreover, the introduction of datasets that are mostly used as benchmarks for the evaluation of techniques in this field provides an absolute view on this issue. Better results are obtained when approaches that combine more than one perspective are used.
Forensic Analysis of Private Mode Browsing Artifacts in Portable Web Browsers Using Memory Forensics

The popularity of portable web browsers is increasing due to its convenient and compact nature along with the benefit of the data being stored and transferred easily using a USB drive. As technology gets updated frequently, developers are working on web browsers that can be portable in nature with additional security features like private mode browsing, built in ad blockers etc. The increased probability of using portable web browsers for carrying out nefarious activities is a result of cybercriminals with the thought that if they use portable web browsers in private mode it won't leave a digital footprint. Hence, the research paper aims at performing a comparative study of four portable web browsers namely Brave, TOR, Vivaldi, and Maxthon along with various memory acquisition tools to understand the quantity and quality of the data that can be recovered from the memory dump in two different conditions that is when the browser tabs were open and when the browser tabs were closed in a system to aid the forensic investigators.
IoT Based Emergency Communication System for Medical Transportation

In developing countries such as India, the need for improved health care facilities and transportation is of great significance. Many village areas in our country do not have sufficient transportation facilities or hospitals nearby, which could be dangerous. Providing an efficient monitoring solution in basic ambulances to improve their functionality can be lifesaving. In times of covid, this system can be used to monitor vitals even from the comfort of our home, and telepresence with a professional can bring a momentous change in our society. This is the essence of our Product. Our Product monitors the various health parameters such as oxygen level, blood pressure, pulse rate, heartbeat rate, temperature, and other health parameters of the patient in the ambulance. The data is then processed by using raspberry pi and Arduino. All the processed data can be viewed by the hospital staff in the ambulance with an interface so that he/she can give emergency first aid. All the sensor data is also sent to the hospital through IoT platforms which helps them in preparing the necessary treatment facilities. A video calling facility is also developed for efficient communication between the patient and the doctor. The real-time location of the ambulance is also sent to the hospital along with the other data. So, if there is any abnormality in the parameters it can be known beforehand and an alert is provided for taking necessary actions.
A Comparative Analysis of Tweets from the South Indian States based on COVID-19 Omicron wave

COVID-19 pandemic had shaken societies in the world at various levels. Most organizations were forced to adopt the Work from Home strategy from 2020 onwards. This had reportedly increased internet and social media usage among the people. India was also not different from the rest of the world. The nation was affected by the second and third waves of the COVID-19 pandemic. Among them, Kerala and Tamil Nadu were some of the affected states in the nation; with Tamil Nadu reporting fewer positive cases and Test Positive Rate (TPR) compared to that Kerala. This paper focuses on the attitude of people towards the pandemic by extracting tweets from Twitter. A comparative study between the two states was conducted. After analyzing the rumors and information shared by the people through tweets, it has been identified that the people of Tamil Nadu were a bit more conscious than the people in Kerala. The study also reveals that people in Kerala were not vigilant during the Omicron wave as compared to the time before.