Reviewerly/exHarmony huggingface dataset:
Fall Detection System using XGBoost and IoT

This project aims to design and implement a fall detection system for the elders using machine learning techniques and Internet-of-Things (IoT). The main issue with fall detection systems is false alarms and hence incorporating machine learning in the fall detection algorithm can tackle this problem. Therefore, choosing the right machine learning algorithm for the given problem is essential and several factors need to be considered in making that choice. For this project, the XGBoost algorithm is used and the machine learning model is trained on the Sisfall dataset. A wearable device that is worn on the waist is designed using an accelerometer, a microcontroller, a Global Positioning System (GPS) module and a buzzer. The acceleration data obtained is converted into features and fed into the machine learning model which will then make a prediction. If a fall event has occurred, the buzzer is activated and emergency contacts of the victim are notified immediately using IoT and Global System for Mobile Communications (GSM). This allows the fall victim to be attended quickly, thus reducing the negative consequences of the fall. The details of the fall are stored on the cloud so that they can be easily accessed by healthcare professionals.Testing the system concluded that the XGBoost machine learning algorithm is well suited for this problem due to the small percentage error obtained.
Online Learning in Pandemic Times

A global outbreak of a new disease, Covid-19, affects a large number of people in globe, it reshaping people lives and education is not an exception. This paper will look at how the education system deals with the challenges especially for women learners in this uncertain time, what are the barriers of the online learning process for them, and who we can overcome the obstacles.
The Heisenberg limit for laser coherence

Quantum optical coherence can be quantified only by accounting for both the particle- and wave-nature of light. For an ideal laser beam1–3, the coherence can be thought of roughly as the number of photons emitted consecutively into the beam with the same phase. This number, $${\mathfrak{C}}$$ , can be much larger than the number of photons in the laser itself, μ. The limit for an ideal laser was thought to be of order μ2 (refs. 4,5). Here, assuming only that a laser produces a beam with properties close to those of an ideal laser beam and that it has no external sources of coherence, we derive an upper bound on $${\mathfrak{C}}$$ , which is of order μ4. Moreover, using the matrix product states method6, we find a model that achieves this scaling and show that it could, in principle, be realized using circuit quantum electrodynamics7. Thus, $${\mathfrak{C}}$$ of order μ2 is only a standard quantum limit; the ultimate quantum limit—or Heisenberg limit—is quadratically better. The coherence of a close-to-ideal laser beam can be quadratically better than what was believed to be the quantum limit. This new Heisenberg limit could be attained with circuit quantum electrodynamics.
Development of a Robust Multi-Scale Featured Local Binary Pattern for Improved Facial Expression Recognition

Compelling facial expression recognition (FER) processes have been utilized in very successful fields like computer vision, robotics, artificial intelligence, and dynamic texture recognition. However, the FER’s critical problem with traditional local binary pattern (LBP) is the loss of neighboring pixels related to different scales that can affect the texture of facial images. To overcome such limitations, this study describes a new extended LBP method to extract feature vectors from images, detecting each image from facial expressions. The proposed method is based on the bitwise AND operation of two rotational kernels applied on LBP(8,1) and LBP(8,2) and utilizes two accessible datasets. Firstly, the facial parts are detected and the essential components of a face are observed, such as eyes, nose, and lips. The portion of the face is then cropped to reduce the dimensions and an unsharp masking kernel is applied to sharpen the image. The filtered images then go through the feature extraction method and wait for the classification process. Four machine learning classifiers were used to verify the proposed method. This study shows that the proposed multi-scale featured local binary pattern (MSFLBP), together with Support Vector Machine (SVM), outperformed the recent LBP-based state-of-the-art approaches resulting in an accuracy of 99.12% for the Extended Cohn–Kanade (CK+) dataset and 89.08% for the Karolinska Directed Emotional Faces (KDEF) dataset.
The Effects of Mobile Learning on Listening Comprehension Skills and Attitudes of Omani EFL Adult Learners

This study aimed to explore the effect of using mobile learning on improving adult learners' listening skills in Oman, to investigate their attitudes, and to explore the factors that stand as barriers to its implementation.The study is quasi-experimental consisted of two groups, an experimental group (n=15) and a control group (n=16) from a foundation program at a military educational institute.The research data included the results of two sets of listening tests and learners' responses on an attitude questionnaire.The students in the experimental group outperformed their counterparts in the control group as a result of the mobile learning strategy.There was a statically significant improvement in the experimental group students' listening ability.Also, the participants had positive attitudes towards using mobile learning in improving their listening comprehension skills.The participants found that mobile learning enhanced their motivation, increased their exposure, expanded their vocabulary repertoire, and provided easy access to "anytime" and "everywhere" learning.However, they emphasized some challenges that were related to mobile software design, screen sizes of mobile phones, network connections, and the appropriateness of the listening content.Based on the findings, the study suggested some educational implications and recommendations.
ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation

