Reviewerly/exHarmony huggingface dataset:
An interactive and secure Blockchain web portal for online healthcare services

In a world where privacy and authenticity get questioned at every turn, technologies like Blockchain exist to help assist the digital information&#x2019;s integrity to remain. The healthcare industry is a volatile and sensitive industry, which is seen as an avenue for multiple technological ideas and advancements. With privacy being the main concern, work talks about the importance of its resolution, putting users and their needs first through user research, and further implementing a secure and effective Blockchain file storage system using IPFS and Ethereum, accessed via an interactive web application and using a secure backend for user information integrity within the system.
An Improved Procedure for Face Mask Detection using Convolution Neural Network

The present COVID-19 pandemic scenario where entire world is facing a lot of unforeseen medical challenges, can be somehow controlled by following some medical guidelines and procedures when monitored strictly. Wearing a specifically recommended face mask is one of the easiest ways to prevent it. However, due to negligence by the common people for avoiding face masks, need a meticulous system to find such people and appropriate action may be taken against them. Medical guidelines state that primarily COVID-19 and its variants outspread typically through nose discharge or droplets of saliva by the infected person&#x2019;s coughs or sneezes. Droplets of saliva all around us could infect us and others. So, People need to wear masks as it&#x2019;s one of the methods which can effectively depress the growth of its spreading. Identifying masks over the faces could be done using many methodologies in Computer Science. The primary concern is to work upon identifying masks on people&#x2019;s faces and increasing their recognition accuracy with simple methodology and network. Keep the fact in mind, this paper presents a methodology for the identification of masks on people's faces using Convolution Neural Networks (CNN), for this, a training dataset has been used to generate more augmented images. Afterward, it has been pre-processed by applying CNN. The method has been evaluated using a CNN-based algorithm on the test dataset. The simulation result shows satisfactory performance and accuracy through different curves.
Perception and Clustering Analysis towards Cryptocurrency Investment Decision using Machine Learning

The objectives of this research are 1) to study the relationship of people interested in investing in cryptocurrencies; 2) to identify people interested in cryptocurrencies. This research study is quantitative research by survey research using an online questionnaire data collection method of 402 respondents. The research results found that Most of them are Generation Z people interested in investing in cryptocurrencies and low to middle-income groups, most of whom are interested in BTC and ETH, the well-known crypto-currency groups. The analysis was then divided by K-Means Clustering analysis into three groups, namely "Not interested in cryptocurrencies", "Moderate-interest" and "Risk-takers" that interested in investing in cryptocurrencies. The last group has a selection to invest in a variety of cryptocurrencies, especially SOL, BNB and ADA, which are smart contract coins.
The Price of Secrecy: How Hiding Internal DRAM Topologies Hurts Rowhammer Defenses

DRAM vendors today choose to keep the internal topologies of DRAM devices secret. This decision introduces significant practical challenges for designers of memory controllers who wish to provide their own forms of Rowhammer defenses. This paper describes three such challenges and the vulnerabilities, inefficiencies, and overhead they introduce. Defenses implemented at the memory controller could reduce (if not eliminate) these deficiencies should internal DRAM topology be made available.
Cloud Computing Risk: A Decision-making Framework

Cloud computing systems have the potential to provide many advantages for businesses. However, the use of cloud-based systems exposes organizations to risks that they might not otherwise have experienced, specifically because their data is stored on remote servers accessed via password-protected applications and controlled by cloud service providers, thus allowing for the possibility of a myriad of risks. Prior research on cloud computing has either focused on the advantages of cloud computing or on its associated risks, doing so which only provides half the picture. This two-part study presents findings from a survey questionnaire and a review culminating in a proposed model that combines both the proposed positive outcomes and potential risks of cloud computing.
Post Covid-19 Strategy Through Supporting Teacher Digital Literacy as the Sustainable Decision to Enhance Education System: Indonesia Case Study

For the world of education, including students, instructors, and policymakers for adoption of digital literacy has now become a key concern. Teachers must be aware of the importance of mastering digital literacy in the learning experience. After the Covid-19 epidemic, this research intends to examine educator knowledge of the application of digital literacy in the learning process. The study employs a quantitative descriptive technique to carefully review the types of teacher digital literacy awareness, supportive factors in the usage of digital literacy, and the consequences of tutor computer literacy recognition following the Covid 19 epidemic. The research process was conducted in the Islamic Public Senior High School (MAN) in Palembang, Sumatra. The consistent findings indicated that positive' competence of digital literacy was significant in terms of their capacity to use technology about 100%. The most influential supporting factor correctly is the motivation of friends (97.90%). The implication of using digital literacy for teachers is the execution of virtual meeting learning (94%). The logical conclusion of this study is that teachers develop awareness in utilizing technology, and it is more effortless to convey material after the Covid-19 pandemic. Mastery of digital literacy for teachers needs to be carefully reviewed to the stage of performance and application to students.
Feature Extraction of National Physical Fitness Data Based on Data Mining

