Reviewerly/exHarmony huggingface dataset:
Opinion Mining-Based Term Extraction Sentiment Classification Modeling

The spread of social media has accelerated the formation and dissemination of user review data, which contain subjective opinions of users on products, in an e-commerce environment. Because these reviews significantly influence other users, opinion mining has garnered substantial attention in analyzing the positive and negative opinions of users and deriving solutions based on these analytical results. Terms that include sentimental information and used in user reviews serve as the most crucial element in sentimental classification. In this regard, it is crucial to distinguish the most influential terms in user reviews. This study proposed a document-level sentiment classification model based on the collection and application of user reviews generated in an e-commerce environment. Here, a term information extraction method was applied to the proposed model to select core terms, classify the selected terms according to parts of speech (POS), determine terms that can increase information power and influence, and adopt these terms in opinion mining research, based on SVM, SVM+, and SVM+MTL techniques. The results obtained from evaluating the proposed model indicate that it exhibited excellent sentiment analysis performance. The proposed model is expected to be effectively utilized in providing enhanced services for users and increasing competitiveness in the e-commerce environment.
MR-FPN: Multi-Level Residual Feature Pyramid Text Detection Network Based on Self-Attention Environment

With humanity entering the age of intelligence, text detection technology has been gradually applied in the industry. However, text detection in a complex background is still a challenging problem for researchers to overcome. Most of the current algorithms are not robust enough to locate text regions, and the problem of the misdetection of adjacent text instances still exists. In order to solve the above problems, this paper proposes a multi-level residual feature pyramid network (MR-FPN) based on a self-attention environment, which can accurately separate adjacent text instances. Specifically, the framework uses ResNet50 as the backbone network, which is improved on the feature pyramid network (FPN). A self-attention module (SAM) is introduced to capture pixel-level relations, increase context connection, and obtain efficient features. At the same time, the multi-scale enhancement module (MEM) improves the expression ability of text information, extracting strong semantic information and integrating the multi-scale features generated by the feature pyramid. In addition, information regarding the upper features will cause loss when the feature pyramid is passed down step by step, and multi-level residuals can effectively solve this problem. The proposed model can effectively improve the fusion ability of the feature pyramid, provide more refined features for text detection, and improve the robustness of text detection. This model was evaluated on CTW1500, Total-Text, ICDAR2015, and MSRA-TD500 datasets of different kinds and achieved varying degrees of improvement. It is worth mentioning that the F-measure of 83.31% obtained by this paper on the Total-Text dataset exceeds that of the baseline system by 5%.
Weapon Detection using Yolov4, CNN

Abstract: A Weapon Detection software is a very efficient way to monitor the streets and alert the operator only when there is a situation where people carrying a weapon like a gun or knife can be detected and analyzed with the help of Yolov4 object detection algorithm. And the notification of the same will be given via email to the CCTV operator with the photo where people carrying a gun or knife can be seen and also the location of the CCTV. So this will enhance the safety and security of any city with the requirement of limited man power. The proposed paper focuses on different approaches and algorithms that could potentially help in Weapon detection. Keywords: Weapon Detection, Yolov4, Google Colab, CNN
A Deep Residual Learning Implementation of Metamorphosis

In medical imaging, most of the image registration methods implicitly assume a one-to-one correspondence between the source and target images (i.e., diffeomorphism). However, this is not necessarily the case when dealing with pathological medical images (e.g., presence of a tumor, lesion, etc.). To cope with this issue, the Metamorphosis model has been proposed. It modifies both the shape and the appearance of an image to deal with the geometrical and topological differences. However, the high computational time and load have hampered its applications so far. Here, we propose a deep residual learning implementation of Metamorphosis that drastically reduces the computational time at inference. Furthermore, we also show that the proposed framework can easily integrate prior knowledge of the localization of topological changes (e.g., segmentation masks) that can act as spatial regularization to correctly disentangle appearance and shape changes. We test our method on the BraTS 2021 dataset, showing that it outperforms current state-of-the-art methods in the alignment of images with brain tumors.
Top 100 Cited Publications for The Last Thirty Years in Digital Learning and Mobile Learning

Digital learning and mobile learning are two different things but have something in common. This research aims to find trends, topics, similarities, differences, advantages, and disadvantages of digital learning and mobile learning over the last thirty years (1992 – 2021). The method used in this research is bibliometric analysis with the help of Ms-excel, VOSViewer, and Wordcloud to visualize the metadata obtained from Scopus. The findings show that the trend of digital learning continues to increase from year to year. In addition, it can explain that mobile learning is an innovative part of digital learning with almost the same differences, similarities, advantages, and disadvantages. In the future, it is hoped that further research related to digital learning will continue to develop along with technological developments.
Graph-Based Small Bowel Path Tracking with Cylindrical Constraints

