Reviewerly/exHarmony huggingface dataset:
Design of a Deep-Learning Model to Improve Learning Capabilities of LD Children via Statistical Modelling of Examination Behavioral Patterns

Students with learning disorders (LD) are unable to perform certain set of tasks due to their difficulty in understanding & interpreting them.These tasks include, but are not limited to, solving simple Mathematical identities, understanding English Grammar related questions, spelling certain words, arranging words in sequence, etc.A wide variety of system models are proposed by researchers to analyze such issues with LD students, and recommend various remedies for the same.But a very few of these models are designed for end-to-end continuous learning support, which limits their applicability.Moreover, even fewer system models are designed to improve capabilities of LD students, via modification of system's internal parameters.To cater these issues, a novel deep-learning model (DL2CSMBP) is proposed in this text, which assists in incrementally improving learning capabilities of LD children via statistical modelling of examination behavioural patterns.The model initially proposes design of a novel examination system that generates question sets based on student's temporal performance, and collects their responses via an LD-friendly approach.These responses are processed using a deep learning model that extracts statistical characteristics from student responses.These characteristics include question skipping probability, percentage of correct answers, question revisit probability, time spent on each question, un-attempted questions, & frequently skipped question types.They were extracted from 12 different question types which include Basic English Grammar, Medium English Grammar, Advanced English Grammar, direct comprehension, inference comprehension, vocabulary comprehension, sequencing, spelling, synonyms, Mathematics (addition & subtraction), and finding the odd Man out.The results of these questions were evaluated for 80+ LD students, and their responses were observed.Based on these responses a customized 1D convolutional Neural Network (CNN) layer was trained, which assisted in improving classification performance.It was observed that the proposed model was able to identify LD students with 95.6% efficiency.The LD students were able to incrementally improve the performance by attempting a series of exam sessions.Due to this incremental performance improvement, the LD students were able to cover 28% more questions, and answer almost 97% of these questions with precision & correctness.Due to such promising results, the system is capable of real-time deployment, and can act as an automated schooling tool for LD students to incrementally improve their examination performance without need of medical & psychological experts.This can also assist in reducing depression among LD students, because they don't need to interact with a physical doctor while improving their LD condition in real-time, thus suggesting its use in nonintrusive medical treatments of these students.
Production and Carbon Emission Calculation of Bricks Incorporating Construction And Demolition Waste and Pet Pellets

The study aims to incorporate solid wastes in the brick where cement is the binding agent and perform the comparative analysis of mechanical properties (compressive strength, density, and water absorption). Polyethylene Terephthalate (PET), and Construction and Demolition waste are the wastes that partially replace sand in the mixture of cement and sand. The compressive strength, water absorption, and dry density of the waste-incorporated bricks are determined which are compared with all parameters of the traditional bricks. The brick with 2% PET showed the best compressive strength of 13.515 MPa. While determining the carbon emissions of composite bricks it showed 0.6 to 0.9 kg CO2 had been generated from the raw material extraction and processing phase, however, the production phase involved no carbon emission because the process was manual and involved no heating or firing. The composite bricks have the potential to be used as structural elements, however, the composite bricks require further study for the optimization of binder content with respect to cost, compressive strength, and carbon emission.
COMMUNICATION ACTIVITY OF THE FUTURE SPECIALIST

An essential characteristic of the psychological and professional competence of future specialist is focus on a colleague as the main value of one's work. The need for self-discovery and self-change, methods of educational activity, methods of influencing colleagues, taking into account the patterns of personality development, is also determined. The constant variability of the professional activity tasks requires the future specialist to be aware of his / her role in this process, to display a creative approach taking into account the changing situations of professional interaction. The specialist's personality dynamism is explained by the need to choose means, forms, and methods of communication depending on the age characteristics of others, their individual characteristics and level of education. The ability to cooperate integrates, absorbs complex skills, namely: to formulate one's point of view, to listen and hear others, to find out the positions of one's partners, to resolve disagreements with the help of logical argumentation, not to transfer logical disagreements into the plane of personal relations, to encourage the activity of others and show initiative in a timely manner; to provide emotional and meaningful support to those who need it; to give others the opportunity to become consolidated, to try their hand at various types of activities; take the other's position and coordinate different views by exchanging ideas; take dialogical, not monological position; organically combine ″role″ and ″interpersonal″, business and human relations. Many philosophers, educators and psychologists point out that the individual consciousness and activity are influenced by two polar forces: external (environment) and internal (own will, desires and aspirations). As a result, each person passes his / her own path of individualization as an ascent to his / her ″Self″ under the influence of internal forces. And everyone has own way of socialization as result of adaptation to the external environment and the choice of ways of socialization in it. One of the courses of pedagogical effect on the future specialist is to provide students with information, namely: about man's manifestation of him / herself in society (environment), about the mechanisms (motives) that determine a person's behavior in his / her life as a whole, about ways of self-realization, about what helps a person form a real self-image. This is help in mastering the techniques of self-analysis, in forming an adequate self-assessment, in understanding one's relationships in society, in determining one's mirror self and one's meaning in mastering goal setting and planning. Keywords. Activity, personality, development, specialist, communication, professional interaction, cooperation, information, self-analysis.
Compute Nano topology by used the programing language python