In order to better understand and optimize the national physical fitness, this paper puts forward the national physical fitness data change feature extraction method based on data mining, uses the decision tree and association rule data mining algorithm to collect the national physical fitness data in recent years, constructs the database to realize the effective data management, and uses the data mining algorithm to construct the physical fitness change feature evaluation index. Finally, through experiments, it is confirmed that the national physique data change feature extraction method based on data mining has high effectiveness in the process of practical application. It can better understand the national physique change trend and put forward targeted suggestions for national physique health optimization.
Neighbor-Augmented Transformer-Based Embedding for Retrieval

With rapid evolution of e-commerce, it is essential but challenging to quickly provide a recommending service for users. The recommender system can be divided into two stages: retrieval and ranking. However, most recent academic research has focused on the second stage for datasets with limited size, while the role of retrieval is heavily underestimated. Generally, graph-based or sequential models are used to generate item embedding for the retrieval task. However, the graph-based methods suffer from over-smoothing, while sequential models are largely influenced by data sparseness. To alleviate these issues, we propose NATM—a novel embedding-based method in large-scale learning incorporating both graph-based and sequential information. NATM consists of two key components: i) neighbor augmented graph construction with user behaviors to enhance item embedding and mitigate data sparseness, followed by ii) transformer-based representation network, targeting on minimizing NCE loss. The competitive performance of the proposed method is demonstrated through comprehensive experiments, including a benchmark study on MovieLens dataset and a real-world e-commerce scenario in Alibaba Group.
Image Set Quality Optimization for Handwritten Gujarati Character and Its Modifier Recognition

The problem of recognizing handwritten Gujarati characters has been tried by many researchers but still it requires enough work from vowel recognition up to its online application. The problem becomes even more complex if we use characters with vowels. Machine learning and Deep learning are extensively used to solve the image classification problem. It is observed by reviewing survey papers that Support Vector Machine, Bayes Probability Model, Deterministic Finite Automaton (DFA), Hidden Markov Model techniques are used as classifiers in this problem but machine learning and deep learning gives more promising result. It requires large data set to train and test the model. We collected hand-written Gujarati &#x2018;Barakshari&#x2019; and text images from more than 1000 people having different ages. Deep learning requires a comparatively larger image set than machine learning. Both can have their pros and cons and so it is very much essential to optimize the data set if we are using the &#x2018;hybrid mode&#x2019; to get benefits from both. Different augmentation techniques are also applied to the image set to raise the size, quality, and variety.
CNN-Based Gaze Estimation for Off-angle Iris Recognition

Standoff iris recognition systems have been developed to identify not only fully cooperative individuals but also non-cooperative moving subjects. Since iris images are captured in much less constrained conditions, they are likely to be non-ideal including being off-angle. However, traditional iris recognition systems are designed for only frontal iris images. Therefore, the existing databases save binary iris codes using only frontal images for iris matching. To use these existing iris codes, standoff systems need to be compatible with existing iris matching algorithms by generating the frontal version of iris codes even for off-angle images. Since gaze estimation is the first essential component to reconstruct the frontal view of off-angle iris images, we first need to estimate the eye gaze direction from a single off-angle iris image. This paper presents a deep learning-based gaze angle estimation method for standoff iris recognition frameworks. As the main contribution, our approach will allow us to estimate the gaze angle from an off-angle image using transfer learning and a regression model without segmentation and using additional hardware. The proposed CNN-based gaze estimation method is trained and tested with 108,600 off-angle images from 100 subjects. It shows high accuracy in gaze estimation with an average error of 4&#x00B0; in angle for off-angle images captured from every angle ranging -50&#x00B0; to +50&#x00B0; even if training images are not available for all angles.
Caching Networks: Capitalizing on Common Speech for ASR

We introduce Caching Networks (CachingNets), a speech recognition network architecture capable of delivering faster, more accurate decoding by leveraging common speech patterns. By explicitly incorporating select sentences unique to each user into the network’s design, we show how to train the model as an extension of the popular sequence transducer architecture through a multitask learning procedure. We further propose and experiment with different phrase caching policies, which are effective for virtual voice-assistant (VA) applications, to complement the architecture. Our results demonstrate that by pivoting between different inference strategies on the fly, CachingNets can deliver significant performance improvements. Specifically, on an industrial-scale, VA ASR task, we observe up to 7.4% relative word error rate (WER) and 11% sentence error rate (SER) improvements with accompanied latency gains.
Sentence Level Sentiment Classification Using Machine Learning Approach in the Bengali Language

