Reviewerly/exHarmony huggingface dataset:
ԲԱՐՁՐԱԳՈՒՅՆ ԿՐԹՈՒԹՅԱՆ ՈԼՈՐՏՈՒՄ ԿԱՐՈՂՈՒՆԱԿՈՒԹՅՈՒՆՆԵՐԻ ՄՈԴԵԼՆԵՐԻ ԿԻՐԱՌՄԱՆ ՀՆԱՐԱՎՈՐՈՒԹՅՈՒՆՆԵՐԻ ՀԱՄԱՌՈՏ ՔՆՆՈՒԹՅՈՒՆ / BRIEF ANALYSIS OF THE POTENTIAL USE OF COMPETENCY MODELS IN HIGHER EDUCATION

The article covers the concept of competency models, their evolution as well as the approaches to their development and application. The article analyses the current diversity of definitions on competency models, excerpted from the available literature, identifying the patterns of the application of these models in the labour market, and proposing to consider their use in higher education, too, in order to verify the achievements of learning outcomes and the validity of the granted qualification.
Multi-task Perception for Autonomous Driving

Multi-task learning has become a popular paradigm to tackle multiple tasks simultaneously with less inference time and computation resources. Recently, many self-supervised pre-training methods have been proposed and they have achieved impressive performance on a range of computer vision tasks. However, their generalization ability to multi-task scenarios is yet to be explored. Besides, most multi-task algorithms are designed for specific tasks usually not within the scope of autonomous driving, which makes it difficult to compare state-of-the-art multi-task learning methods in autonomous driving. In this chapter, we divide the multi-task perception into 2D perception and 3D perception in autonomous driving. For 2D perception, we extensively investigate the transfer ability of various self-supervised methods and reproduce multiple popular multi-task methods. Then we introduce a simple and effective pretrain-adapt-finetune paradigm for multi-task learning and a novel adapter named LV-Adapter which reuses powerful knowledge from the Contrastive Language-Image Pre-training (CLIP) model pre-trained on image-text pairs. We further present an effective multi-task framework for autonomous driving, GT-Prompt, which learns general prompts and generates task-specific prompts to guide the model to capture task-invariant and task-specific information. For 3D perception, we investigate both multi-modality fusion and multi-task learning, and introduce an effective multi-level gradient calibration learning framework across tasks and modalities during optimization.
Colonoscopy Coverage Revisited: Identifying Scanning Gaps in Real-Time

Colonoscopy is the most widely used medical technique for preventing Colorectal Cancer, by detecting and removing polyps before they become malignant. Recent studies show that around 25% of the existing polyps are routinely missed. While some of these do appear in the endoscopist’s field of view, others are missed due to a partial coverage of the colon. The task of detecting and marking unseen regions of the colon has been addressed in recent work, where the common approach is based on dense 3D reconstruction, which proves to be challenging due to lack of 3D ground truth and periods with poor visual content. In this paper we propose a novel and complementary method to detect deficient local coverage in real-time for video segments where a reliable 3D reconstruction is impossible. Our method aims to identify skips along the colon caused by a drifted position of the endoscope during poor visibility time intervals. The proposed solution consists of two phases. During the first, time segments with good visibility of the colon and gaps between them are identified. During the second phase, a trained model operates on each gap, answering the question: “Do you observe the same scene before and after the gap?” If the answer is negative, the endoscopist is alerted and can be directed to the appropriate area in real-time. The second phase model is trained using a contrastive loss based on the auto-generated examples. Our method evaluation on a dataset of 250 procedures annotated by trained physicians provides sensitivity of 75% with specificity of 90%.
Multispectral 3D Masked Autoencoders for Anomaly Detection in Non-Contrast Enhanced Breast MRI

Mammography is commonly used as an imaging technique in breast cancer screening but comes with the disadvantage of a high overdiagnosis rate and low sensitivity in dense tissue. dynamic contrast enhanced (DCE)-magnetic resonance imaging (MRI) features higher sensitivity but requires time consuming dynamic imaging and injection of contrast media, limiting the capability of the technique as a widespread screening method. In this work, we extend the masked autoencoder (MAE) approach to perform anomaly detection on volumetric, multispectral MRI. This new model, coined masked autoencoder for medical imaging (MAEMI), is trained on two non-contrast enhanced breast MRI sequences, aiming at lesion detection without the need for intravenous injection of contrast media and temporal image acquisition, paving the way for more widespread use of MRI in breast cancer diagnosis. During training, only non-cancerous images are presented to the model, with the purpose of localizing anomalous tumor regions during test time. We use a public dataset for model development. Performance of the architecture is evaluated in reference to subtraction images created from DCE-MRI. Code has been made publicly available: https://github.com/LangDaniel/MAEMI .
Automated Voice-to-Image Generation Using Generative Adversarial Networks in Machine Learning

