Email Users Research Articles

Student-teacher out-of-class communication on engineering courses during the COVID-19 pandemic: from face-to-face to videocalls

ABSTRACT Out-of-class communication between teachers and students is essential throughout university engineering courses for in-depth explanations of the concepts covered in class. This study evaluates this aspect throughout the COVID-19 pandemic. Thus, a survey that addressed this issue was administered to students at the beginning (March 2020) and at the end (May 2020) of the COVID-19 lockdown in Spain, and at the end of each semester of the 2020/21 and 2021/22 academic years. The results were analyzed with statistical, qualitative, and mixed methods. E-mail usage was widely observed at the beginning of the pandemic, while the use of online videoconferencing tools progressed during that time, thanks to their temporal and spatial flexibility, and the direct and personal nature of student-teacher contact through those channels. Its success was linked to the prior training of teachers in the use of videoconferencing, the establishment of fixed schedules for the meetings, and the use of digital whiteboards that instantaneously display writing when discussing problems. According to the opinions of students, videocalls could be used for questions on theoretical and easy practical aspects despite the resumption of out-of-class face-to-face meetings. Videoconferencing tools for educational communication between engineering students and teachers seem to be here to stay.

Nudges can be both autonomy-preserving and effective: evidence from a survey and quasi-field experiment

Abstract Nudges are widely employed tools within organizations, but they are often criticized for harming autonomy and for being ineffective. We assess these two criticisms simultaneously: can nudges be both autonomy-preserving and effective in changing behavior? We developed three nudges – an opinion leader nudge, a rule-of-thumb and self-nudges – to reduce a particularly sticky behavior: email use. In a survey experiment of 4,112 healthcare employees, we tested their effect on perceived autonomy and subjective effectiveness. We also tested traditional policy instruments for comparison. Next, to assess objective effectiveness, we conducted a quasi-field experiment in a large healthcare organization with an estimate of 1,189 active email users. We found that each nudge in isolation, but especially when combined, was perceived to be both autonomy-preserving and effective, and more so than traditional policy instruments like an access limit or a monetary reward. We also found some evidence that the combination of all nudges decreased actual email use. This paper advances the literature by showing how innovations in nudge design improve nudges’ ability to be autonomy-preserving and effective.

Digital Marketing and Its Impacts on Small Businesses in India

Abstract: This project aims to investigate how online/digital marketing affects small firms' performance. In addition, looking into ambiance's current social media strategies to eventually decide how to contact the target audience. Identifying the platforms and technologies used by current small enterprises is also important. And those companies that use web marketing are productive in it. We are interested to examine if our present marketing strategies are effectively reaching as many clients as possible because we are aware that the target audience is expanding as a result of new technological advancements. By conducting a survey focused on social media and email usage, we will be able to determine which strategy is most successful and efficient for our company. Planning a poll that emphasizes social media and email usage will allow us to determine which approach is more successful and beneficial for our target demographic. Because of the broad age range of our clientele, asking customers where they desire to see a company's promotion is appropriate for ambiance and other small enterprises as well.

SISTEM KEAMANAN RUANGAN MENGGUNAKAN SENSOR PIR DENGAN BUZZER ALARM DAN EMAIL NOTIFIKASI PADA SMAN 15 KOTA BEKASI

Security systems using motion sensors provide the benefit of knowing crimes that occur using the prototype method, PIR sensors (Passive Infrared Receiver) as sensors to detect human motion so that, when human movement is detected by the PIR sensor, it will sound an alarm and send message notifications via e-mail users use e-mail, so users can find out about actions quickly. The research was carried out by installing motion detection sensors with buzzer alarms and email notifications in the principal, teacher, and administration rooms.

Email Spam Filtering Methods: Comparison and Analysis

Email is a common way of communication due to its cheap cost, efficacy, and efficiency. With the emerging of deep learning and machine learning methods, spam filter classification achieves boosting performance with fast inference speed. However, individuals and email servers are affected by spam, which causes issues with wasted time and computer storage space, as well as adverse effects on bandwidth. Even worse, email users are susceptible to scams and fraud that may result in financial loss. Therefore, it is essential to discover an efficient approach to filter spam from the entire number of emails. The purpose of this study is to evaluate and contrast the five most popular machine learning based spam filtering techniques, including Naive Bayes, Supported Vector Machine K-Nearest Neighbor, and XGBoost. We evaluate them based on their performance and efficacy. We hope this paper will help to conclude the current condition and help the researchers to improve better algorithms with higher accuracy.