In recent times, sentiment analysis is one of the most important aspects of machine learning research. However, many context-aware systems have developed based on the English language, which can automatically process the English language to make a clear emotion. However, much less work has done on Bengali than that. The main reason for this is the lack of an accurate Bengali dataset. In our works, we have used a unique dataset. The data is mainly various comments made by people through online news portals and social media. Humans collect those and maximum awareness sought while labeling the emotions. All data labeled into positive (1) and negative (0) emotions. Our main objective in this research is to build a Bengali context-aware system using various supervised machine-learning algorithms that can easily find out the emotions of any Bengali language. For this, we used K-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LR), Support Vector Classifier (SVC), Multinomial Na&#x00EF;ve Bayes (MNB), and Random Forest (RF) algorithm. Among them, the Random Forest (RF) algorithm generates the maximum accuracy that is 67.34 %.
Person Localization using Machine Learning in Multi-Source Camera Surveillance System

The localization of individuals, using face recognition technology, is important in today&#x2019;s society for various reasons, such as locating criminals. Closed-circuit television (CCTV) systems offer high level of security at a reasonable price by recognizing suspicious activities. Because of the wide usage of facial recognition technology, the general public&#x2019;s worry about privacy increased significantly. These concerns have prompted numerous federal authorities to propose new legislation to protect personal privacy. In this paper, we propose an accurate person localization scheme to enable law enforcement agencies to identify the locations visited by wanted and suspected people using surveillance cameras placed in various public places. Unlike the existing techniques that measure the Euclidean distance between two images to determine if they are for the same person, we propose a more accurate technique that is based on a machine learning model to determine whether the features of two images belong to the same person. Our performance evaluations demonstrate that our model outperforms the Euclidean distance-based schemes when operating in a multi-source camera environment with changing recording quality.
Efficient Identity-Based Chameleon Hash for Mobile Devices

Online/offline identity-based signature (OO-IBS) is an adequate cryptographic tool to provide the message authentication and integrity in mobile devices, since it lightens the computational burden after the signer receives the message and eliminates the overhead of certificate management. It has several valuable applications, such as wireless sensor networks and automatic dependent surveillance-broadcast systems. Identity-based chameleon hash (IB-CH), as an alternative building block to construct OO-IBS, has been explored in several literatures. Nevertheless, almost all of the prior IB-CH schemes are in the random oracle model, which may lead to security risks in practicality. The only IB-CH scheme in the standard model proposed by Xie et al. (ICC’21) suffers from the large size of public parameters and inefficient setup process. In this paper, we propose an efficient IB-CH scheme in the standard model, significantly reducing the computational costs of all the algorithms and the size of public parameters compared with Xie’s scheme. The security and experimental analyses demonstrate the security and good performance of our scheme. Furthermore, we applied our scheme to optimize the existing generic OO-IBS construction. Our optimized construction reduces computational overhead by 50.0% in the online phase compared with the original construction.
Weighted Wavelet-Based Spectral-Spatial Transforms For CFA-Sampled Raw Camera Image Compression Considering Image Features

To efficiently compress raw camera images captured using a color filter array (CFA-sampled raw images), wavelet-based spectral-spatial transforms (WSSTs) that change a CFA-sampled raw image from an RGB color space into a decorrelated color space have been presented. This study introduces weighted WSSTs (WWSSTs) that work especially for the CFA-sampled raw images with many edges well. The WWSSTs are obtained by considering that each predict step of the conventional WSSTs is constructed by a combination of two 1-D diagonal transforms and by weighting them along the edge directions in the images. The experiments at JPEG 2000-based lossless and lossy compression basically show that compared with the WSSTs, our WSSTs improve the results for the images with many edges by about 0.04 bpp in LBRs, 2.10 [%] in BD-rates, and 0.12 dB in BD-PSNRs while keeping the compression efficiency for the general images.
Improving Ultrasound Image Classification with Local Texture Quantisation

Ultrasound image classification is important for disease diagnosis. It is more challenging than usual image classification tasks since ultrasound images are difficult to collect and usually contain lots of noise. This paper proposes a novel image classification framework for small-scaled and noisy ultrasound image datasets. The framework first transforms images into discrete index grids. The index grids use discrete indices encoding the local texture patterns of the images. Then, it will conduct classification based on index grids. The proposed framework can significantly reduce the impact of noise as well as the amount of training data that needed. Comparing with existing models, the proposed framework is a lite model and has better explainability. We evaluated the proposed approach on two public ultrasound image datasets for thyroid nodule classification and breast nodule classification. The experiment results show that the proposed approach achieves the new state-of-the-art. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
JE<sup>2</sup>NET: Joint Exploitation and Exploration in Reinforcement Learning Based Image Restoration