Creating visuals from words may appear to be a complex process, but it is achievable with today’s technological advancements in Information Systems. Naturally, all the human-centric actions and assumptions may lead to visualization using Artificial Intelligence. In today’s Information Systems technological world, any item or a thing can be best described in pictorial form as a human person. Our paper aims to focus on providing machines with this intelligence. To complete this challenge, we used Natural Language Processing with Deep Learning. Our primary focus is on Generative Adversarial Networks. GANs will generate data based on word labels that are provided. NLP is also important since it helps to translate the provided speech into embedding vectors that the model can use. Our study is on the CUB dataset, which comprises bird photos. In today’s world, there are text-to-image generating models accessible. The authors investigated all of them, extending text-to-image generation to voice-to-image generation.
An Automated System for Indian Currency Classification and Detection using CNN

The visibly disabled frequently experience difficulties with daily tasks that a typical person would take for granted, such as handling financial transactions. Since India’s demonetization took effect, accepting money has become a difficult task. Due to the similar dimensions of new cash banknotes and the fact that some old money banknotes are still in use, India now has two banknotes for every category. Due to the current situation, it is extremely difficult to identify banknotes for those who appear to be weak. The sight and brain are gifts to humans. Detecting things with the same qualities is practically impossible for persons who are sight impaired. In keeping with this, we suggest an automated system that would enable those who are visually impaired to recognize currency through a sound notification from a variety of applications. Therefore, in this quest, we help them locate the currency notes. In this study, we apply different convolution neural network (CNN) models to datasets of Indian banknotes in order to extract deep features and recognize different currencies. To train, verify, and test the CNN model, we can produce a fresh dataset of Indian banknotes. The proposed model may be created with TensorFlow, enhanced by choosing the best hyper parameter value, and evaluated against well-established CNN architectures using transfer learning.
Human Action Recognition by Learning Spatio-Temporal Features with Deep Neural Networks

Human action recognition plays a crucial role in various applications, including video surveillance, human-computer interaction, and activity analysis. This paper presents a study on human action recognition by leveraging CNN-LSTM architecture with an attention model. The proposed approach aims to capture both spatial and temporal information from videos in order to recognize human actions. We utilize the UCF-101 and UCF-50 datasets, which are widely used benchmark datasets for action recognition. The UCF-101 dataset consists of 101 action classes, while the UCF-50 dataset comprises 50 action classes, both encompassing diverse human activities. Our CNN-LSTM model integrates a CNN as the feature extractor to capture spatial information from video frames. Subsequently, the extracted features are fed into an LSTM network to capture temporal dependencies and sequence information. To enhance the discriminative power of the model, an attention model is incorporated to improve the activation patterns and highlight relevant features. Furthermore, the study provides insights into the importance of leveraging both spatial and temporal information for accurate action recognition. The findings highlight the efficacy of the CNN-LSTM architecture with an attention model in capturing meaningful patterns in video sequences and improving action recognition accuracy. You should leave 8 mm of space above the abstract and 10 mm after the abstract. The heading Abstract should be typed in bold 9-point Arial. The body of the abstract should be typed in normal 9-point Times in a single paragraph, immediately following the heading. The text should be set to 1 line spacing. The abstract should be centred across the page, indented 17 mm from the left and right page margins and justified. It should not normally exceed 200 words.
An Automated Framework for Summarizing YouTube Videos Using NLP

In recent times, YouTube has increasingly become the preferred platform to consume educational content. In order to learn complex and intricate concepts, a student must sit through many of hours of YouTube videos where an average video length is about 20 minutes. To see if the content of a given YouTube video is relevant to what the user is looking for, YouTube Video Summarizer was conceptualized. YouTube Video Summarizer is a Chrome Extension tool which can be used to quickly generate the summary of a YouTube video using the English-language transcript of the video Automation. This allows for a seamless generation of a synopsis without spending hours watching the content to determine its relevancy.
Design of Language Translator Headphone: The Future of Sustainable Communication