Relationship between personality type and self-control with cyberbullying behavior in adolescents

Introduction: Personality type and self-control are internal factors that can determine the behaviour of adolescents, one of which is cyberbullying. Personality determines the nature of adolescents, and self-control is the control of adolescents in holding back their responses and emotions to show behaviour. This study aimed to determine the relationship between personality type and self-control with cyberbullying behaviour in adolescents at Y High School Banyuwangi Regency.Methods: The research method used is quantitative with a cross-sectional approach design. This study was conducted on 196 adolescents using a purposive sampling technique with several inclusion criteria: active students aged 15 to 17 years, users of smartphones, email, and social media. The data were collected by using several measuring instruments: the Eysenck Personality Questionnaire (EPQ) with a reliability test value of α = 0.762, the Brief Self Control Scale with a reliability test value using a T-test with r = 0.18, and the Cyber Aggressore Scale (CYB-AGS) with reliability test value α = 0.867. Logistic regression statistical tests analyzed the data. Results: This study revealed no relationship between personality type and cyberbullying behavior with p-value = 0.573 and a relationship between self-control and cyberbullying behaviour with p-value <0.001.Conclusions: There is no relationship between personality type and cyberbullying behaviour, and there is a relationship between self-control and cyberbullying behaviour at Y High School Banyuwangi Regency. Keywords: cyberbullying; personality type; self-control

A COMPARISON OF DIFFERENT KERNEL FUNCTIONS OF SVM CLASSIFICATION METHOD FOR SPAM DETECTION

Today, the use of e-mail, especially for formal online communication, is still often done. There is one common problem faced by e-mail users, which is the frequent receiving of spam messages. Spam messages are generally in the form of advertising or promotional messages in bulk to everyone. Of course this will cause inconvenience for people who receive the SPAM message. SPAM e-mails can be interpreted as junk messages or junk mail. So that spam has the nature of sending electronic messages repeatedly to the owner of the e-mail. This is abuse of the messaging system. One way to solve the spam problem is to identify spam messages for automatic message filtering. Several machine learning based methods are used to classify spam messages. In this study, a comparison was made between several kernel functions (i.e., linear, degree 1 polynomial, degree 2 polynomial, degree 3 polynomial, and RBF) of the SVM method to get the best SVM model in identifying spam messages. The evaluation results based on the Kaggle 1100 dataset showed that the best model were the SVM model with a linear kernel function and a degree 1 polynomial, where both models returned Precision = 0.99, Recall = 0.99, and F1-Score = 0.98. On the other hand, the RBF kernel produced lower performance in terms of Precision, Recall, and F1-Score of 0.95, 0.95, and 0.94, respectively.

The Study of AI-Mediated Communication and Socio-Cultural Language-Related Variables: Gmail Reply Suggestions

ABSTRACT The current research describes the use of e-mail reply suggestions from a socio-cultural perspective of some language-related variables. It also correlates how users perceive the use of e-mail reply suggestions to some variables: job, specialization, age, and gender. In this descriptive-inferential survey design, a self-developed questionnaire was distributed online to collect the data from a convenient sample of e-mail users. The analysis shows the study sample perceived medially the use of e-mail reply suggestions. There are statistically significant differences in the respondents’ means about using e-mail reply suggestions according to the variable of job for education, specialization for humanities, and gender for females. Also, ages of less than 30 and above 50 years are of significance in using e-mail reply suggestions. The study reveals a weak inverse correlation, however, statistical between using e-mail reply suggestions and the language of those suggestions. In addition, a statistically significant positive direct correlation is found between usability and culture and society of e-mail reply suggestions. In light of the findings, the study recommends the better implementation of artificial intelligence-mediated communication in e-mail reply suggestions in association with some sociolinguistic factors. Also, future related studies are suggested.

Cybersecurity Awareness: Investigating Students’ Susceptibility to Phishing Attacks for Sustainable Safe Email Usage in Academic Environment (A Case Study of a Nigerian Leading University)

With the advancement in information communication technology (ICT), cyber-attacks have become a global phenomenon, with email phishing at the topmost. Academic institutions' ICT infrastructures are one of many targets, thus the need to facilitate cybersecurity awareness among students. This research is aimed at investigating students’ susceptibility to phishing attacks for sustainable safe electronic mail (email) usage in the academic environment. Two email phishing tests were carried out during this research work to discover how students reacted to phish emails and understand how students respond to phish emails where all group members are recipients. Finally, questionnaires are administered to participants after completing the exercise to ascertain the students' awareness of phishing attacks based on received emails. The results show that 70.6% of college students surveyed are susceptible to this form of attack due to unawareness. In conclusion, recommendations are outlined on securing the academic community and ICT infrastructures to achieve a sustainable and Safe email usage environment.

