Reviewerly/exHarmony huggingface dataset:
ANALISIS SENTIMEN ULASAN APLIKASI SHAZAM DI GOOGLE PLAY STORE MENGGUNAKAN SUPPORT VECTOR MACHINE

Authors:
raw_author_name: None Meilinda

Abstract: Shazam is a popular music recognition app on the Google Play Store. Shazam allows users to discover new songs and identify songs that are playing around them. In addition, it provides song lyrics, music videos, and song recommendations. User reviews give an idea of how users perceive the application, be it positive or negative. In this study, sentiment analysis was conducted on Shazam user reviews on the Google Play Store. The researchers used a Support Vector Machine (SVM) model to classify user reviews into two sentiments, namely positive and negative. Results show that SVM has 84% accuracy in predicting the sentiment of Shazam reviews. Furthermore, it can be shown that Shazam gets five times more positive responses than negative responses. Therefore, SVM is better at predicting positive reviews than negative reviews. This research can help interested parties to understand how users perceive the app as a whole.
Communication Prototype for Post-Stroke Patients Using Electrooculography (EOG)

Authors:
raw_author_name: Erika Loniza
raw_author_name: Wisnu Kartika

Abstract: The background of this prototype is meant for post-stroke patients who have difficulties owing to trouble completing daily activities, particularly speaking with others. They have trouble communicating, which reduces their quality of life after a stroke. The goal of developing novel communication aids for post-stroke patients is to make it easier for post-stroke patients to communicate with caretakers. Electrooculography (EOG) signals generated by the movement of the eye muscles during eye glances collected by the MaM Sense sensor are employed in post-stroke communication aids. The microcontroller processes the method's command signal, which is subsequently displayed on a 20 x 4 Character LCD. For the four communications utilized, the MaM Sense sensor reading was successful 80.5% of the time. Thus, communication tools for post-stroke patients can help them communicate while also assisting caretakers in understanding the patient's wants. Improvements will be made in the design and wireless technologies for EOG signal recovery in the future.
The Effect of Coaching Leadership on Employee Voice Behavior: The Mediating Role of Thriving at Work and the Moderating Role of Person-Supervisor Fit

Authors:
raw_author_name: Daehyun Kim

Abstract: This study has two purposes. First, it aims to verify the mediating role of thriving at work in the relationship between coaching leadership and employee voice behavior. Second, it looks at the moderating role of person-supervisor fit in terms of the influence of coaching leadership. Data collected from 174 supervisor-subordinate pairs was used for the analysis. The results show that coaching leadership was positively related to thriving at work, and thriving at work was positively related to voice behavior. In particular, thriving at work mediated coaching leadership and voice behavior. The positive relationship between coaching leadership and thriving at work was weaker with a better person-supervisor fit, and the moderated mediation effect was also weaker in this scenario. Based on the results, the limitations of this study are acknowledged, and theoretical and empirical implications are suggested, as well as a fruitful future direction of research.
PhishScanner : A Web-Based Tool for Detection of Phishing Websites Using Machine Learning

Authors:
raw_author_name: Sainath Patil
raw_affiliation_string: Assistant Professor, Vidyavardhini's College of Engineering and Technology, K.T. Marg, Vartak College Campus, Vasai Road (W), District Palghar, Vasai, Maharashtra - 401 202

Abstract: Phishing is a common attack that tricks an individual to reveal personal information through fake websites. The main goal is to steal important data, including logins for social media accounts, bank account numbers, passwords, and usernames. The attacker uses his/her website that is visually and semantically similar to the actual website. Over the years there have been multiple attacks of phishing and many people have lost huge sums of money by becoming victims of these attacks. Attackers frequently employ fake emails or websites to trick victims into clicking hazardous links or downloading malicious attachments. The victim's login details and account information may be accessible to the attacker, thanks to these acts. The complexity of phishing attacks has increased, making it more challenging for the typical user to tell whether a link in an email or a message is authentic or not. To detect these types of websites there are different methods such as blacklist approach, content based approach etc. Detecting attacks through traditional methods such as blacklists can be unreliable as it has become simpler to register new domains, and maintaining a comprehensive and current database is difficult. An internet user can interact with a web application through a graphical user interface by running it on a web server and accessing it through a web browser. Therefore, we developed a web application whose primary objective is to detect Phishing URLs by utilizing advanced machine learning models that analyze features extracted from the website. After reviewing various classification algorithms, we decided on adopting the Random Forest Algorithm because of its high accuracy of 0.965. Random Forest Algorithm is a well-known algorithm for Machine Learning that predicts using an ensemble of decision trees. It has proven to be effective in a wide range of classification tasks.
Setting up classroom libraries in rural areas: The case of Mogodumo circuit in Limpopo