In an increasingly interconnected world, the ability to overcome language barrier has become a crucial requirement. for effective communication. Language translator headphones have emerged as a promising technological solution to facilitate seamless multilingual conversations. This research paper aims to evaluate their accuracy, usability, and potential impact on communication dynamics. The paper begins by discussing the significance of language translation technology in various domains, such as tourism, business, and diplomacy. It highlights the limitations of traditional translation methods and emphasizes the need for innovative as well as sustainable solutions that can enhance communication efficiency and convenience with the help of raspberry pi as a microcontroller.
Sustainably empowered crowdfunding through blockchain-enhanced security technology

A sustainable crowdfunding smart contract is a specialized type of smart contract that empowers individuals to contribute funds towards a specific work or venture while ensuring ecological and ethical longevity. The smart contract is meticulously programmed to establish the parameters of the crowdfunding campaign, encompassing the total funds requisite for project completion, the quantity of available tokens for acquisition, campaign duration, and token valuation. In alignment with sustainable principles, the contract orchestrates the transparent collection and distribution of funds. It unequivocally designates the blockchain address for fund allocation and delineates how these funds will be equitably apportioned among the project’s development team, thus promoting sustainable practices.Furthermore, the sustainable crowdfunding smart contract features a built-in mechanism to restore funds to contributors in the event that the project fails to achieve its funding target within the stipulated time frame. Upon campaign culmination, the smart contract autonomously dispenses tokens to contributors and allocates funds to the project’s development team, fostering an environment of trust and accountability. This sustainable approach leverages the power of smart contracts to streamline and automate the entire process, assuring contributors that their resources will be judiciously utilized for their intended purpose. A pivotal advantage of this innovative approach lies in the elimination of intermediaries, fostering heightened security for assets through a human-intervention-free programmed process. By combining blockchain technology as the foundational backend and leveraging solidity for smart contract implementation, this sustainable solution exemplifies the pinnacle of accuracy and reliability. Through this seamless integration of technology and sustainability, the paper presents a progressive paradigm in crowdfunding that resonates with environmentally conscious and ethically mindful principles.
Automated Interview Evaluation

The Interview Automation System is an Artificial Intelligence (AI) powered platform designed to help users prepare for real-life interviews. By asking questions and evaluating responses against original answers, our system provides users with a valuable opportunity to practice and improve their interview skills. The research aims to increase users' familiarity with the interview process, ultimately boosting their confidence and improving their chances of success. The workflow of the research involves several key steps, including data pre-processing and database management, audio-based question delivery, and audio-based answer collection and evaluation. Our platform is easily scalable and can be expanded to include additional functionalities such as users confidence detection, resume-based topic questioning, and body language analysis.
A Real-time Automated System for Object Detection and Facial Recognition

Object detection, facial recognition, and person identification are important tasks in computer vision with numerous real-life applications. The major goal of the proposed model is to identify people and recognize them in the images. In this paper, we propose a real-time automated system that combines power of both EfficientDet model for object detection and the FaceNet model for facial recognition to detect persons in an input image, recognize their faces, and label them with their corresponding names. The experimental study of the model takes place on COCO dataset and a custom dataset of images of students. This solution can be applied to various scenarios beyond education, such as in security and surveillance, healthcare, transportation, retail, and entertainment etc. The importance of the model lies in its ability to efficiently and accurately perform person identification and recognition in real-time scenarios, which can save time and resources and improve overall efficiency.
Bayesian Uncertainty Estimation in Landmark Localization Using Convolutional Gaussian Processes

Landmark localization is an important step in image analysis, where the clinical definition of a landmark can be ambiguous, leading to a practical necessity for model uncertainty quantification that is rigorous and trustworthy. In this paper, we present the first Bayesian framework using Gaussian processes to capture both dataset-level landmark ambiguity and sample-level model uncertainty. Our proposed two-stage approach includes a deep learning based U-Net for coarse predictions, followed by a convolutional Gaussian process (CGP) for fine-grained predictions with uncertainty estimates, learning covariance matrices rather than using a pre-defined covariance matrix. Our Bayesian approach yields a more rigorous quantification of uncertainty compared to deep learning-based uncertainty estimation techniques, whilst still achieving comparable localization accuracy. Our results suggest that CGPs can better model the inherent uncertainties in landmark localization tasks and provide more reliable confidence estimates, making it a promising direction for future research.
LOCALLY ADAPTIVE AND DIFFERENTIABLE REGRESSION

Overparameterized models like deep nets and random forests have become very popular in machine learning. However, the natural goals of continuity and differentiability, common in regression models, are now often ignored in modern overparametrized, locally adaptive models. We propose a general framework to construct a global continuous and differentiable model based on a weighted average of locally learned models in corresponding local regions. This model is competitive in dealing with data with different densities or scales of function values in different local regions. We demonstrate that when we mix kernel ridge and polynomial regression terms in the local models, and stitch them together continuously, we achieve faster statistical convergence in theory and improved performance in various practical settings.
Effectiveness of Mathematics Digitized Activities for Learners’ Intervention (Math-DALI) Game Apps to the Mathematics Performance of Grade 10 Learners