Breast tumor segmentation is a critical task in computer-aided diagnosis (CAD) systems for breast cancer detection because accurate tumor size, shape, and location are important for further tumor quantification and classification. However, segmenting small tumors in ultrasound images is challenging due to the speckle noise, varying tumor shapes and sizes among patients, and the existence of tumor-like image regions. Recently, deep learning-based approaches have achieved great success in biomedical image analysis, but current state-of-the-art approaches achieve poor performance for segmenting small breast tumors. In this paper, we propose a novel deep neural network architecture, namely the Enhanced Small Tumor-Aware Network (ESTAN), to accurately and robustly segment breast tumors. The Enhanced Small Tumor-Aware Network introduces two encoders to extract and fuse image context information at different scales, and utilizes row-column-wise kernels to adapt to the breast anatomy. We compare ESTAN and nine state-of-the-art approaches using seven quantitative metrics on three public breast ultrasound datasets, i.e., BUSIS, Dataset B, and BUSI. The results demonstrate that the proposed approach achieves the best overall performance and outperforms all other approaches on small tumor segmentation. Specifically, the Dice similarity coefficient (DSC) of ESTAN on the three datasets is 0.92, 0.82, and 0.78, respectively; and the DSC of ESTAN on the three datasets of small tumors is 0.89, 0.80, and 0.81, respectively.
Tied Block Convolution: Leaner and Better CNNs with Shared Thinner Filters

Convolution is the main building block of a convolutional neural network (CNN). We observe that an optimized CNN often has highly correlated filters as the number of channels increases with depth, reducing the expressive power of feature representations. We propose Tied Block Convolution (TBC) that shares the same thinner filter over equal blocks of channels and produces multiple responses with a single filter. The concept of TBC can also be extended to group convolution and fully connected layers, and can be applied to various backbone networks and attention modules. Our extensive experimentation on classification, detection, instance segmentation, and attention demonstrates that TBC is consistently leaner and significantly better than standard convolution and group convolution. On attention, with 64 times fewer parameters, our TiedSE performs on par with the standard SE. On detection and segmentation, TBC can effectively handle highly overlapping instances, whereas standard CNNs often fail to accurately aggregate information in the presence of occlusion and result in multiple redundant partial object proposals. By sharing filters across channels, TBC reduces correlation and delivers a sizable gain of 6% in the average precision for object detection on MS-COCO when the occlusion ratio is 80%.
Machine Learning-Based Phishing Attack Detection

This paper explores machine learning techniques and evaluates their performances when trained to perform against datasets consisting of features that can differentiate between a Phishing Website and a safe one.This capability of telling these sites apart from one another is vital in the modernday internet surfing.As more and more of our resources shift online, one vulnerability and a leak of sensitive information by someone could bring everything down in a connected network.This paper's objective through this research is to highlight the best technique for identifying one of the most commonly occurring cyberattacks and thus allow faster identification and blacklisting of such sites, therefore leading to a safer and more secure web surfing experience for everyone.To achieve this, we describe each of the techniques we look into in great detail and use different evaluation techniques to portray their performance visually.After pitting all of these techniques against each other, we have concluded with an explanation in this paper that Random Forest Classifier does indeed work best for Phishing Website Detection.
Noisy-As-Clean: Learning Unsupervised Denoising from the Corrupted Image.

Supervised deep networks have achieved promisingperformance on image denoising, by learning image priors andnoise statistics on plenty pairs of noisy and images. Unsupervised denoising networks are trained with only noisy images. However, for an unseen corrupted image, both supervised andunsupervised networks ignore either its particular image prior, the noise statistics, or both. That is, the networks learned from external images inherently suffer from a domain gap problem: the image priors and noise statistics are very different between the training and test images. This problem becomes more clear when dealing with the signal dependent realistic noise. To circumvent this problem, in this work, we propose a novel Noisy-As-Clean (NAC) strategy of training self-supervised denoising networks. Specifically, the corrupted test image is directly taken as the clean target, while the inputs are synthetic images consisted of this corrupted image and a second and similar corruption. A simple but useful observation on our NAC is: as long as the noise is weak, it is feasible to learn a self-supervised network only with the corrupted image, approximating the optimal parameters of a supervised network learned with pairs of noisy and images. Experiments on synthetic and realistic noise removal demonstrate that, the DnCNN and ResNet networks trained with our self-supervised NAC strategy achieve comparable or better performance than the original ones and previous supervised/unsupervised/self-supervised networks. The code is publicly available at this https URL.
Probabilistic Safety for Bayesian Neural Networks