We present a new graph-based method for small bowel path tracking based on cylindrical constraints. A distinctive characteristic of the small bowel compared to other organs is the contact between parts of itself along its course, which makes the path tracking difficult together with the indistinct appearance of the wall. It causes the tracked path to easily cross over the walls when relying on low-level features like the wall detection. To circumvent this, a series of cylinders that are fitted along the course of the small bowel are used to guide the tracking to more reliable directions. It is implemented as soft constraints using a new cost function. The proposed method is evaluated against ground-truth paths that are all connected from start to end of the small bowel for 10 abdominal CT scans. The proposed method showed clear improvements compared to the baseline method in tracking the path without making an error. Improvements of 6.6% and 17.0%, in terms of the tracked length, were observed for two different settings related to the small bowel segmentation.
Modeling the Enablers and Barriers to Effective E-learning: A TISM Approach

&#x0D; Abstract: The drastic change in the technological environment has transformed the entire world including the education sector which was and is largely dominated by classroom teaching. In the last decade, a new entrant in the education sector has been e-learning. The COVID-19 pandemic has forced educational institutes to look at e-learning as a path to continue the learning process. The present paper aims to propose a model highlighting the enablers that encourage the smooth and effective delivery of e-learning process and highlight the barriers that cause hurdles in the effective delivery of e-learning. The researchers have followed the Total Interpretive Structural Modelling and Fuzzy Matriced’ Impacts Croise's Multiplication Appliquée a UN Classement (MICMAC) analysis approach of extant literature review, expert opinion and multiple iterations to arrive at the proposed model. The findings of the present research study show the linkages between the identified enablers: institutional culture, institutional capability and support, flexibility in the teaching-learning process, e-readiness, motivation, knowledge management practices, and technology. Knowledge management practices that include practices of capturing knowledge and sharing knowledge have emerged as the most significant enabler of e-learning. The model on barriers to e-learning shows the relationship between lack of required skills, lack of access to technology, quality concerns, time as a barrier, learner engagement as barriers to effective e-learning. Modelling of enablers and barriers and effective e-learning is a less explored area, particularly in the Indian context with special emphasis on the pandemic. The study was carried out to address this research gap.
An Integration of Blockchain and Machine Learning into the Health Care System

Currently, machine learning and blockchain play a predominant role in the health care sector. In health care, machine learning is most commonly used for administrative purposes, clinical decision support, diagnosis and treatment suggestions, and administrative tasks, such as automating medical bills, improving and managing health records, and others. Whereas blockchain technology is employed to ensure forge-free management of the drug supply chain, safeguarding the health record of a patient, enhancing security in order to protect medical services like insurance, and so on, machine learning allows for the analysis of thousands of different data points and the prediction of results, as well as the provision of timely risk ratings and accurate resource allocation. This chapter discusses and analyzes the implementation of different machine learning approaches, such as the decision tree algorithm, the random forest algorithm, the support vector machine algorithm, linear regression, deep learning models, and others, in medical applications such as glaucoma diagnosis, heart disease detection in diabetic patients, the detection of dementia, breast cancer detection, and the application of blockchain in for maintaining and securing the medical records of patients, avoiding scams in medical insurance, and managing the drug supply chain. These developing technologies act as powerful tools for clinicians for improving the health care systems.
Image pattern recognition by edge detection using discrete wavelet transforms

Edge is the high-frequency part of an image and represents location where abrupt change takes place in the intensity of luminescence. Edge detection is the basic step of the feature extraction and pattern recognition of any image. Wavelet transforms extract low and high-frequency information of any signal separately. In two-dimensional wavelet transforms, an image is decomposed into four sub-images the one approximation image and three different images (horizontal, vertical, and diagonal images) in each decomposition level. The differences’ images show how the neighboring pixels differ in the horizontal, vertical and diagonal directions. The approximation coefficients are forced to zero and differences’ coefficients are inverse wavelet transformed. As the reconstructed image shows the edges of the image and describes its pattern. Using Haar wavelet at decomposition level 1, 2 and 3, the image pattern recognition by edge detection is performed and discussed.
Experimental Implementation of Quantum Algorithm for Association Rules Mining

