Reviewerly/exHarmony huggingface dataset:
A Supervised Autoencoder for Human Activity Recognition with Inertial Sensors

Human Activity Recognition (HAR) with inertial sensors is one of the most active research fields. Various machine learning algorithms have been proposed in HAR for classifying human activities. However, these methods heavily rely on the quality of hand-crafted features, requiring extensive feature engineering. Recent deep learning approaches have tried to perform training in an end-to-end manner. We propose a new learning scheme based on Supervised Autoencoder with Self-Attention (SAE-SA). Our main idea is two-fold: (1) Through the dimensional reduction in supervised autoencoder, our model is robust to noisy input signals sensor data (2) We incorporate the self-attention mechanism in the classifier layer of supervised autoencoder, which focuses more to the relevant signals, and can learn the features without any knowledge of the aggregated signal data. We evaluate SAE-SA on benchmark datasets: WISDM v2.0, and PAMAP2. We achieve accuracy of 95.76%, and 97.60% respectively, which is the state-of-the-art results in HAR.
Uncertainty Aware Kernel Estimation and Edge Preserving Attention for Blind Image Deblurring

Blind deconvolution is a challenging problem because of its ill-posed nature. Most existing blind deconvolution techniques are based on classical methods and utilize a maximum a posterior (MAP) framework to estimate clean images and blur kernels. Very recently, a method that utilizes the Deep Image Prior (DIP) principle has been proposed. This method uses two generative networks to model the deep priors of clean image and blur kernel. But this method fails for complex kernels, and estimates erroneous kernels, hence leading to ringing artifacts in the reconstructed image. To address this issue and estimate better kernels, we introduce a Bayesian uncertainty guided kernel estimation technique. Also, to improve the quality of the reconstructed images, we present a new type of edge-preserving attention. We perform evaluations on several benchmark datasets to show the performance improvement obtained by our network.
Character-based Outfit Generation with Vision-augmented Style Extraction via LLMs

The outfit generation problem involves recommending a complete outfit to a user based on their interests. Existing approaches focus on recommending items based on anchor items or specific query styles but do not consider customer interests in famous characters from movie, social media, etc. In this paper, we define a new Character-based Outfit Generation (COG) problem, designed to accurately interpret character information and generate complete outfit sets according to customer specifications such as age and gender. To tackle this problem, we propose a novel framework LVA-COG that leverages Large Language Models (LLMs) to extract insights from customer interests (e.g., character information) and employ prompt engineering techniques for accurate understanding of customer preferences. Additionally, we incorporate text-to-image models to enhance the visual understanding and generation (factual or counterfactual) of cohesive outfits. Our framework integrates LLMs with text-to-image models and improves the customer’s approach to fashion by generating personalized recommendations. With experiments and case studies, we demonstrate the effectiveness of our solution from multiple dimensions.
Few-Shot Fine-Grained Classification of Histological Images

Histological image classification plays a crucial role in cancer diagnosis. However, the acquisition of well-labeled histological images is prohibitively expensive, and obtaining rare abnormal samples is challenging. Therefore, applying few-shot learning methods to histological image classification tasks holds significant clinical value. Nevertheless, existing research predom-inantly relies on coarse-grained image classification approaches based on natural image datasets, which struggle to address the fine-grained challenges encountered in histological image classification, such as intra-class diversity and inter-class similarity. To tackle this issue, this study proposes a novel few-shot fine-grained classification method for histological images, named “Category-Aware Feature Map Reconstruction Network.” This method employs channel weights to localize the differences between inter-class and intra-class regions, composed of intra-class channel weights and inter-class channel weights, collectively referred to as category-aware weights. Specifically, intra-class channel weights indicate the matching degree of salient regions within the support set of a particular class, while inter-class channel weights represent the degree of containing distinct information between classes. The category-aware weights are utilized to transform the support feature maps and query feature maps, generating feature maps that capture differentiating details between categories. Finally, the distance between the transformed query feature map and support feature map is calculated to achieve probabilistic predictions for the categories. On a histological few-shot dataset, this method achieves an accuracy of 90.23% using ResNet-12 as the feature extractor, surpassing the baseline model by 5.24% and outperforming other few-shot methods by at least 10% in the 5-way 10-shot experimental setting. The proposed method exhibits exceptional performance on histological image few-shot datasets, playing a vital role in more accurate and pathologist-independent cancer diagnosis.
Role of Digital Transformation in Inspection and Certification

In today’s fast evolving era, digital transformation plays a pivotal role in each and every field. Absolutely, it has become necessary to integrate the cutting-edge digital technologies in the landscape of Quality System, Accreditation, and Conformity Assessment. In this chapter, author have promulgated the paradigm shift brought about by digitization of Inspection and Certification process. It discusses about the general framework for Digital Inspection and Certification process, strategies, tools, and methodologies used to enhance the efficiency, accuracy, and transparency of these critical processes focusing on mitigating the challenges faced earlier by Industry. The Digital Inspection and Certification process will enhance the productivity of Industry 4.0.
Large Language Models Improve Alzheimer's Disease Diagnosis Using Multi-Modality Data