We study probabilistic safety for Bayesian Neural Networks (BNNs) under adversarial input perturbations. Given a compact set of input points, $T \subseteq \mathbb{R}^m$, we study the probability w.r.t. the BNN posterior that all the points in $T$ are mapped to the same region $S$ in the output space. In particular, this can be used to evaluate the probability that a network sampled from the BNN is vulnerable to adversarial attacks. We rely on relaxation techniques from non-convex optimization to develop a method for computing a lower bound on probabilistic safety for BNNs, deriving explicit procedures for the case of interval and linear function propagation techniques. We apply our methods to BNNs trained on a regression task, airborne collision avoidance, and MNIST, empirically showing that our approach allows one to certify probabilistic safety of BNNs with millions of parameters.
Error covariance bounds for suboptimal filters with Lipschitzian drift and Poisson-sampled measurements

We design suboptimal filters for a class of continuous-time nonlinear stochastic systems when the measurements are assumed to arrive randomly at discrete times under a Poisson distribution. The proposed filter is a dynamical system with a differential equation and a reset map which updates the estimate whenever a new measurement is received. We analyze the performance of the proposed filter by computing the expected value of the error covariance which is described by a differential equation. We study throughly the conditions under which the error covariance remains bounded, which depend on the system data and the mean sampling rate associated with the measurement process. We also study the particular cases when the error covariance is seen to decrease with the increase in the sampling rate. For the particular case of linear filters, we can also compare the error covariance bounds with the case when the measurements are continuously available.
Pengembangan Media Pembelajaran E-Learning Berbasis Moodle Pada Mata Kuliah Metodologi Penelitian

The aim of this research is to produce Moodle-based E-Learning learning media in the Research Methodology course with the help of Power Point Software, Google Slides and Moodle. This research is a type of Research and Development research using the 4D development model (Define, Design, Development and Dissemination). This study uses a non-test instrument data collection technique that is a questionnaire given to 2 material experts and 2 media experts. The results of the feasibility of learning media products, amounting to 11 learning materials using a Likert scale in the form of a score of 1-5 are as follows: a) The validation results by the material experts obtained a score of 81.54% which is included in the very feasible category; b) Results validation by media experts obtained a score of 78.4% which is included in the feasible category. Based on the results of the feasibility, it can be concluded that the learning media of E-Learning Moodle is appropriate to be used.
On the exact analysis of an idealized quantum switch

We study an entanglement distribution switch that serves k users in a star topology. The function of the switch is to facilitate end-to-end bipartite entangled state generation for pairs of users. We study a simple variant of this problem, wherein all links connecting the users to the switch are identical, the effects of state decoherence are negligible, and the switch can store an arbitrary number of qubits. We model the system using a discrete-time Markov chain and obtain the capacity of the switch. When the switch operates at capacity, we also present a numerical method for computing the expected number of qubits stored at the switch, which depends on the number of users k and the probability of successful entanglement generation at the link level p. We then compare the results of our exact analysis to that of a continuous-time Markov chain model of a quantum switch and argue that the latter is a reasonable approximation to the more realistic model presented in this work.
GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction

Recent progress in cross-lingual relation and event extraction use graph convolutional networks (GCNs) with universal dependency parses to learn language-agnostic sentence representations such that models trained on one language can be applied to other languages. However, GCNs struggle to model words with long-range dependencies or are not directly connected in the dependency tree. To address these challenges, we propose to utilize the self-attention mechanism where we explicitly fuse structural information to learn the dependencies between words with different syntactic distances. We introduce GATE, a Graph Attention Transformer Encoder, and test its cross-lingual transferability on relation and event extraction tasks. We perform experiments on the ACE05 dataset that includes three typologically different languages: English, Chinese, and Arabic. The evaluation results show that GATE outperforms three recently proposed methods by a large margin. Our detailed analysis reveals that due to the reliance on syntactic dependencies, GATE produces robust representations that facilitate transfer across languages.
Winning Lottery Tickets in Deep Generative Models