The research aims to Compute the Nano topologies of any set, consisting of five or less elements, through the Python programming language, with finding the necessary algorithms for the solution steps, We explain the mechanism of action: In first we will find the equivalence relations on set, and then we must compute the nano topology, and end know who set was semi-open, alpha-open,beta-open Regular open ,pre open and gamma-open set .
Short Updates- Machine Learning Based News Summarizer

Automated Text Summarization is becoming important due to the vast amount of data being generated. Manual processing of documents is tedious, mostly due to the absence of standards. Therefore, there is a need for a mechanism to reduce text size, structure it, and make it readable for users. Natural Language Processing (NLP) is critical for analyzing large amounts of unstructured, text-heavy data. This project aims to address concerns with extractive and abstractive text summarization by introducing a new neural network model that deals with repetitive and incoherent phrases in longer documents. The model incorporates a novel Seq2Seq architecture that enhances the standard attentional model with an intra-attention mechanism. Additionally, a new training method that combines supervised word prediction and reinforcement learning is employed. The model utilizes a hybrid pointer-generator network, which distinguishes it from the standard encoder-decoder model. This approach produces higher quality summaries than existing models.
Self-Efficacy and Digital Anxiety and their Influence on Virtual Educational Performance

Digital self-efficacy is the degree of confidence to efficiently use technological tools. This type of self-efficacy reduces digital anxiety and increases students' performance in virtual environments. This justifies the objective of the present research, which is to analyze the levels of self-efficacy and digital anxiety, as well as their influence on virtual educational performance in students of a private university in Lima, Peru. Using a non-probabilistic convenience sampling procedure, 116 students participated, 105 females (90.5%) and 11 males (9.5%) between 17 and 50 years of age (MeanAge = 26.78, SD = 7.02). The results showed that students perceived to agree in having optimal levels of digital self-efficacy and efficient virtual educational performance. However, they indicated neither agreement nor disagreement in feeling digitally anxious. In addition, it was observed that digital self-efficacy is related to virtual educational performance; and digital anxiety was higher in the older study cycles. The results dispute the concept of "digital native" because age was not related to high levels of self-efficacy, digital performance, or low levels of digital anxiety.
Planning and Learning for Reliable Autonomy in the Open World

Safe and reliable decision-making is critical for long-term deployment of autonomous systems. Despite the recent advances in artificial intelligence, ensuring safe and reliable operation of human-aligned autonomous systems in open-world environments remains a challenge. My research focuses on developing planning and learning algorithms that support reliable autonomy in fully and partially observable environments, in the presence of uncertainty, limited information, and limited resources. This talk covers a summary of my recent research towards reliable autonomy.
Detecting Deepfakes: A Novel Framework Employing XceptionNet-Based Convolutional Neural Networks

Social networking sites have become primary sources of information for web users, making the rapid dissemination of deepfakes a cause for concern.Deepfakes are digitally manipulated images or videos that contain the computer-generated face of another person.Advancements in hardware and computational technologies have made the creation of deepfakes increasingly accessible, even to individuals without technical expertise.The potential harm posed by deepfakes necessitates urgent efforts to improve the detection of these manipulated media.Deep learning (DL) models have experienced rapid growth, enabling the synthesis and generation of hyper-realistic videos, often referred to as "deepfakes."DL algorithms can now create faces, swap faces between 2 individuals in video, and modify facial expressions, gender, also other features.These video manipulation techniques have applications in numerous fields, but deepfakes specifically exploit DL to synthesize and alter images in a manner that makes it difficult to discern between fake and genuine media.In this study, we present novel deepfake detection framework using DL and pre-trained XceptionNet model depends upon deep CNNs (Convolutional Neural Networks).We employ facial landmark recognition to extract information related to several facial characteristics from videos.This data is then used to facilitate the deep learning model's differentiation between genuine and deepfake videos.Features extracted from videos are utilized to train CNN concurrently.Our deepfake detection system is built on a multi-input Xception Neural Network model, which leverages CNNs.The system is trained using the Dessa Dataset and subset of Deepfake Detection Challenge Dataset.Proposed model demonstrates strong performance, achieving 96% classification accuracy and an AUC of 0.97, offering a promising solution for detecting deepfake videos.
The Importance of Examining Student’s Choice of Modalities in Higher Education [Abstract]

