Reviewerly/exHarmony huggingface dataset:
GJG@TamilNLP-ACL2022: Using Transformers for Abusive Comment Classification in Tamil

This paper presents transformer-based models for the "Abusive Comment Detection" shared task at the Second Workshop on Speech and Language Technologies for Dravidian Languages at ACL 2022.Our team participated in both the multi-class classification sub-tasks as a part of this shared task.The dataset for sub-task A was in Tamil text; while B was codemixed Tamil-English text.Both the datasets contained 8 classes of abusive comments.We trained an XLM-RoBERTa and DeBERTA base model on the training splits for each subtask.For sub-task A, the XLM-RoBERTa model achieved an accuracy of 0.66 and the DeBERTa model achieved an accuracy of 0.62.For sub-task B, both the models achieved a classification accuracy of 0.72; however, the DeBERTa model performed better in other classification metrics.Our team ranked 2 nd in the code-mixed classification sub-task and 8 th in Tamil-text sub-task.
A Quantile-Based Watermarking Approach for Distortion Minimization

Abstract Distortion-based watermarking techniques embed the watermark by performing tolerable changes in the digital assets being protected. For relational data, mark insertion can be performed over the different data types of the database relations’ attributes. An important goal for distortion-based approaches is to minimize as much as possible the changes that the watermark embedding provokes into data, preserving their usability, watermark robustness, and capacity. This paper proposes a quantile-based watermarking technique for numerical cover type focused on preserving the distribution of attributes used as mark carriers. The experiments performed to validate our proposal show a significant distortion reduction compared to traditional approaches while maintaining watermark capacity levels. Also, positive achievements regarding robustness are visible, evidencing our technique’s resilience against subset attacks.
Accuracy of Explanations of Machine Learning Models for Credit Decision

One of the biggest challenges for the application of machine learning (ML) models in finance is how to explain their results. In recent years, different interpretability techniques have appeared to assist in this task, although their usefulness is still a matter of debate. In this article we contribute to the debate by creating a framework to assess the accuracy of these interpretability techniques. We start from the generation of synthetic data sets, following an approach that allows us to control the importance of each explanatory variable (feature) in our target variable. By defining the importance of features ourselves, we can then calculate to what extent the explanations given by the interpretability techniques match the underlying truth. Therefore, if in our synthetic dataset we define a feature as relevant to the target variable, the interpretability technique should also identify it as a relevant feature. We run an empirical example in which we generate synthetic datasets intended to resemble underwriting and credit rating datasets, where the target variable is a binary variable representing applicant default. We then use non-interpretable ML models, such as deep learning, to predict default, and then explain their results using two popular interpretability techniques, SHAP and permutation Feature Importance (FI). Our results using the proposed framework suggest that SHAP is better at interpreting relevant features as such, although the results may vary significantly depending on the characteristics of the dataset and the ML model used. We conclude that generating synthetic datasets shows potential as a useful approach for supervisors and practitioners looking for solutions to assess the interpretability tools available for ML models in the financial sector.
giniUs @LT-EDI-ACL2022: Aasha: Transformers based Hope-EDI

This paper describes team giniUs' submission to the Hope Speech Detection for Equality, Diversity and Inclusion Shared Task organised by LT-EDI ACL 2022.We have fine-tuned the RoBERTa-large pre-trained model and extracted the last four Decoder layers to build a binary classifier.Our best result on the leaderboard achieves a weighted F1 score of 0.86 and a Macro F1 score of 0.51 for English.We rank fourth in the English task.We have opensourced our code implementations on GitHub to facilitate easy reproducibility by the scientific community.
DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resource Entity Extraction Using Clinical Trials Literature

PICO recognition is an information extraction task for identifying participant, intervention, comparator, and outcome information from clinical literature.Manually identifying PICO information is the most time-consuming step for conducting systematic reviews (SR), which is already labor-intensive.A lack of diversified and large, annotated corpora restricts innovation and adoption of automated PICO recognition systems.The largest-available PICO entity/span corpus is manually annotated which is too expensive for a majority of the scientific community.To break through the bottleneck, we propose DISTANT-CTO, a novel distantly supervised PICO entity extraction approach using the clinical trials literature, to generate a massive weakly-labeled dataset with more than a million "Intervention" and "Comparator" entity annotations.We train distant NER (namedentity recognition) models using this weaklylabeled dataset and demonstrate that it outperforms even the sophisticated models trained on the manually annotated dataset with a 2% F1 improvement over the Intervention entity of the PICO benchmark and more than 5% improvement when combined with the manually annotated dataset.We investigate the generalizability of our approach and gain an impressive F1 score on another domain-specific PICO benchmark.The approach is not only zero-cost but is also scalable for a constant stream of PICO entity annotations.
An Approach to Convert Compound Document Image to Editable Replica