Previous reinforcement learning (RL) based image restoration studies typically train RL agents to search for recovery tools from a constructed toolset and iteratively recover images. However, we argue that these agents rely on pre-trained RL models with fixed-length paths for restoration, which performs poorly in the case of unknown distortions. To address these issues, we propose a joint exploitation and exploration reinforcement learning network (JE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net). Specifically, we propose a new deep classification network for image feature extraction and tool selection, which serves as a model prior. Second, we design a stochastic strategy to randomly select tools and a dynamic termination strategy to adaptively stop the recovery process. In this way, the model prior and exploration mechanism can be jointly used to expand the search space and obtain more quality gain. Experimental results show that our proposed method is more flexible compared to other state-of-the-art methods and achieves significant quality improvements in the presence of unknown distortions.
Photon-Limited Deblurring Using Algorithm Unrolling

Image deblurring in a photon-limited condition is ubiquitous in a variety of low-light applications such as photography, microscopy and astronomy. However, the presence of photon shot noise due to a low-illumination and/or short exposure makes the deblurring task substantially more challenging than conventional deblurring. In this paper we present an algorithm unrolling approach that unrolls a Plug-and-Play algorithm using a fixed-iteration network. By changing the conventional two-variable splitting formulation of Plug-and-Play to an alternate three-variable splitting, we obtain a differentiable and end-to-end trainable network. Our algorithm outperforms existing methods for 1dB across different illuminations. We also overcome the difficulty of acquiring real motion blur kernels at low-light by presenting a photon-limited motion deblurring image dataset.
UReST: A Knowledge-Based Usability Requirements Specification Support Tool

Despite the importance of identifying and specifying a complete set of usability requirements in the early stage of software development, there are only a few methods and tools that can adequately support the requirement engineers in the task of eliciting and specifying relevant usability requirements. This paper presents UReST, a knowledge-based usability requirement support tool aimed at supporting the requirement engineer with a knowledge repository and predefined structured template that aids in performing their task to capture and specify relevant usability requirements during the requirement gathering. A controlled experiment and usability study have been conducted to evaluate the effectiveness of the UReST in supporting the requirement engineer to perform usability requirement specification activities. The results from both experiment and usability study concluded that UReST could support the requirement engineer to identify and generate more usability requirements relevant to a domain functionality. Nevertheless, the developed UReST also received a high acceptance level of usefulness and ease of use in the usability study.
Bayesian Continual Imputation and Prediction For Irregularly Sampled Time Series Data

Learning from irregularly sampled, streaming, multi-variate time-series data with many missing values is a very challenging task. In this paper, we propose a Bayesian Continual Imputation and Prediction for Time-series Data (B-CIPIT), for learning from a sequence of time-series tasks. First, we develop a Bayesian LSTM based continual learning algorithm, which is capable of learning continually from a sequence of multi-variate time-series tasks, without catastrophically forgetting any representations. Second, we impute missing values in these time-series sequences, in a continual learning setting. We demonstrate and evaluate the robustness of the proposed algorithm on two real-world clinical time-series data sets, namely MIMIC-III [1] and PhysioNet Challenge 2012 [2]. Performance study results show the superiority of the proposed learning algorithm.
End-to-End Low Resource Keyword Spotting Through Character Recognition and Beam-Search Re-Scoring

This paper describes an end-to-end approach to perform keyword spotting with a pre-trained acoustic model that uses recurrent neural networks and connectionist temporal classification loss. Our approach is specifically designed for low-resource keyword spotting tasks where extremely small amounts of in-domain data are available to train the system. The pre-trained model, largely used in ASR tasks, is fine-tuned on in-domain audio recordings. In inference the model output is matched against the set of predefined keywords using a beam-search re-scoring based on the edit distance.We demonstrate that this approach significantly outperforms the best state-of-the art systems on a well known keyword spotting benchmark, namely "google speech commands". Moreover, com-pared against state-of-the-art methods, our proposed approach is extremely robust in case of limited in domain training material. We show that a very small performance reduction is observed when fine tuning with a very small fraction (around 5%) of the training set.We report an extensive set of experiments on two keyword spotting tasks, varying training sizes and correlating keyword classification accuracy with character error rates provided by the system. We also report an ablation study to assess on the contribution of the out-of-domain pre-training and of the beam-search re-scoring.