In diagnosing challenging conditions such as Alzheimer's disease (AD), while imaging is an important reference, non-image data such as patient information, genetic data, medication information, cognitive and memory tests also play a very important role. However, limited by the ability of artificial intelligence models to mine such information, most of the existing models only use image data, and cannot make comprehensive use of non-image data. We based our approach on an existing pretrained large language model (LLM) to enhance the model's ability to utilize non-image data on diagnosing AD. Our test results on the ADNI dataset show that the approached achieved state-of-the-art (SOTA) performance.
Degradation Aware Multi-Scale Approach to No Reference Image Quality Assessment

With the advent of smartphones and social media, images have become a popular medium for sharing information. As a result understanding the perceived quality of images has gained importance. In the recent years No-Reference Image Quality Assessment (NR-IQA) has gained prominence due to the abundance of User Generated Content (UGC) being consumed. Applications ranging from photo capture assistance, suggestion of best images for sharing, creation of stories from user media gallery, and enhancement of images for best user experience require NR-IQA. This paper presents a novel Degradation Aware Multi-Scale NR-IQA technique that leverages the multi-scale nature of feature extraction in the Human Visual System (HVS) and also aims at understanding the perceived quality of images across various distortions. This is achieved via learning of two auxiliary tasks: (i) Identification of distortions present in images; (ii) Learning to rank images across distortions. Additionally, a unique data generation strategy is introduced to generate realistic distortions for learning cross-distortion ranking of image quality. The proposed approach achieves State-Of-The-Art (SOTA) performance on multiple IQA datasets and demonstrates generalization in cross-dataset testing. The results showcase the efficacy of the proposed method in identifying and ranking the quality of images across various distortions, making it a promising approach for NR-IQA in real-world applications.
Indian Regional Sign Language Recognition

Sign Language is a medium of communication in the Deaf and Hard of Hearing community (DHH community). According to WHO, there are approximately 63 million people in India, including 3.3 million from the state of Karnataka, with 0.3 million children belonging to DHH community. The sign language gestures vary globally, making communication difficult between the DHH community and others. Hence, Sign Language Recognition has gained significant attention in recent years. There is a limited publicly available dataset on Indian Sign Language. In addition, the dataset for regional languages especially the Kannada dataset, is unavailable for the sign recognition tasks. Hence initially, in our work, we built a dataset of Indian Regional Kannada Sign Language (IRKSL) for different levels of data at character, word, and sentence levels. We propose a method for recognizing the Indian sign language and translating it into Kannada regional language. We demonstrate the proposed method on the IRKSL dataset and obtained an accuracy of 81.69% and 73.87% with and without feature augmentation, respectively, on the IRKSL dataset. We also evaluated our work on the INCLUDE 50 dataset and obtained a better accuracy of 93.42% as compared to the state-of-art method.
ViLP: Knowledge Exploration using Vision, Language, and Pose Embeddings for Video Action Recognition

Video Action Recognition (VAR) is a challenging task due to its inherent complexities. Though different approaches have been explored in the literature, designing a unified framework to recognize a large number of human actions is still a challenging problem. Recently, Multi-Modal Learning (MML) has demonstrated promising results in this domain. In literature, 2D skeleton or pose modality has often been used for this task, either independently or in conjunction with the visual information (RGB modality) present in videos. However, the combination of pose, visual information, and text attributes has not been explored yet, though text and pose attributes independently have been proven to be effective in numerous computer vision tasks. In this paper, we present the first pose augmented Vision-language model (VLM) for VAR. Notably, our scheme achieves an accuracy of 92.81% and 73.02% on two popular human video action recognition benchmark datasets, UCF-101 and HMDB-51, respectively, even without any video data pre-training, and an accuracy of 96.11% and 75.75% after kinetics pre-training.
TransDocUNet: A Transformer-based UNet Architecture for Degraded Document Image Binarization

The enhancement of historical document images is critical for improving the quality and legibility of scanned or captured document images. Convolutional-based techniques previously generated competitive results for document image binarization, however, due to their inherent locality, these models are often limited in explicitly expressing long-range dependency. Transformers (ViT) have evolved as an alternative design with a global self-attention mechanism to tackle this issue, however, they can result in restricted localization capabilities due to a lack of low-level details. To address this problem, we propose TransDocUNet, a CNN-Transformer hybrid UNet architecture for document image binarization that merits both attention and convolution capabilities in a U-Net architecture and serves as a strong alternative to the existing solutions. The experimental results, obtained using the DIBCO/H-DIBCO datasets, highlight that our proposed method outperforms all the existing competing methods in terms of both objective quality metrics and visual quality assessment, achieving state-of-the-art performance in document image binarization. In addition, we undertake an ablation study to understand the role of dilation in the CNN to capture feature dependencies while reducing the computational cost as well. The findings helped us arrive at the final model and provide valuable insights into the importance of acquiring both global and local contextual information for tasks like enhancing document images.
Automatic assessment of communication skill in real-world job interviews: A comparative study using deep learning and domain adaptation.