Various OCR techniques are available to extract text from the image, although these techniques fail to recreate the original document regarding its layout and text formatting. The document image contains components like text, tables, and pictures that pose challenges in extracting them separately and placing them at their correct positions. The said need and challenges are addressed in this paper by proposing a methodology that constructs a suitable feature vector. It extracts the components (text, tables, pictures) from a compound document image which contains multiple text blocks, pictures, and/or table components, for reconstructing the document in an editable format (LaTeX). The strength of the feature vector relies on the clarity of the source image. Robustness and accuracy of the editable reconstruction of a compound document are measured by analysis of feature vector.
Abnormal Event Detection using Additive Summarization Model for Intelligent Transportation Systems

Video surveillance is used for capturing the abnormal events on roadsides that are caused due to improper driving, accidents, and hindrances resulting in transportation lags and life-critical issues.It is essential to highlight the accident keyframes in videos to achieve intelligent video surveillance.Video summarization plays a vital role in summarizing the keyframe for an abnormal event from the stacked video surveillance input.The observed video is converted into frames and analyzed for providing an accurate summarization for accident analysis forecast and guiding the users in avoiding such events.The main issues in summarization arise from the inconsistency between the spatiotemporal redundancies and the classification of sequence verification in video surveillance.This article introduces an Additive Event Summarization Method (AESM) for projecting classified events through a gated recurrent unit learning paradigm.In this process, the gates are assigned for unclassified and active frames for sequence verification.Based on the sequence, the abnormality is classified and summarized with higher accuracy than the state of art techniques.This proposed method relies on heterogeneous features for classifying events with better structural indices.The proposed method's performance is analyzed using the metrics accuracy, false rate, analysis time, SSIM, and F1-Score.
Evaluation of Individual Innovative Characteristics and Problem-Solving Skills of Students

Purpose:The purpose of this study is to evaluate the individual innovative characteristics and problem-solving skills of students who are studying in health sciences.Methods: This research is descriptive.A sample of 608 students who were at school at the time of collection of the study data and who agreed to participate in the survey were included.Three data collection methods were employed: 1) A socio-demographic questionnaire, 2) Individual Innovativeness Scale, 3) Problem Solving Inventory. Results:The individual innovativeness level score of the students participating in the study was 67.13 ± 8.19; and the problem-solving skill score was 89.82 ± 19.23. Conclusions:The students perceived their individual innovativeness level in the questionable category and perceived themselves as moderately competent in problem solving skills.The positive relationship between these two skills suggests that plans for the development of these skills in education should be made.
Visual Language of Communication: Comics, Memes, and Emojis

Emojis are rapidly becoming an integral part of our written language (or the ‘typed’ language) if they haven’t done so yet. Memes and GIFs are not far behind since their inclusion into the keypad of our smartphones. The present paper employs a linguistic approach to discuss the widespread use of memes and emojis in everyday text communication. In this paper, I have argued that despite many differences in application, comics, memes, and emojis all have a distinct similarity of being ‘visual’ in nature.I have shown with examples that instead of competing to replace each other, these verbal elements (words) and visual (images) elements complement the text together. The textual and pictorial elements of such textual-visual language make writing an even more efficient process of thought representation and a wholesome means for communication.
Can Synthetic Translations Improve Bitext Quality?

Synthetic translations have been used for a wide range of NLP tasks primarily as a means of data augmentation.This work explores, instead, how synthetic translations can be used to revise potentially imperfect reference translations in mined bitext.We find that synthetic samples can improve bitext quality without any additional bilingual supervision when they replace the originals based on a semantic equivalence classifier that helps mitigate NMT noise.The improved quality of the revised bitext is confirmed intrinsically via human evaluation and extrinsically through bilingual induction and MT tasks.
Product Titles-to-Attributes As a Text-to-Text Task

