Reviewerly/exHarmony huggingface dataset:
An Application of Rule-Based Classification with Fuzzy Logic to Image Subtraction

Surveillance camera is used as a new technology for security.In this research, the combination of OpenCV with image processing will be discussed.Saving the space in the hard drive by recording only video when here would be an image formed in the subtraction of the original image to the next image captured.With the use of Image Processing and Fuzzy logic, the research was enhanced by eliminating the recording of same image captured.After analyzing the background images, it can now determine when to start recording the video or when to stop recording a video by subtracting the images in the backdrop image and comparing the image if there was an object in motion using template matching.With the application of the project, memory storage saved up to forty-six percentage points.
A Novel Technique for Image Retrieval based on Concatenated Features Extracted from Big Dataset Pre-Trained CNNs

Accessing semantically relevant data from a database is not only essential in commercial applications but also in medical imaging diagnosis.Representation of the query image by its features and subsequently the dataset are the key factors in Content Based Image Retrieval (CBIR).Texture, shape and color are commonly used features for this purpose.Features extracted from the pre-trained Convolutional Neural Networks (CNNs) are used to improve the performance of CBIR methods.In this work, we explore a recent state of the art big dataset pre-trained CNNs which are known as Big Transfer Networks.Features extracted from Big Transfer Network have higher discriminative power compared to features of many other pre-trained CNNs.The idea behind the proposed work is to demonstrate the effectiveness of using features of big transfer networks for image retrieval.Further, features extracted from big transfer networks are concatenated to improve the performance of the proposed method.Feature diversity supplemented with network diversity should ensure good discriminative power for image retrieval.This idea is supported by performing simulations on four datasets with varying sizes in terms of number of images and classes.As feature size increases with the concatenation, we applied a dimensionality reduction algorithm i.e., Principal Component Analysis.Several distance metrics are explored in this work.By properly choosing the pre-trained CNNs and distance metric, it is possible to achieve higher mean average precisions.ImageNet-21K pre-trained CNN and Instagram pre-trained CNN are chosen in this work.Further, a pre-trained network trained on Imagenet-21K dataset is superior compared to the networks trained on ImageNet-1K dataset as there are more classes and presence of wide variety of images.This is demonstrated by applying our algorithm on four datasets i.e., COREL-100, CALTECH-101, FLOWER-17 and COIL-100.Simulations are presented for various precisions (scopes), and distance metrics.Results are compared with the existing algorithms and superiority of the proposed method in terms of mean Average Precision is shown.
VR Based Remote Campus Tour

Abstract: We recommend a system called "Virtual Tour of College Infrastructure" that gives a virtual college tour to visitors, such as students, parents, and guests. They may be interested in learning about the facilities and infrastructure offered by the college. For this, students must really travel to the college campus, which will take a lot of their time and effort and cost money for those who live far away. The suggested solution offers a virtual perspective of the infrastructure at the college in order to address this problem. This enables visitors access a certain department or lab from a distance. Additionally, it can be displayed at the College's main gate to assist visitors in finding a certain student, member of staff, or lab by outlining the precise route through the "Search" module. Virtual reality is essentially how this technology operates. Combining two or more photos to create an image panorama is the procedure. The method of producing an image panorama from a collection of photos with overlapped fields is known as image stitching. The process of stitching together photographs is difficult because of issues including noise-corrupted images, indexing a lot of images, high image resolution, parallax, and scene motion. Another difficulty is getting rid of seams that are noticeable. This work applied a feature-based automatic image stitching approach to obtain a broad seamless panorama.
Hate Speech Detection Using Machine Learning

Abstract: Twitter's central goal is to enable everybody to make and share thoughts and data, and to communicate their suppositions and convictions without boundaries. Twitter's job is to serve the public discussion, which requires portrayal of a different scope of points of view. Yet, it does not advance viciousness against or straightforwardly assault or undermine others based on race, nationality, public cause, rank, sexual direction, age, inability, or genuine illness. Hate Speech can hurt a person or a community. So, it is not appropriate to use hate speech. Now, due to increase in social media usage, hate speech is very commonly used on these platforms. So, it is not possible to identify hate speeches manually. So, it is essnetial to develop an automated hate speech detection model and this resaech work shows different approaches of Natural Language Processing for classification of Hate Speech through Machine Learning Algorithms.
Scaling MadMiner with a deployment on REANA