With the increasing use of video-based job interviews, there is a growing demand for automated tools that can accurately evaluate the interviewee's performance. While hiring decisions have traditionally been made based on a combination of a candidate's knowledge, skills, and overall fit for the job, the use of automated evaluation tools can help provide more objective and consistent assessments. Traditional methods of assessing communication skills are subjective and prone to biases, leading to inconsistent evaluation of candidates. Such tools can assist in identifying candidates who possess the necessary competencies for the job and may help streamline the recruitment process for employers. This paper explores the use of deep learning and domain adaptation algorithms to improve the automatic assessment of communication skills for candidates preparing for the Civil Services Examination. Automated assessment tools provide a more objective and consistent evaluation, based on speech, language, and nonverbal cues in video-based interviews. However, these tools require large amounts of labeled data, which can be a challenge in some cases. To address this issue, we have used domain adaptation, which can transfer knowledge from a source domain to improve predictions in a target domain. The paper investigates video classification and multimodal classification methods, attention mechanisms, and deep learning techniques, along with domain adaptation approaches to enhance performance. The paper presents the interview dataset collected in an uncontrolled setting, our modeling approaches adopted to address the problem, and the experimental results and analysis. The results show the best classification accuracy is 71%.
Metaphysics of the Virtual: From Plato's Cave to modern VR: Questioning reality's essence

Virtual Reality (VR) technology and its metaphysical implications pose critical questions concerning the nature of reality, a conundrum that has enthralled thinkers since Plato's epoch. Plato's allegory of the cave emphasized the world of forms over the shadowy illusions, emphasizing a layered reality. Contemporary VR challenges this stratification, bringing forth constructed realities that overlap, enhance, or even replace tangible experiences. Drawing upon a plethora of scholarly articles, this essay examines VR's metaphysical dimensions, juxtaposing ancient philosophical delineations of reality against the transformative implications of VR technologies.
Slovene as a Minority and Foreign Language in Italian Mainstream Schools in the Province of Trieste

Abstract This paper describes the history, presence, actions and perspective of Slovene language teaching and learning in Italian primary mainstream schools in the province of Trieste as an extraordinary opportunity – after 30 years of Slovenia’s independence and almost 25 years of open borders – to reflect on the role of Slovene as a minority and foreign language among the majority population of neighbouring Italy, on the interethnic relationships it fosters, and on its potential to become an element of identity awareness development within the process of identity formation for both the kin state Slovenia homeland and the Slovene community in Trieste.
Unlocking Security: Pioneering a Novel Elliptic Curve-Based Hashing Scheme

Low-power networks and devices are becoming increasingly prevalent globally. These networks facilitate the exchange of concise messages, such as measurements and instructions. However, ensuring security, particularly concerning message integrity and sender authentication, presents a challenge in constrained environments. This article introduces a major breakthrough in the field of cryptography through the development of an innovative hash function leveraging the torsion subgroup on an elliptic curve. By incorporating the unique properties of this group, our approach redefines data security standards. We demonstrate the heightened resilience of our hash function against current attacks while maintaining exceptional performance. This novel method represents a significant advancement in safeguarding sensitive information, paving the way for more robust cybersecurity and practical applications across various domains. Experimental results confirm the effectiveness and security of our approach, establishing new perspectives for the evolution of modern cryptography.
Constant Stepsize Algorithms

In many practical circumstances, it is more convenient to use a small constant stepsize $$a(n) \equiv a \in (0, 1)$$ rather than the decreasing stepsize considered thus far. One such situation is when the algorithm is ‘hard-wired’ and decreasing stepsize may mean additional overheads.
Stochastic Gradient Schemes

By far the most frequently applied instance of stochastic approximation is the stochastic gradient descent (or ascent) algorithm and its many variants. As the name suggests, these are noisy cousins of the eponymous algorithms from optimization that seek to minimize or maximize a given performance measure.
PENGARUH JARAK KE SEKOLAH TERHADAP ANGKA PARTISPASI DAN PUTUS SEKOLAH SMP DI INDONESIA