Aim/Purpose. Examination of student’s preferences regarding educational modalities after the COVID-19 pandemic. Background. It is necessary to investigate whether the mode of course delivery (online or traditional) offered by educational institutions aligns with the preferences of students, or if students are limited to enrolling in courses based solely on the available course modalities. Methodology. The paper in which this abstract is written is a quantative causal comparative study with the use of publicly available secondary data collected during course enrollment. Contribution. The contribution of this abstract to further the discussion of students needs and how an institution can continue to provide structure to adapt to a changing generation of students. Findings. The research suggests that higher education institutions should adjust their course offerings to accommodate the enrollment habits and preferences of students with respect to online and in-person courses. Recommendations for Practitioners. Recommendations can have practical impacts for university, colleges, program development, and faculty. Specific recommendations will be discussed after full development of the paper. Recommendations for Researchers. Specific recommendations will be discussed after full development of the paper. Impact on Society. This abstract and forthcoming paper may provide a different perspective on the priority of higher education institutions use adapt to student’s enrollment habits and preferences regarding their enrolling in an online or face-to-face course. Future Research. To account for the variations in institutional structures and student preferences regarding course delivery modes, it is recommended that further research be conducted.
SumREN: Summarizing Reported Speech about Events in News

A primary objective of news articles is to establish the factual record for an event, frequently achieved by conveying both the details of the specified event (i.e., the 5 Ws; Who, What, Where, When and Why regarding the event) and how people reacted to it (i.e., reported statements). However, existing work on news summarization almost exclusively focuses on the event details. In this work, we propose the novel task of summarizing the reactions of different speakers, as expressed by their reported statements, to a given event. To this end, we create a new multi-document summarization benchmark, SumREN, comprising 745 summaries of reported statements from various public figures obtained from 633 news articles discussing 132 events. We propose an automatic silver-training data generation approach for our task, which helps smaller models like BART achieve GPT-3 level performance on this task. Finally, we introduce a pipeline-based framework for summarizing reported speech, which we empirically show to generate summaries that are more abstractive and factual than baseline query-focused summarization approaches.
SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images

Visual question answering on document images that contain textual, visual, and layout information, called document VQA, has received much attention recently. Although many datasets have been proposed for developing document VQA systems, most of the existing datasets focus on understanding the content relationships within a single image and not across multiple images. In this study, we propose a new multi-image document VQA dataset, SlideVQA, containing 2.6k+ slide decks composed of 52k+ slide images and 14.5k questions about a slide deck. SlideVQA requires complex reasoning, including single-hop, multi-hop, and numerical reasoning, and also provides annotated arithmetic expressions of numerical answers for enhancing the ability of numerical reasoning. Moreover, we developed a new end-to-end document VQA model that treats evidence selection and question answering as a unified sequence-to-sequence format. Experiments on SlideVQA show that our model outperformed existing state-of-the-art QA models, but that it still has a large gap behind human performance. We believe that our dataset will facilitate research on document VQA.
RINK: Reader-Inherited Evidence Reranker for Table-and-Text Open Domain Question Answering

Most approaches used in open-domain question answering on hybrid data that comprises both tabular-and-textual contents are based on a Retrieval-Reader pipeline in which the retrieval module finds relevant “heterogenous” evidence for a given question and the reader module generates an answer from the retrieved evidence. In this paper, we present a Retriever-Reranker-Reader framework by newly proposing a Reader-INherited evidence reranKer (RINK) where a reranker module is designed by finetuning the reader’s neural architecture based on a simple prompting method. Our underlying assumption of reusing the reader’s module for the reranker is that the reader’s ability to generating an answer from evidence contains the knowledge required for the reranking, because the reranker needs to “read” in-depth a question and evidences more carefully and elaborately than a baseline retriever. Furthermore, we present a simple and effective pretraining method by extensively deploying the commonly used data augmentation methods of cell corruption and cell reordering based on the pretraining tasks - tabular-and-textual entailment and cross-modal masked language modeling. Experimental results on OTT-QA, a large-scale table-and-text open-domain question answering dataset, show that the proposed RINK armed with our pretraining procedure makes improvements over the baseline reranking method and leads to state-of-the-art performance.
Low Resource Quantitative Information Extraction via Structure Searching and Prefix-Based Text Generation