With learners becoming more technologically savvy by the day, this necessitates the development of more relevant approaches to the use of technology in the classroom. This research determined the effectiveness of using Mathematics Digitized Activities for Learners’ Intervention (Math-DALI) game apps in enhancing the mathematics performance of grade 10 learners. This study utilized a pretest-posttest quasi-experimental design. The participants of the study were the grade 10 learners of Los Baños Integrated School S.Y. 2021-2022. Among the grade 10 learners, 28 pairs were selected using match-pairing. These pairs of learners were from the online distance learning modality and modular learning distance modality. Mean and standard deviation were utilized to determine the participants’ performance in pre-test, post-test, and formative tests. Likewise, an independent t-test was utilized to determine the significant difference between the pretest and posttest of the two groups. At the same time, Cohen’s d was used to determine the effect size or to quantify the size of the difference between two test scores. This study was able to conclude that the use of digitized activity learning intervention (Math DALI) in teaching Mathematics can help strengthen the performance of the learners. It was determined that the intervention designed addressed the demands of the learners' numeracy issues.
Enhanced Word-Unit Broad Learning System With Sememes

High accuracy in text classification can be achieved by simultaneously learning multiple sources of information, such as sequence and word. In this study, we propose a novel learning framework for text classification, called Word-unit Broad Learning System (BLS). The Word-unit BLS utilises a flat neural network known as BLS and offers three key advantages. First, it provides higher accuracy and shorter training time compared to popular machine learning methods, allowing for the simultaneous learning of sequence information and word importance. Second, we incorporate a multi-layer perceptron with attention aggregation in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feature-mapped</i> layer, along with position encoding, to capture the latent relationship between each word and the contextual information in a global context. Lastly, we introduce a novel approach to enhance word representation by employing sememes in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">enhancement node</i> layer, thereby improving the feature distribution of each word in the vector space. The effectiveness of the proposed framework was evaluated by conducting experiments on four datasets covering various types of text classifications. The results demonstrate that Word-unit BLS achieves 8.26% higher accuracy than Naive Bayes while requiring 1/33 of the training time. Furthermore, when compared with traditional BLS models, Word-unit BLS outperforms in learning the sequence information. The effectiveness of sememe enhancement in word representation is also demonstrated, particularly in the case of large-scale datasets.
Applying Collaborative Adversarial Learning to Blind Point Cloud Quality Measurement

As an emerging three-dimensional presentation format, point cloud plays a vital role in the field of immersive visual media, especially for applications related to human vision. The measurement of point cloud quality is of great significance for facilitating point cloud quality enhancement, optimizing the equipment chain of the point cloud, and improving human visual perception quality. In this paper, we propose MMF-PCQM, a blind point cloud quality measurement network that leverages multimodal and multiscale features, i.e., multiscale texture features in 2D images and geometric information in points, to assess the point cloud quality. MMF-PCQM consists of a preprocessing module, dual-branch fusion network module, and feature learning module. Specifically, the preprocessing module transfers a point cloud into 2D images and 3D point clusters. The dual-branch fusion network module aims to capture and integrate the multiscale complementary features from the multimodal data to attain the final quality measurement. The feature learning module bridges two modalities to enhance the fused feature through a collaborative adversarial learning strategy. We evaluate MMF-PCQM on two large publicly available datasets, including the Waterloo Point Cloud dataset and the SIAT Point Cloud Quality Database. Extensive experimental results and ablation studies demonstrate that our MMF-PCQM achieves better results compared with the state-of-the-art methods.
A Tale of Two Cities: Data and Configuration Variances in Robust Deep Learning

Deep neural networks (DNNs) have widespread applications in industries such as image recognition, supply chain, medical diagnosis, and autonomous driving. However, previous work has shown that the high accuracy of a DNN model does not imply high robustness (i.e., consistent performances on new and future datasets) because the input data and external environment (e.g., software and model configurations) for a deployed model are constantly changing. Therefore, ensuring robustness is crucial to enhance business and consumer confidence. Previous research focuses mostly on the data aspect of model variance. This article takes a holistic view of DNN robustness by summarizing the issues related to both data and software configuration variances. We also present a predictive framework using search-based optimization to generate representative variances for robust learning, considering data and configurations.