Recently, a quantum algorithm for a fundamentally important task in data mining, association rules mining (ARM), called qARM for short, has been proposed. Notably, qARM achieves significant speedup over its classical counterpart for implementing the main task of ARM, i.e., finding frequent itemsets from a transaction database. In this paper, we experimentally implement qARM on both real quantum computers and a quantum computing simulator via the IBM quantum computing platform. In the first place, we design quantum circuits of qARM for a 2$\times$2 transaction database (i.e., a transaction database involving two transactions and two items), and run it on four real five-qubit IBM quantum computers as well as on the simulator. For a larger 4$\times$4 transaction database which would lead to circuits with more qubits and a higher depth than the currently accessible IBM real quantum devices can handle, we also construct the quantum circuits of qARM and execute them on "aer\_simulator" alone. Both experimental results show that all the frequent itemsets from the two transaction databases are successfully derived as desired, demonstrating the correctness and feasibility of qARM. Our work may serve as a benchmarking, and provide prototypes for implementing qARM for larger transaction databases on both noisy intermediate-scale quantum devices and universal fault-tolerant quantum computers.
HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data

A pressing challenge in current dialogue systems is to successfully converse with users on topics with information distributed across different modalities.Previous work in multiturn dialogue systems has primarily focused on either text or table information.In more realistic scenarios, having a joint understanding of both is critical as knowledge is typically distributed over both unstructured and structured forms.We present a new dialogue dataset, HYBRIDIALOGUE, which consists of crowdsourced natural conversations grounded on both Wikipedia text and tables.The conversations are created through the decomposition of complex multihop questions into simple, realistic multiturn dialogue interactions.We propose retrieval, system state tracking, and dialogue response generation tasks for our dataset and conduct baseline experiments for each.Our results show that there is still ample opportunity for improvement, demonstrating the importance of building stronger dialogue systems that can reason over the complex setting of informationseeking dialogue grounded on tables and text.
Vision Robot Path Control Based on Artificial Intelligence Image Classification and Sustainable Ultrasonic Signal Transformation Technology

The unsupervised algorithm of artificial intelligence (AI), named ART (Adaptive Resonance Theory), is used to first roughly classify an image, that is, after the image is processed by the edge filtering technology, the image window is divided into 25 square areas of 5 rows and 5 columns, and then, according to the location of the edge of the image, it determines whether the robot should go straight (represented by S), turn around (represented by A), stop (T), turn left (represented by L), or turn right (represented by R). Then, after sustainable ultrasonic signal acquisition and transformation into digital signals are completed, the sustainable supervised neural network named SGAFNN (Supervised Gaussian adaptive fuzzy neural network) will perform an optimal path control that can accurately control the traveling speed and turning of the robot to avoid hitting walls or obstacles. Based on the above, this paper proposes the use of the ART operation after image processing to judge the rough direction, followed by the use of the ultrasonic signal to carry out the sustainable development of artificial intelligence and to carry out accurate speed and direction SGAFNN control to avoid obstacles. After simulation and practical evaluations, the proposed method is proved to be feasible and to exhibit good performance.
The Images of Man in Indonesian Literature

The research aims to describe masculinities contained in two Indonesian novels, Orang-orang Biasa and Rafilus. Specifically, the aims are divided into 2, namely (1) how the characters’ behaviors that are represented in the Orang-orang Biasa and Rafilus reviewed from the psychology of masculinities perspective are, and (2) how the impacts of characters’ behaviors on other people represented in the Orang-orang Biasa and Rafilus reviewed from the psychology of masculinities perspective. The theory used in this research was the psychology of masculinities which was related to the psychology of literature. The method used was descriptive qualitative which focused on narrative data exposure. The data collection technique used was a literature review. The data analysis technique was conducted with several steps, namely identifying the data, classifying the data, reducing the data, interpreting the data, describing the data, and verifying the data. The results showed that man's behaviors are represented in several forms. First, the men who have healthy behaviors are the ones who are strong, responsible, and honest. Second, the men who have unhealthy behaviors are the ones that have toxic masculinities which are represented in their behaviors that tend to hurt others and commit crimes.
Mathematics Teachers’ Perceptions on the Implementation of the Quizizz Application

Quizizz is an application proposed by the Ministry of Education Malaysia that can be used to obtain information about pupils’ learning acquisition throughout lessons. Quizizz integrates technology and gamification concepts that captivate students’ desire to learn. For home-based learning, Malaysian teachers were advised to guide pupils using communication mediums and applications so that the pupils would not be left behind in their learning. This study investigated the prior knowledge and perceptions of mathematics teachers regarding Quizizz, and their attitudes towards using this application. Furthermore, this study aimed to discover the interests among primary school mathematics teachers with different years of teaching experience in integrating the Quizizz application into their classes. We adopted a quantitative research method, using a questionnaire as research instrument. Fifty primary school teachers teaching mathematics in Johor were selected as respondents. The data obtained were analyzed using descriptive statistical analysis and inferential statistical analysis. Findings showed that more than half of the respondents (68%) had followed a course on using Quizizz, and 94% felt that they could access the Quizizz website easily. Most of the respondents (mean &gt; 3.67) had a positive attitude towards the Quizizz application. This study showed that teaching experience does not influence the teacher’s decision in implementing the Quizizz application in the teaching and learning process. Teachers are willing to learn new teaching methods.
The Impact of JPEG Compression on Prior Image Noise