MadMiner is a Python package that implements a powerful family of multivariate inference techniques that leverage matrix element information and machine learning. This multivariate approach neither requires the reduction of high-dimensional data to summary statistics nor any simplifications to the underlying physics or detector response. In this paper, we address some of the challenges arising from deploying MadMiner in a real-scale HEP analysis with the goal of offering a new tool in HEP that is easily accessible. The proposed approach encapsulates a typical MadMiner pipeline into a parametrized yadage workflow described in YAML files. The general workflow is split into two yadage sub-workflows, one dealing with the physics simulations and the other with the ML inference. After that, the workflow is deployed using REANA, a reproducible research data analysis platform that takes care of flexibility, scalability, reusability, and reproducibility features. To test the performance of our method, we performed scaling experiments for a MadMiner workflow on the National Energy Research Scientific Computer (NERSC) cluster with an HT-Condor back-end. All the stages of the physics sub-workflow had a linear dependency between resources or wall time and the number of events generated. This trend has allowed us to run a typical MadMiner workflow, consisting of 11M events, in 5 hours compared to days in the original study.
Data-Driven Response Regime Exploration and Identification for Dynamical Systems

Data-Driven Response Regime Exploration and Identification (DR$^2$EI) is a novel and fully data-driven method for identifying and classifying response regimes of a dynamical system without requiring human intervention. This approach is a valuable tool for exploring and discovering response regimes in complex dynamical systems, especially when the governing equations and the number of response regimes are unknown, and the system is expensive to sample. Additionally, the method is useful for order reduction, as it can be used to identify the most dominant response regimes of a given dynamical system. DR$^2$EI utilizes unsupervised learning algorithms to transform the system's response into an embedding space that facilitates regime classification. An active sequential sampling approach based on Gaussian Process Regression (GPR) is used to efficiently sample the parameter space, quantify uncertainty, and provide optimal trade-offs between exploration and exploitation. The performance of the DR$^2$EI method was evaluated by analyzing three established dynamical systems: the mathematical pendulum, the Lorenz system, and the Duffing oscillator. The method was shown to effectively identify a variety of response regimes with both similar and distinct topological features and frequency content, demonstrating its versatility in capturing a wide range of behaviors. While it may not be possible to guarantee that all possible regimes will be identified, the method provides an automated and efficient means for exploring the parameter space of a dynamical system and identifying its underlying "sufficiently dominant" response regimes without prior knowledge of the system's equations or behavior.
Hard Patches Mining for Masked Image Modeling

Masked image modeling (MIM) has attracted much research attention due to its promising potential for learning scalable visual representations. In typical approaches, models usually focus on predicting specific contents of masked patches, and their performances are highly related to pre-defined mask strategies. Intuitively, this procedure can be considered as training a student (the model) on solving given problems (predict masked patches). However, we argue that the model should not only focus on solving given problems, but also stand in the shoes of a teacher to produce a more challenging problem by itself. To this end, we propose Hard Patches Mining (HPM), a brand-new framework for MIM pre-training. We observe that the reconstruction loss can naturally be the metric of the difficulty of the pre-training task. Therefore, we introduce an auxiliary loss predictor, predicting patch-wise losses first and deciding where to mask next. It adopts a relative relationship learning strategy to prevent overfitting to exact reconstruction loss values. Experiments under various settings demonstrate the effectiveness of HPM in constructing masked images. Furthermore, we empirically find that solely introducing the loss prediction objective leads to powerful representations, verifying the efficacy of the ability to be aware of where is hard to reconstruct.
Towards Understanding How Data Augmentation Works with Imbalanced Data

Data augmentation forms the cornerstone of many modern machine learning training pipelines; yet, the mechanisms by which it works are not clearly understood. Much of the research on data augmentation (DA) has focused on improving existing techniques, examining its regularization effects in the context of neural network over-fitting, or investigating its impact on features. Here, we undertake a holistic examination of the effect of DA on three different classifiers, convolutional neural networks, support vector machines, and logistic regression models, which are commonly used in supervised classification of imbalanced data. We support our examination with testing on three image and five tabular datasets. Our research indicates that DA, when applied to imbalanced data, produces substantial changes in model weights, support vectors and feature selection; even though it may only yield relatively modest changes to global metrics, such as balanced accuracy or F1 measure. We hypothesize that DA works by facilitating variances in data, so that machine learning models can associate changes in the data with labels. By diversifying the range of feature amplitudes that a model must recognize to predict a label, DA improves a model's capacity to generalize when learning with imbalanced data.
Frequency-tunable microwave quantum light source based on superconducting quantum circuits