This research empirically to analyze the effect of distance to school on junior secondary enrolment rate and schools dropout rate in Indonesia. The data were collected from 33 provinces between 2010 and 2017. To analyze cross-provinces panel data, we used fixed effect model (FEM). The findings showed a positive relationship between distance to school and schools dropout rate. But, the relationship not statistically significant. Another finding is the distance to school have a negative influence on enrollment rate and statistically significant. And finally, we provide a few recommendations which policy makers may adopt in reducing dropout from school and increasing enrollment rate of junior secondary school.
Residue Number Systems Quantization for Deep Learning Inference

Quantization of learned CNN weights to Residue Number System can improve inference latency by taking advantage of fast and precise low bit integer arithmetic. In this paper we review the mathematical aspects of RNS operations for signed integer values and evaluate implementation choices for conversion of conventional float-point PyTorch weights of CNN models to RNS representation. We also present a workflow to convert weights of PyTorch neural network layers specific for computer vision domain to 4-bit RNS moduli-sets able to maintain classification accuracy within 5% of 8-bit quantization baseline.
Vote Bank Forecast Based on Social Media Sentiment Analysis

Sentiment Analysis of human behavior from social media revolutionized many fields, such as health, business, and election prediction. Sentiment analysis is the process of analyzing an individual’s opinions, attitudes, views, sentiments, and emotions. In order to predict sentiments, predicting the polarity of words and categorizing them as positive or negative are the major goals. A quick and effective way to determine what the public thinks about one’s own political party, the opposition party, and political figures is through Twitter. prior sentiment analysis techniques have primarily struggled with classification accuracy because biases in the training data cause a sizable portion of tweets to be incorrectly classified. As a remedy, we set out to collect real-time data in order to forecast Pakistan’s next election results. Users can quickly share real-time updates on a variety of activities and events on Twitter, promoting the rapid diffusion of information and news. To do this, we accessed the Twitter API through the “Tweepy’’ Python package and retrieved real-time data from the service. Through keyword searches, we were able to find more than 5,000 tweets. We attributed feelings to particular tweets using Python’s “TextBlob’’ package. We are able to generate prediction graphics by determining the frequency of positive attitudes with respect to a particular candidate.
EfficientNetB0 Ensemble Model for Unified Deepfakes Detection

In recent years, we have witnessed the generation of exceptional authentic deepfake images and videos due to the availability of cutting-edge Artificial Intelligence and deep learning techniques. Deepfakes represent synthetic multimedia content used to propagate disinformation for defamation, political unrest, manipulating elections, committing crimes, etc. In this paper, we present a novel ReLU-Swish EfficientNet (RSE-Net) for deepfakes detection. Our proposed RSE-Net is capable of reliably detecting deepfakes videos that are generated using different techniques. Our model leverages an ensemble of EfficientNet architectures, which are combined using a fusion technique to enhance the model’s performance in detecting deepfakes. We suggested the ReLU activation in conv2D layers in place of regular Swish activation in EfficientNetB0 first variant as ReLU is computationally more efficient and reduces the risk of overfitting. We evaluated our model on two large-scale challenging deepfake datasets: FaceForensics ++ and CelebDF. Our RSE-Net attained an average accuracy of 99.7% on the FaceForensics++ dataset, and 96.09% on the CelebDF dataset. Furthermore, our model generalizes well and effectively detects deepfake videos in realworld scenarios. Thus, it is a valuable tool for analyzing and detecting potentially manipulated content.
AR Qaida: AR Assisted Multi-level Interactive Framework for Smart Learning of Early to Primary School Children

Integration of technology into education improves teaching and learning. Augmented Reality (AR) technology has received a lot of attention due to its vast potential for a variety of applications. Many subjects can be difficult to convey in a classroom or comprehend from a 2D textbook for school-aged children. This is because school-aged children need a lot of attention to become more enthusiastic and satisfied in their classes. AR applications with educational elements provide an exciting and unique learning experience. This is accomplished by blurring the boundaries between the real world they are in and the virtual world they perceive on the screen. This paper proposes an AR-based e-learning framework "AR Qaida" involving 3D interaction to meet specific goals for learning divided into levels and subjects, aiming to provide interactive, immersive learning for early-primary school children. This framework consists of 5 levels, each aiming at a specific subject: English alphabet learning (Level 1), Urdu letters (Level 2), Mathematics (Level 3), Solar System Visualization (Level 4), and Frog Dissection (Level 5) for early to primary school children’s education (age 5-14). This framework was developed utilizing Vuforia marker-based AR and Unity 3D, deployed on mobile phones. We further conducted a study on 20 participants, using the SUS survey. Younger children were assisted in using the framework and filling out the survey. AR Qaida framework achieved a SUS score of 73.85 demonstrating that the framework is useful and acceptable. We did a t-test to identify how gender affected the results. A two-tailed p-value of 0.9793 indicates that the observed difference in means is not statistically significant. It is inferred from the t-test results that there is no substantial disparity between genders in this study.