The lottery ticket hypothesis suggests that sparse, sub-networks of a given neural network, if initialized properly, can be trained to reach comparable or even better performance to that of the original network. Prior works in lottery tickets have primarily focused on the supervised learning setup, with several papers proposing effective ways of finding winning tickets in classification problems. In this paper, we confirm the existence of winning tickets in deep generative models such as GANs and VAEs. We show that the popular iterative magnitude pruning approach (with late resetting) can be used with generative losses to find the winning tickets. This approach effectively yields tickets with sparsity up to 99% for AutoEncoders, 93% for VAEs and 89% for GANs on CIFAR and Celeb-A datasets. We also demonstrate the transferability of winning tickets across different generative models (GANs and VAEs) sharing the same architecture, suggesting that winning tickets have inductive biases that could help train a wide range of deep generative models. Furthermore, we show the practical benefits of lottery tickets in generative models by detecting tickets at very early stages in training called early-bird tickets. Through early-bird tickets, we can achieve up to 88% reduction in floating-point operations (FLOPs) and 54% reduction in training time, making it possible to train large-scale generative models over tight resource constraints. These results out-perform existing early pruning methods like SNIP (Lee, Ajanthan, and Torr 2019) and GraSP(Wang, Zhang, and Grosse 2020). Our findings shed light towards existence of proper network initializations that could improve convergence and stability of generative models.
Unsupervised Automatic Text Summarization of Konkani Texts using K-means with Elbow Method

Summarization is an emerging field of research in Natural Language Processing (NLP).A bulk of the work is related to texts in English and other popular languages.This paper presents some of the early works attempted at performing single document extractive Automatic Text Summarization on Konkani language documents, which is an under-research language in the domain of Automatic Text Summarization (ATS).The input documents need to be cleaned of punctuation and then sentence scores are calculated for each sentence in the document.The scores for each sentence are computed using Term-Frequency/Inverse Document Frequency (TF-IDF) of constituent words and overlap with the title of the story and its positional value.K-means algorithm is applied to determine clusters of sentences for the formation of the final summary.The value of 'K' is determined using the Elbow method.The dataset employed was specially designed by the authors of the paper to perform the experiments.It consists of folk tales derived from books on Konkani literature.The performance assessment of the output summaries indicated that the summaries obtained by using three clusters were better than the ones obtained using two clusters.The proposed system exhibited promising outcome, considering, no language-dependent domain knowledge or any training corpora was utilized.
Automated Essay Scoring System using Multi-Model Machine Learning

Standardized testing such as the SAT often requires students to write essays and hires a large number of graders to evaluate these essays which can be time and cost consuming.Using natural language processing tools such as Global Vectors for word representation (GloVe), and various types of neural networks designed for picture classification, we developed an automatic grading system that is more time-and cost-efficient compared to human graders.We applied our application to a set of manually graded essays provided by a previous competition on Kaggle in 2012 on automated essay grading and conducted a qualitative evaluation of the approach.The result shows that the program is able to correctly score most of the essay and give an evaluation close to that of a human grader on the rest.The system proves itself to be effective in evaluating various essay prompts and capable of real-life application such as assisting another grader or even used as a standalone grader.
A Lightweight and Secured Certificate-Based Proxy Signcryption (CB-PS) Scheme for E-Prescription Systems

Electronic prescription (E-prescription) is an emerging technology that allows health practitioners (doctors, physicians, pharmacists, or nurses) to electronically transmit prescriptions to pharmacies.E-prescription systems allow doctors to avoid traditional medical practices in which prescriptions are sent manually in handwritten form.Additionally, in cases in which a patient may not be able to collect the medication in person due to physical disabilities, the medications can be delivered to the patient's home directly.Furthermore, payments can also be made online (e.g., using credit cards or bank transfers).However, these distinctive features require a series of guidelines for the successful deployment of the E-prescription system due to stringent legal requirements and privacy regulations.Two major security requirements i.e. confidentiality and authentication need to be addressed.In general, the solution to ensuring confidentiality and authentication lies in the combination of both the encryption and digital signature functions in a single logic step called signcryption.Therefore, in this article, we present a lightweight and provable secured certificate-based proxy signcryption (CB-PS) scheme for e-prescription systems.The formal security verification uses the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool along with informal security analysis, which authenticates that the proposed CB-PS scheme can potentially be implemented in resource-constrained low-computing electronic devices in E-prescription systems.