JPEG compression is widely used to store digital images and extensive studies analysed its impact on the image quality; in particular the quantization noise and artefacts created by JPEG. Nevertheless, there is little work on the impact of JPEG compression on the noise already present in the image. In this paper, we propose a model predicting how the noise power is affected by JPEG compression. This allows for a better understanding the noise traces on the image, which is crucial for image forensic analysis and image restoration. An interactive demo for this article is available at https://ipolcore.ipol.im/demo/clientApp/demo.html?id=77777000136
Healthcare Cybersecurity Vulnerabilities

The healthcare industry sector is often considered a soft target for malicious actors. Having a large attack surface coupled with a focus directed toward patient care rather than security, often health organizations haven’t taken the necessary precautions to secure patient data or access to medical devices within their infrastructures. As the severity and the associated costs of cyber-attacks on entities within healthcare organizations continue to escalate, an increased effort within this industry to mitigate the risks associated with these vulnerabilities is necessary. This study seeks to present the most common types of healthcare attacks and their mitigation methodologies. Additionally, a discussion of how compliance with the GDPR in the European Union and the HIPPA regulation in the United States can positively affect a healthcare organization’s defensive posture.
Creation and evolution of ISO 12233, the international standard for measuring digital camera resolution

Thirty years ago, ISO/TC42 WG18, a newly created ISO working group on digital photography, began developing a standard to measure the spatial resolution of digital cameras.After years of proposals, testing, and analysis, consensus was reached on a test chart with tilted edge features for measuring spatial frequency response (SFR) and hyperbolic wedges for measuring visual and limiting resolution.The group ensured that the test chart and analysis software would be available internationally.First published in 2000, ISO 12233 is now used to measure cameras in a wide range of applications.It was revised in 2014 to define three new charts, a sine-wave modulated target in polar format, a low contrast e-SFR target, and the CIPA chart with software which computes a "human equivalent visual resolution" value.ISO 12233 is now being revised to provide improved results in challenging applications.This paper describes early resolution measurement approaches, and work in the 1990s to standardize the SFR method.It also describes enhancement made in later editions, including
A Hierarchical Goal-Biased Curriculum for Training Reinforcement Learning

Hierarchy and curricula are two techniques commonly used to improve training for Reinforcement Learning (RL) agents. Yet few works have examined how to leverage hierarchical planning to generate a curriculum for training RL Options. We formalize a goal skill that extends an RL Option with state-based conditions that must hold during training and execution. We then define a Goal-Skill Network that integrates a Hierarchical Goal Network, a variant of hierarchical planning, with goal skills as the leaves of the network. An automatically generated plan for a Goal-Skill Network correctly orders goal skills such that (1) it is a Goal-Biased Curriculum for training the goal skills, and (2) it can be executed to achieve top-level goals. In a set of six distinct gridworld environments using up to ten goal skills, we demonstrate that these contributions train nearly perfect policies significantly faster than learning a whole policy from scratch.
Considering Inter-Case Dependencies During Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning

In Case-Based Reasoning (CBR), knowledge gained from previously experienced problem-solving situations is stored as cases that can be used to solve similar upcoming problems. Although these cases act as independent knowledge entities, dependencies between cases are common in real-world scenarios, despite being only rarely considered during case retrieval or other CBR phases. In this paper, we introduce so-called inter-case dependencies, which are considered in the context of Process-Oriented CBR (POCBR). Therefore, we 1) derive requirements that must be satisfied for considering dependencies during the retrieval phase, 2) analyze which knowledge representations are suitable for representing dependencies between cases, and, 3) present our approach for Dependency-Guided Retrieval (DGR) that considers these dependencies between cases during the retrieval phase. In the experimental evaluation, the proposed DGR approach is compared to a regular CBR approach in case retrieval scenarios from the cooking domain. The results demonstrate that the use of the DGR approach leads to significantly reduced times for human problem-solving compared to regular CBR.
An introduction to the surface code

This chapter provides an introduction to the surface code, discussing details about how physical qubits are connected and operated to create logical qubits, how errors are handled, and what performance metrics are needed to generate logical qubits with performance that exceeds their underlying physical qubit performance.