A nonclassical light source is essential for implementing a wide range of quantum information processing protocols, including quantum computing, networking, communication, and metrology. In the microwave regime, propagating photonic qubits that transfer quantum information between multiple superconducting quantum chips serve as building blocks of large-scale quantum computers. In this context, spectral control of propagating single photons is crucial for interfacing different quantum nodes with varied frequencies and bandwidth. Here we demonstrate a microwave quantum light source based on superconducting quantum circuits that can generate propagating single photons, time-bin encoded photonic qubits and qudits. In particular, the frequency of the emitted photons can be tuned in situ as large as 200 MHz. Even though the internal quantum efficiency of the light source is sensitive to the working frequency, we show that the fidelity of the propagating photonic qubit can be well preserved with the time-bin encoding scheme. Our work thus demonstrates a versatile approach to realizing a practical quantum light source for future distributed quantum computing.
MED-VT: Multiscale Encoder-Decoder Video Transformer with Application to Object Segmentation

In this paper, we present an end-to-end trainable unified multiscale encoder-decoder transformer that is focused on dense prediction tasks in video. The presented Multiscale Encoder-Decoder Video Transformer (MED-VT) uses multiscale representation throughout and employs an optional input beyond video (e.g., audio), when available, for multimodal processing (MED-VT++). Multiscale representation at both encoder and decoder yields three key benefits: (i) implicit extraction of spatiotemporal features at different levels of abstraction for capturing dynamics without reliance on input optical flow, (ii) temporal consistency at encoding and (iii) coarse-to-fine detection for high-level (e.g., object) semantics to guide precise localization at decoding. Moreover, we present a transductive learning scheme through many-to-many label propagation to provide temporally consistent video predictions. We showcase MED-VT/MED-VT++ on three unimodal video segmentation tasks (Automatic Video Object Segmentation (AVOS), actor-action segmentation and Video Semantic Segmentation (VSS)) as well as a multimodal segmentation task (Audio-Visual Segmentation (AVS)). Results show that the proposed architecture outperforms alternative state-of-the-art approaches on multiple benchmarks using only video (and optional audio) as input, without reliance on optical flow. Finally, to document details of the model's internal learned representations, we present a detailed interpretability study, encompassing both quantitative and qualitative analyses.
KubeEdge-Sedna v0.3: Towards Next-Generation Automatically Customized AI Engineering Scheme

The scale of the global edge AI market continues to grow. The current technical challenges that hinder the large-scale replication of edge AI are mainly small samples on the edge and heterogeneity of edge data. In addition, edge AI customers often have requirements for data security compliance and offline autonomy of edge AI services. Based on the lifelong learning method in the academic world, we formally define the problem of edge-cloud collaborative lifelong learning for the first time, and release the industry's first open-source edge-cloud collaborative lifelong learning. Edge-cloud collaborative lifelong learning adapts to data heterogeneity at different edge locations through (1) multi-task transfer learning to achieve accurate prediction of "thousands of people and thousands of faces"; (2) incremental processing of unknown tasks, the more systems learn and the smarter systems are with small samples, gradually realize AI engineering and automation; (3) Use the cloud-side knowledge base to remember new situational knowledge to avoid catastrophic forgetting; (4) The edge-cloud collaborative architecture enables data security compliance and edge AI services to be offline autonomy while applying cloud resources. This work hopes to help fundamentally solve the above-mentioned challenges of edge-cloud collaborative machine learning.
GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot Learning