Adoption and use of electronic-mail by staff in Federal College of Education (Technical), Gombe

The purpose of this study is to examine the adoption and use of electronic-mail by staff in Federal College of Education (Technical) Gombe. The objectives of the study include: to assess the level of usage of e-mail among staff of Federal College of Education, Gombe, to determine the impacts of adoption of e-mail among staff in the College and to identify the challenges associated with the use osf e-mail among the staff of the College under study. This study was guided by descriptive research design. The population of this study consisted of 1449 academic and nonacademic staff across various departments and units in the College. Simple random sampling in probability sampling techniques was used for this study and the sample size of the study was 302. The findings of the study revealed that respondents used email to share information faster than the conventional mail. The study also revealed that the adoption of email enhanced efficiency and promotes productivity in service delivery among the staff. The study concluded that though not all the public higher institutions in Gombe Staff participated in this study, the use of email were found to be useful among the staff. The study recommended that the adoption and use of e-mail should be given priority as a means of communication to enhance productivity in the delivery of information. Also, there is also the need to facilitate easy access to information as a means of enhancing impacts to improve public information system among the staff.

Manipulative tactics are the norm in political emails: Evidence from 300K emails from the 2020 US election cycle

We collect and analyze a corpus of more than 300,000 political emails sent during the 2020 US election cycle. These emails were sent by over 3000 political campaigns and organizations including federal and state level candidates as well as Political Action Committees. We find that in this corpus, manipulative tactics—techniques using some level of deception or clickbait—are the norm, not the exception. We measure six specific tactics senders use to nudge recipients to open emails. Three of these tactics—“dark patterns”—actively deceive recipients through the email user interface, for example, by formatting “from:” fields so that they create the false impression the message is a continuation of an ongoing conversation. The median active sender uses such tactics 5% of the time. The other three tactics, like sensationalistic clickbait—used by the median active sender 37% of the time—are not directly deceptive, but instead, exploit recipients’ curiosity gap and impose pressure to open emails. This can further expose recipients to deception in the email body, such as misleading claims of matching donations. Furthermore, by collecting emails from different locations in the US, we show that senders refine these tactics through A/B testing. Finally, we document disclosures of email addresses between senders in violation of privacy policies and recipients’ expectations. Cumulatively, these tactics undermine voters’ autonomy and welfare, exacting a particularly acute cost for those with low digital literacy. We offer the complete corpus of emails at https://electionemails2020.org for journalists and academics, which we hope will support future work.

Application of Domain Keys Identified Mail, Sender Policy Framework, Anti-Spam, and Anti-Virus: The Analysis on Mail Servers

Viruses spread through email are often sent by irresponsible parties that aim to infect email users' servers. This background encouraged the author to analyze the application of DKIM, SPF, anti-spam, and anti-virus to avoid spam, viruses, and spoofing activities. The goal is for the server to prevent spam, spoofing, and viruses to ensure the security and convenience of email users and prevent the impact of losses caused by them. The design and analysis of DKIM, SPF, anti-spam, and anti-virus applications use the NDLC methodology. The process includes designing spam, spoofing, and virus filtering systems and performing installation and configuration simulations. The next stage is implementation, during which the previously developed system is tested on the spam filtering system, spoofing, and viruses. The last stage is the monitoring stage, where supervision is conducted on the approach to determine its success level. This study concludes that applying the DKIM protocol can prevent spoofing through private and public key-matching methods for authentication. Meanwhile, the application of the SPF protocol can prevent spoofing by authorizing the IP address of the sending server. Additionally, SpamAssassin, ClamAV and Amavisd-New can prevent spam and viruses from entering by checking email headers, bodies, and attachments.

Detection of Spam Email

Spam, often known as unsolicited email, has grown to be a major worry for every email user. Nowadays, it is quite challenging to filter spam emails since they are made, created, or written in such a unique way that anti-spam filters cannot recognize them. In order to predict or categorize emails as spam, this paper compares and reviews the performance metrics of a few categories of supervised machine learning techniques, including Svm (Support Vector Machine), Random Forest, Decision Tree, Cnn, (Convolutional Neural Network), Knn(K Nearest Neighbor), Mlp(Multi-Layer Perceptron), Adaboost (AdaptiveBoosting), and Nave Bayes algorithm. Thegoal of this study is to analyze the specificsor content of the emails, discover a limited dataset, and create a classification model that can predict or categorize whether spam is present in an email. Transformers’ Bidirectional Encoder Representations) has been optimized to perform the duty of separating spam emails from legitimate emails (Ham). To put the text’s context into perspective, Bert uses attention layers. Results are contrasted with a baseline Dnn (deep neural network) modelthat consists of two stacked Dense layers and a Bilstm (bidirectional Long Short-Term Memory) layer. Results are also contrasted with a group of traditional classifiers, including k- Nn (k-nearest neighbours) and Nb (Naive Bayes). The model is tested for robustness andpersistence using two open-source data sets, one of which is utilized to train the model.