Authors:
raw_author_name: MM Maja
raw_affiliation_string: University of South Africa, Pretoria, South Africa

Abstract: There is an urgent need to establish classroom libraries, especially in rural areas where learners hardly come across any reading material. The purpose of this study was to investigate how teachers set up classroom libraries in rural areas of the Mogodumo circuit in the Limpopo province of South Africa. The study was qualitative in nature. Fifteen English First Additional Language (EFAL) teachers from 12 schools participated in the study. Observation and semi-structured interviews were employed to collect data. It was found that while all participants recognised the importance of reading for their learners, only 7 of the 15 teachers managed to establish classroom libraries. The study revealed that teacher self-efficacy (TSE) played a crucial role in setting up classroom libraries, given the serious challenges of funding, resources and support experienced by rural schoolteachers. Teachers with high TSE managed to establish classroom libraries by using their own time, money, and resources, while those with low TSE failed to establish classroom libraries. We recommend that school managers should make time and resources available to rural schoolteachers to enable them to establish classroom libraries.
UTILIZATION OF THE ANDROID APPLICATION SI-RAJA BUMIL (ESTIMATED FETAL WEIGHT OF PREGNANT WOMEN) IN MONITORING FETAL GROWTH IN MIDWIFE AT INDEPENDENT PREMISES IN TANJUNGPINANG CITY

Authors:
raw_author_name: Darwitri Darwitri
raw_affiliation_string: PUI PK Kemilau Poltekkes Kemenkes Tanjungpinang
raw_author_name: None Ristina Rosauli Harianja
raw_affiliation_string: PUI PK Kemilau Poltekkes Kemenkes Tanjungpinang
raw_author_name: None Metasari Sihaloho
raw_affiliation_string: PUI PK Kemilau Poltekkes Kemenkes Tanjungpinang
raw_author_name: None Kartika Sri Dewi Batubara
raw_affiliation_string: PUI PK Kemilau Poltekkes Kemenkes Tanjungpinang

Abstract: Estimated fetal weight is useful for tracking fetal growth in the womb, allowing for the early detection of aberrant fetal growth. With the advancement of technology, Android users can be used as a channel for health education information. The objective of this study was to look at how utilizing the Android application Si-RAJA Bumil (Estimated Fetal Weight of Pregnant Women) affects pregnant women’s understanding of fetal growth monitoring. This study applied a quasi-experimental design with two groups pre-testing and post-testing. Samples of 60 pregnant women were collected using the Simple Random Sampling approach and divided into two groups: the intervention group and the control group. The Midwife Independent Practice in Tanjungpinang City collected data in September 2023. Wilcoxon and Mann-Whitney tests were used to analyze data. The knowledge value of pregnant women differs significantly between groups who do not utilize the Si-RAJA Bumil android application and groups that do, with a p-value of 0.003. Pregnant mothers are expected to be able to monitor their foetus’ growth and development using technological media such as the Si-RAJA Bumil application.
A Survey on Path Planning Algorithms for Unmanned Aerial Vehicles Using Bio-Inspired Optimization Techniques

Authors:
raw_author_name: Arpan Adhikary
raw_affiliation_string: Haldia Institute of Technology, India

Abstract: The rapid development of unmanned aerial vehicles (UAVs) and UAV-based applications has been increased in the recent past due to the advancement in software and electronics industry. Use of UAVs are considered to be a very efficient and useful platform that can deeply monitor the critical infrastructures around the geographical areas. UAVs are also useful for data collection through different wireless sensor networks. Based on the collected data, an optimal path can be formed. Bio-inspired algorithms are inspired from the principles of the biological evolution of nature. The recent trends tend to employ the bio-inspired optimization techniques that are best-suitable for handling strenuous optimization problems. In this chapter, the authors investigate different bio-inspired algorithms for the UAV path planning over the last decade. They compared the working principles, key features, advancements, and limitations of different path planning algorithms. Furthermore, the challenges and future research scopes are also discussed and summarized.
There’s a Strong Wind a’Blowing re Security Disclosures: Lessons Learned from SolarWinds — An analysis of the significance of the SolarWinds case for companies with making security statements on websites and in public filings