In the realm of 3D-computer vision applications, point cloud few-shot learning plays a critical role. However, it poses an arduous challenge due to the sparsity, irregularity, and unordered nature of the data. Current methods rely on complex local geometric extraction techniques such as convolution, graph, and attention mechanisms, along with extensive data-driven pre-training tasks. These approaches contradict the fundamental goal of few-shot learning, which is to facilitate efficient learning. To address this issue, we propose GPr-Net (Geometric Prototypical Network), a lightweight and computationally efficient geometric prototypical network that captures the intrinsic topology of point clouds and achieves superior performance. Our proposed method, IGI++ (Intrinsic Geometry Interpreter++) employs vector-based hand-crafted intrinsic geometry interpreters and Laplace vectors to extract and evaluate point cloud morphology, resulting in improved representations for FSL (Few-Shot Learning). Additionally, Laplace vectors enable the extraction of valuable features from point clouds with fewer points. To tackle the distribution drift challenge in few-shot metric learning, we leverage hyperbolic space and demonstrate that our approach handles intra and inter-class variance better than existing point cloud few-shot learning methods. Experimental results on the ModelNet40 dataset show that GPr-Net outperforms state-of-the-art methods in few-shot learning on point clouds, achieving utmost computational efficiency that is $170\times$ better than all existing works. The code is publicly available at https://github.com/TejasAnvekar/GPr-Net.
OVERVIEW OF THE METHODOLOGY OF UNIVERSITIES` INTERNATIONAL RANKINGS FOR MEETING THE CHALLENGES OF OPEN SCIENCE

International rankings directly affect the development of universities around the world, especially in the context of Open Science.The purpose of the article is a comparative analysis of the methodology for calculating the main international university rankings and establishing the readiness of their transformation to meet the requirements of open science. The methodological basis of the study was the methodology of the main international universities presented on their websites. The article analyzes the methodologies of the main international university rankings and highlights the main criteria by which the rankings are calculated. According to the results of the conducted research, the main sources of information necessary for determining international ratings were systematized, namely: Information from official sources external to the university, which is publicly available; Results of a survey of the academic community; University survey results; Student survey results; The results of the survey of employers; Information from the university website; Information on scientific profiles of the university; Information on scientific profiles of university employees; Information from the websites of scientific journals; Information from university repositories. The main features that universities should take into account in order to achieve higher positions in international rankings, which will become possible if they comply with the requirements of Plan S regarding Open Science, have been clarified.
Utilization of the Kahoot Games Application for Understanding Economic Learning in High School Students

Learning Economics is one of the lessons that need to be well understood by students. With the existence of the economy, students will have a basis for starting and managing the economy and businesses that will be built. Still, there are problems with students of SMAN 1 Majalaya, where they lack enthusiasm and feel less active in learning economics lessons. There needs to be an improvement in learning methods so that their interest in and understanding of basic economics increases, one of which is by utilizing the Kahoot Games application. This study used a quantitative approach with the method of testing the oneway ANOVA hypothesis with a total of 42 respondents who were students of class XI IPS 3 at SMAN 1 Majalaya. The results of the study explain that the use of the learning activity application using the Kahoot Games application has increased students' understanding of the importance of the economy in the daily lives of students so that participants who are Majalaya 1 Public High School students will easily understand basic economics lessons and begin to manage their finances properly.
The Illocutionary Speech Acts in Students’ Public Speaking

Public speaking is not only about sharing information but also about pointing out the speakers’ intentions. There is the meaning should be understood by the hearers. This study discusses the illocutionary speech acts in the students’ public speaking in English Department. This study aims to determine the types of illocutionary speech acts in a formative speech in the sixth semester of the English language education study program at Pasir Pengaraian University. This study was descriptive qualitative. The sample of this research was the sixth-semester students of the English education study program at Pasir Pengarain University. The samples were taken by using the total sample. There were 22 students. The data of this study were obtained using the listening and note-taking technique design about the meaning of utterances containing. The data were analyzed by using Austin's Theory. The result showed three illocutionary speech acts: ten assertiveness, eight directive, four declarative, three commissives, and ten expressive.
Diagnosis of Maths Teaching Efficacy Beliefs Using Expert System

Research on the design of an expert system to diagnose Mathematics Teaching Efficacy Beliefs (MTEB) using the Certainty Factor method was the first to be developed. The MTEB instrument was first initiated by Enoch and transformed into an expert system application using the paradigm of modified waterfall software development to sequential or sequential testing steps, ranging from analysis, design, coding, and testing. Data calculation and analysis using the certainty factor method assumes the user's choice is not exact and is at a certain interval. The weighting of expert values from MTEB items is carried out by two experts in the field of mathematics education. Meanwhile, the weight of user consultation uses five approval options, namely strongly agree, agree, simply agree, disagree, and disagree, successively represented by -0.2, -0.1, 0, 0.1, and 0.2. The results of the study gave success to each page of the diagnosis application, and the accuracy rate of the MTEB diagnosis was about 98%. The implication of this research is recommendations for related stakeholders to be followed up so that the MTEB of mathematics teachers becomes even better.