Quantitative information plays an important part in the financial and data analysis areas. Prior work relied on pattern-matching methods and complex hand-crafted rules to extract quantitative information due to the lack of labeled data. Such methods can be unstable and difficult to scale to the open domain. In this paper, we study quantitative information extraction in the low-resource setting. We propose a search-based approach by searching from the syntactic structures to acquire basic training data. The search process is simple yet effective. Then, a prefix-based text-to-text generation method is employed to extract the quantitative information. The prefix design can fully leverage pre-trained language models for text generation to serve the information extraction purpose. Experimental results show that our approaches achieves high performance with a limited amount of labeled data. The extraction result could further boost the performance of other tasks such as quantitative reasoning.
Script, Language, and Labels: Overcoming Three Discrepancies for Low-Resource Language Specialization

Although multilingual pretrained models (mPLMs) enabled support of various natural language processing in diverse languages, its limited coverage of 100+ languages lets 6500+ languages remain ‘unseen’. One common approach for an unseen language is specializing the model for it as target, by performing additional masked language modeling (MLM) with the target language corpus. However, we argue that, due to the discrepancy from multilingual MLM pretraining, a naive specialization as such can be suboptimal. Specifically, we pose three discrepancies to overcome. Script and linguistic discrepancy of the target language from the related seen languages, hinder a positive transfer, for which we propose to maximize representation similarity, unlike existing approaches maximizing overlaps. In addition, label space for MLM prediction can vary across languages, for which we propose to reinitialize top layers for a more effective adaptation. Experiments over four different language families and three tasks shows that our method improves the task performance of unseen languages with statistical significance, while previous approach fails to.
Multi-Mask Label Mapping for Prompt-Based Learning

Prompt-based Learning has shown significant success in few-shot classification. The mainstream approach is to concatenate a template for the input text to transform the classification task into a cloze-type task where label mapping plays an important role in finding the ground-truth labels. While current label mapping methods only use the contexts in one single input, it could be crucial if wrong information is contained in the text. Specifically, it is proved in recent work that even the large language models like BERT/RoBERTa make classification decisions heavily dependent on a specific keyword regardless of the task or the context. Such a word is referred to as a lexical cue and if a misleading lexical cue is included in the instance it will lead the model to make a wrong prediction. We propose a multi-mask prompt-based approach with Multi-Mask Label Mapping (MMLM) to reduce the impact of misleading lexical cues by allowing the model to exploit multiple lexical cues. To satisfy the conditions of few-shot learning, an instance augmentation approach for the cloze-type model is proposed and the misleading cues are gradually excluded through training. We demonstrate the effectiveness of MMLM by both theoretical analysis and empirical studies, and show that MMLM outperforms other existing label mapping approaches.
Label-Specific Feature Augmentation for Long-Tailed Multi-Label Text Classification

Multi-label text classification (MLTC) involves tagging a document with its most relevant subset of labels from a label set. In real applications, labels usually follow a long-tailed distribution, where most labels (called as tail-label) only contain a small number of documents and limit the performance of MLTC. To facilitate this low-resource problem, researchers introduced a simple but effective strategy, data augmentation (DA). However, most existing DA approaches struggle in multi-label settings. The main reason is that the augmented documents for one label may inevitably influence the other co-occurring labels and further exaggerate the long-tailed problem. To mitigate this issue, we propose a new pair-level augmentation framework for MLTC, called Label-Specific Feature Augmentation (LSFA), which merely augments positive feature-label pairs for the tail-labels. LSFA contains two main parts. The first is for label-specific document representation learning in the high-level latent space, the second is for augmenting tail-label features in latent space by transferring the documents second-order statistics (intra-class semantic variations) from head labels to tail labels. At last, we design a new loss function for adjusting classifiers based on augmented datasets. The whole learning procedure can be effectively trained. Comprehensive experiments on benchmark datasets have shown that the proposed LSFA outperforms the state-of-the-art counterparts.