Authors:
raw_author_name: John Beardwood

Abstract: Abstract For years now, we have been advising clients to be cautious about including overly broad, or overly detailed, statements in their online privacy policies, noting that the FTC has in the past interpreted such statements as being representations to the market - such that false or misleading statements made therein could constitute misrepresentations. The Securities Exchange Commission complaint last year filed against SolarWinds Corp. and Timothy G. Brown (between July 2017 and December 2020, the Vice President of Security and Architecture and head of the Information Security group, and as of January 2021, the Chief Information Security Officer, for SolarWinds Corp.) has now made it clear that the same risk applies to public statements regarding the state of security of an organization. The SEC complaint raises additional related issues, however: namely, to what extent are organizations required to detail instances of non-compliance with their security policies in their public security disclosures; and to what extent should software companies be held to a higher standard of cybersecurity disclosure. This article proves the background to the SEC complaint (I.); provides a overview of the SEC complaint’s critique of the online security statement published by SolarWinds, and the security disclosures in the securities filings of SolarWinds (II.); outlines the challenges posed by the SEC approach in the SEC complaint and the resulting blowback, in particular from chief information security officers (III.); highlights three key lessons learned from the SEC complaint (IV.); and concludes (V.) with a warning as to what this means for organizations publishing online security statements.
A Study on College Students’ Learning Attitude During Synchronous and Asynchronous Video-Based Group Activities for English Presentation — A Secondary Publication

Authors:
raw_author_name: Hee-Joo Im

Abstract: The purpose of the study is to explore college students’ perceptions/attitudes during synchronous and asynchronous group activities. The survey comprised a 10-item, 5-point Likert-type questionnaire and open-ended questions. In the study, two groups, a synchronous group and an asynchronous group, participated in four activities during one semester. For the data collection, the researcher surveyed 116 students who are in their 1st year of college and collected their final tests, an oral presentation. The findings are as follows. Firstly, between the two groups, the synchronous group showed higher mean scores for online group participation. Secondly, the synchronous group also indicated higher mean scores for English learning, speaking confidence, and interest in learning English. Lastly, both groups mentioned communication with team members, improvement in English speaking and pronunciation, increased self-confidence, and other elements as advantages of group work. In contrast, disadvantages included technology problems, decreased self-confidence, inconveniences when meeting with team members online, and others. The implications for the study are: firstly, online group activity can reduce isolation and increase interaction; secondly, the online video-based platform, Flipgrid, can help students improve English speaking skills by observing their own or peers’ videos; lastly, instructors can selectively use the synchronous and asynchronous group activities to suit their educational needs.
A Probe into the Integration Concept of Blended Teaching Mode of College English in Application-Oriented Universities

Authors:
raw_author_name: Chang Xu

Abstract: Blended teaching is the product of the organic combination of classroom teaching and online learning. The practice of college English teaching in application-oriented colleges shows that the construction of the integration concept of blended teaching mode will play a good role in promoting the quality of college English teaching. The organization and implementation of the blended teaching mode of college English should pay more attention to the integration of all elements of college English core literacy and online teaching resources, and establish a sense of integration in teaching concept, content, mode, and evaluation, so as to further promote the course reform of college English and realize the goal of cultivating English talents with emphasis on application practice.
A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation

Authors:
raw_author_name: Qi Zhao
raw_affiliation_string: Department of Electronic and Information Engineering, Beihang University, Beijing, P.R. China
raw_author_name: Binghao Liu
raw_affiliation_string: Department of Electronic and Information Engineering, Beihang University, Beijing, P.R. China
raw_author_name: Shuchang Lyu
raw_affiliation_string: Department of Electronic and Information Engineering, Beihang University, Beijing, P.R. China