Online marketplaces use attribute-value pairs, such as brand, size, size type, color, etc. to help define important and relevant facts about a listing.These help buyers to curate their search results using attribute filtering and overall create a richer experience.Although their critical importance for listings' discoverability, getting sellers to input tens of different attribute-value pairs per listing is costly and often results in missing information.This can later translate to the unnecessary removal of relevant listings from the search results when buyers are filtering by attribute values.In this paper we demonstrate using a Text-to-Text hierarchical multilabel ranking model framework to predict the most relevant attributes per listing, along with their expected values, using historic user behavioral data.This solution helps sellers by allowing them to focus on verifying information on attributes that are likely to be used by buyers, and thus, increase the expected recall for their listings.Specifically for eBay's case we show that using this model can improve the relevancy of the attribute extraction process by 33.2% compared to the current highlyoptimized production system.Apart from the empirical contribution, the highly generalized nature of the framework presented in this paper makes it relevant for many high-volume search-driven websites.
Adaptive Neural Task Space Control for Robot Manipulators With Unknown and Closed Control Architecture Under Random Vibrations

Robot manipulators are now used in various domains and environments, where they can be subjected to random vibrations. Random vibrations mainly affect the torque control signal, and a torque controller is therefore required to be designed for stabilization purposes. However, for security or intellectual property protection reasons, most commercialized robots are manufactured with unknown and inaccessible torque controller interface such that the user can only design a position/velocity controller. This paper proposes an adaptive task-space velocity controller free from the inner controller&#x2019;s structure and exhibiting stochastic and deterministic disturbances rejection to deal with these issues. To deal with the unknown inner controller, the paper exploits the fact that most torque controllers use a velocity feedback term, and it considers the other terms as an unknown functions vector. To cope with random disturbances, it is demonstrated that the random excitation matrix can be linearly parameterized, and therefore, a direct adaptive method is constructed. Using radial basis function neural network (RBF NN), an indirect adaptive method is developed to cope with deterministic uncertainties. Through Lyapunov theory, the paper proves that all the closed-loop signals are bounded in probability. The effectiveness of the proposed approach is further demonstrated through simulation comparisons.
Sign Language Recognition: High Performance Deep Learning Approach Applyied To Multiple Sign Languages

In this paper we present a high performance Deep Learning architecture based on Convolutional Neural Network (CNN). The proposed architecture is effective as it is capable of recognizing and analyzing with high accuracy different Sign language datasets. The sign language recognition is one of the most important tasks that will change the lives of deaf people by facilitating their daily life and their integration into society. Our approach was trained and tested on an American Sign Language (ASL) dataset, Irish Sign Alphabets (ISL) dataset and Arabic Sign Language Alphabet (ArASL) dataset and outperforms the state-of-the-art methods by providing a recognition rate of 99% for ASL and ISL, and 98% for ArASL.
Real-Time Human Activity Recognition in Smart Home on Embedded Equipment: New Challenges

Abstract Building Energy Management (BEM) and monitoring systems should not only consider HVAC systems and building physics but also human behaviors. These systems could provide information and advice to occupants about the significance of their practices with regard to the current state of a dwelling. It is also possible to provide services such as assistance to the elderly, comfort and health monitoring. For this, an intelligent building must know the daily activities of its residents and the algorithms of the smart environment must track and recognize the activities that the occupants normally perform as part of their daily routine. In the literature, deep learning is one of effective supervised learning model and cost-efficient for real-time HAR, but it still struggles with the quality of training data (missing values in time series and non-annotated event), the variability of data, the data segmentation and the ontology of activities. In this work, recent research works, existing algorithms and related challenges in this field are firstly highlighted. Then, new research directions and solutions (performing fault detection and diagnosis for drift detection, multi-label classification modeling for multi-occupant classification, new indicators for training data quality, new metrics weighted by the number of representations in dataset to handle the issue of missing data and finally language processing for complex activity recognition) are suggested to solve them respectively and to improve this field.
Random Sequential Adsorption of Shapes with Random Geometry: the Case of Rectangles with Random Aspect Ratio

In a previous paper, we studied Random Sequential Adsorption of rectangles with the constant area but random aspect ratio.Here, we investigate the statistical distribution of the density at saturation, the hyperuniformity of the packing, and revisit the analysis of the Available Surface Function and Feder's law.We also evaluate the performance of the algorithm used in the simulations.
Virtual Museum Visits in a Pandemic: Older Adults Discuss Experiences of Art, Culture and Social Connection

This paper focuses on a series of guided virtual museum visits designed for older adults over the COVID-19 pandemic. The visits were undertaken as part of a research project in collaboration with the Montreal Museum of Fine Arts (MMFA) and brought together small groups of older adults for weekly guided visits facilitated by trained guides. The visits were held for twelve weeks between March and May 2021. We conducted a qualitative study on the visits, which included weekly observations as well as interviews with the older tour participants, the guides and the research and museum staff to understand the experiences of the virtual guided museum tours from the perspective of older adults. We explore how virtual museum tours provide opportunities for engagement with art, technologies and people, especially during the pandemic when visits to the museum were limited by social distancing measures. We bring these findings into conversation with our previous work with on-site museum visits and reflect on questions related to accessibility of virtual museum visits as well as the challenges for socialization.
Secure voting system for elections

In this paper, we present a novel electronic voting system (EVS) for political and social elections based on known cryptographic schemes. In cryptography, the El-Gamal encryption system is an asymmetric key encryption algorithm which is based on Diffie-Hellman key exchange. We use the El-Gamal algorithm to generate and encrypt random keys for the voters. This scheme is designed in a way that the communication channels are anonymous and in the meantime privacy, eligibility and fairness are applied to the entire system. Encrypted receipt-free transactions are provided to the voters after they submit a vote successfully. Once the voters have completed voting, the voters can check on voting online using the encrypted receipt, their confirmed participation in the election. The voters will not be aware of the chosen candidate to prevent cheating and vote selling, common issues in elections these days. The name of the chosen candidate will be confidential. The proposed voting system efficiently gives the opportunity for people to vote via their own PCs/laptops, thus decreases the queues accumulated up at voting centres. In addition, it offers a highly dependable authentication approach by national ID or biometrically which leads to overcoming electoral fraud.
Information Security Awareness and Training as a Holistic Key Factor – How Can a Human Firewall Take on a Complementary Role in Information Security?

Human elements have been identified as a factor in over 95% of all security incidents. Current technical preventive, corrective, and defensive mechanisms address intelligent and practical approaches to increase the resilience of information technology (IT) systems. However, these approaches do not fully consider the behavioral, cognitive, and heterogeneous motivations that lead to human failure in the security causal chain. In this paper, we present the Awareness Continuum Management Model (ACM2), which is a role-based and topic-based theoretical approach for an information security awareness and training program that uses Boyd’s observe–orient–decide–act (OODA) loop as a framework. The proposed ACM2 is based on the situational engineering method and regards the human firewall as an integral, indispensable, and complementary part of the holistic approach to increase IT systems’ resilience. The proposed approach can be applied to different types of organizations and critical infrastructure and can be integrated into existing training programs.
Data Security and Deduplication Framework for Securing and Deduplicating Users’ Data in Public and Private Cloud Environment

Maintaining the security of data stored in the public or private cloud is a more tedious task. The cloud is the only arrangement for storing enormous amounts of data, but there is a possibility of storing the same data more than once. The traditional security system generates different unreadable data for the same readable content of a file. Therefore, it is necessary to address data security of the cloud and duplication in cloud storage. This paper concentrates on developing a data security and deduplication framework with different security techniques and mechanisms to address the said difficulties in the cloud. The framework proposed in this paper focuses on reducing security vulnerability as well as data duplication. The paper describes the components used in the frameworks. The main research contribution of the framework is having enhanced the convergent encryption technique, key generation techniques, and deduplication mechanism for maintaining a single copy of data in the cloud. The proposed framework’s efficiency is measured by implementing the work by developing a cloud-based application that coded for all the procedures of the proposed framework and tested in the cloud environment.
Research on real-time reachability evaluation for reentry vehicles based on fuzzy learning

Abstract Accurate and rapid prediction of reentry trajectory and landing point is the basis to ensure the reentry vehicle recovery and rescue, but it has high requirements for the continuity and stability of real-time monitoring and positioning data and the fidelity of the reentry prediction model. In order to solve the above contradiction, based on the theory of relative entropy and closeness in fuzzy learning, research on real-time evaluation of reentry reachability is presented in this article. With the Monte Carlo analysis data during the design and evaluation of the reentry vehicle control system, the reentry trajectory feature information base is designed. With the matching identification decision strategy between the identified trajectory and trajectory feature base, the reachability of the reentry vehicle, reachable trajectory, and landing point can be predicted. The simulation results show that by reasonably selecting the time window and using the evaluation method designed in this article, making statistics of the trajectory sequence number and frequency identified based on relative entropy and closeness method, the reachability evaluation results can be given stably, which is suitable for the real-time task evaluation of TT&amp;C system.