Abstract: Few-shot segmentation focuses on the generalization of models to segment unseen object with limited annotated samples. However, existing approaches still face two main challenges. First, huge feature distinction between support and query images causes knowledge transferring barrier, which harms the segmentation performance. Second, limited support prototypes cannot adequately represent features of support objects, hard to guide high-quality query segmentation. To deal with the above two issues, we propose self-distillation embedded supervised affinity attention model to improve the performance of few-shot segmentation task. Specifically, the self-distillation guided prototype module uses self-distillation to align the features of support and query. The supervised affinity attention module generates high-quality query attention map to provide sufficient object information. Extensive experiments prove that our model significantly improves the performance compared to existing methods. Comprehensive ablation experiments and visualization studies also show the significant effect of our method on few-shot segmentation task. On COCO-20i dataset, we achieve new state-of-the-art results. Training code and pretrained models are available at https://github.com/cv516Buaa/SD-AANet.
Clustering longitudinal ordinal data via finite mixture of matrix-variate distributions

Authors:
raw_author_name: Julien Jacques
raw_affiliation_string: Entrepôts, Représentation et Ingénierie des Connaissances; Université Lumière - Lyon 2

Abstract: In social sciences, studies are often based on questionnaires asking participants to express ordered responses several times over a study period. We present a model-based clustering algorithm for such longitudinal ordinal data. Assuming that an ordinal variable is the discretization of a underlying latent continuous variable, the model relies on a mixture of matrix-variate normal distributions, accounting simultaneously for within- and between-time dependence structures. The model is thus able to concurrently model the heterogeneity, the association among the responses and the temporal dependence structure. An EM algorithm is developed and presented for parameters estimation. An evaluation of the model through synthetic data shows its estimation abilities and its advantages when compared to competitors. A real-world application concerning changes in eating behaviours during the Covid-19 pandemic period in France will be presented.
Importance of GWAS Risk Loci and Clinical Data in Predicting Asthma Using Machine-learning Approaches

Authors:
raw_author_name: Zan-Mei Qin
raw_affiliation_string: Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
raw_author_name: Hai-Li Li
raw_affiliation_string: Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China

Abstract: To understand the risk factors of asthma, we combined genome-wide association study (GWAS) risk loci and clinical data in predicting asthma using machine-learning approaches.A case-control study with 123 asthmatics and 100 controls was conducted in the Zhuang population in Guangxi. GWAS risk loci were detected using polymerase chain reaction, and clinical data were collected. Machine-learning approaches were used to identify the major factors that contribute to asthma.A total of 14 GWAS risk loci with clinical data were analyzed on the basis of 10 times the 10-fold cross-validation for all machine-learning models. Using GWAS risk loci or clinical data, the best performances exhibited area under the curve (AUC) values of 64.3% and 71.4%, respectively. Combining GWAS risk loci and clinical data, the XGBoost established the best model with an AUC of 79.7%, indicating that the combination of genetics and clinical data can enable improved performance. We then sorted the importance of features and found the top six risk factors for predicting asthma to be rs3117098, rs7775228, family history, rs2305480, rs4833095, and body mass index.Asthma-prediction models based on GWAS risk loci and clinical data can accurately predict asthma, and thus provide insights into the disease pathogenesis.
Joint Sparse Locality Preserving Regression for Discriminative Learning

Authors:
raw_author_name: Weilin Huang
raw_affiliation_string: Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Zhihui Lai
raw_affiliation_string: Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Heng Kong
raw_affiliation_string: Department of Breast and Thyroid Surgery, Baoan Central Hospital of Shenzhen, Shenzhen, China
raw_author_name: Junhong Zhang
raw_affiliation_string: Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

Abstract: Ridge Regression (RR) is a classical method that is widely used in multiple regression analysis. However, traditional RR does not take the local geometric structure of data into consideration for discriminative learning and it is sensitive to outliers as it is based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2}$</tex-math></inline-formula> -norm. To address this problem, this article proposes a novel method called Joint Sparse Locality Preserving Regression (JSLPR) for discriminative learning. JSLPR not only applies <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2,1}$</tex-math></inline-formula> -norm on both loss function and regularization term but also takes the local geometric structure of the data into consideration. The use of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2,1}$</tex-math></inline-formula> -norm can guarantee the robustness to outliers or noises and the joint sparsity for effective feature selection. Taking the local geometric structure into consideration can improve the performance of the feature extraction and selection method when the data lie on a manifold. To solve the optimization problem of JSLPR, an iterative algorithm is proposed and the convergence of the algorithm is also proven. Experiments on four famous face databases are conducted and the results show the merit of the proposed JSLPR on feature extraction and selection.