FEATURE ENGINEERING WITH SENTENCE SIMILARITY USING THE LONGEST COMMON SUBSEQUENCE FOR EMAIL CLASSIFICATION

Feature selection plays a prominent role in email classification since selecting the most relevant features enhances the accuracy and performance of the learning classifier. Due to the exponential increase rate in the usage of emails, the classification of such emails posed a fitting problem. Therefore, there is a requirement for a proper classification system. Such an email classification system requires an efficient feature selection method for the accurate classification of the most relevant features. This paper proposes a novel feature selection method for sentence similarity using the longest common subsequence for email classification. The proposed feature selection method works in two main phases: First, it builds the longest common subsequence vector of features by comparing each email with all other emails in the dataset. Later, a template is constructed for each class using the closest features of emails of a particular class. Further, email classification is tested for unseen emails using these templates. The performance of the proposed method is compared with traditional feature selection methods such as TF-IDF, Information Gain, Chi-square, and semantic approach. The experimental results showed that the proposed method performed well with 96.61% accuracy.

Auto-response Email based on User Habits with Privacy Model Approach and Cloud Adoption

Email is an application that is used as a means of sending letters via a computer network or the internet. Many people whose work is done relate to other people, and email is the main channel through which work and related information can be distributed. An email has a feature that is to enable automatic responses. This feature is in the form of an automatic reply that will be sent in response to emails received when the email user is unable to respond. However, this feature still uses the default email to reply to receive emails. So in this study, the researcher proposes a new system to develop this feature by using user habits when the user replies to incoming emails. Using text processing techniques and sentiment analysis, we build a retrieval algorithm that finds similar cases outside of exact matches. This study uses a Cloud Adoption and is designed to use Power Automate to improve the performance of the system.

Voice Based Email System for Blind

The communication among people is sharing of information. The Internet is widely used in almost all communication applications. E-mails are the most widely used and reliable way to communicate with each other. The usage of email is quite easy for regular users but when it comes to the visually imapired persons they are unable to access the system. for those visually impaired people, we are presenting the voice based email system under the principle of voice controlling. The mails which are present in our Gmail accounts those all text mails are converted into speech (Voice) and spoken by the assistant in our systems. The system is totally based on voice reaction which will make it easy to use and effective to utilize. The people who are visually impaired persons are easy to access this voice based email system and can able to listen the all mails present in the inbox in form of voice. This will provide easy accessibility to the visually impaired persons in the world. The Contribution made by the research has enabled the blind people to send and receive voice based email messages in their native languages with the help of computer.

Application of Natural Language Processing and Machine Learning Boosted with Swarm Intelligence for Spam Email Filtering

Spam represents a genuine irritation for email users, since it often disturbs them during their work or free time. Machine learning approaches are commonly utilized as the engine of spam detection solutions, as they are efficient and usually exhibit a high degree of classification accuracy. Nevertheless, it sometimes happens that good messages are labeled as spam and, more often, some spam emails enter into the inbox as good ones. This manuscript proposes a novel email spam detection approach by combining machine learning models with an enhanced sine cosine swarm intelligence algorithm to counter the deficiencies of the existing techniques. The introduced novel sine cosine was adopted for training logistic regression and for tuning XGBoost models as part of the hybrid machine learning-metaheuristics framework. The developed framework has been validated on two public high-dimensional spam benchmark datasets (CSDMC2010 and TurkishEmail), and the extensive experiments conducted have shown that the model successfully deals with high-degree data. The comparative analysis with other cutting-edge spam detection models, also based on metaheuristics, has shown that the proposed hybrid method obtains superior performance in terms of accuracy, precision, recall, f1 score, and other relevant classification metrics. Additionally, the empirically established superiority of the proposed method is validated using rigid statistical tests.

Leveraging Mobile Sensing and Bayesian Change Point Analysis to Monitor Community-scale Behavioral Interventions: A Case Study on COVID-19

During pandemics, effective interventions require monitoring the problem at different scales and understanding the various tradeoffs between efficacy, privacy, and economic burden. To address these challenges, we propose a framework where we perform Bayesian change-point analysis on aggregate behavior markers extracted from mobile sensing data collected during the COVID-19 pandemic. Results generated by 598 participants for up to four months reveal rich insights: We observe an increase in smartphone usage around February 10th, followed by an increase in email usage around February 27th and, finally, a large reduction in participant’s mobility around March 13th. These behavior changes overlapped with important news events and government directives such as the naming of COVID-19, a spike in the number of reported cases in Europe, and the declaration of national emergency by President Trump. We also show that our detected change points align with changes in large scale external sources, including number of COVID-19 tweets, COVID-19 search traffic, and a large-scale foot traffic data collected by SafeGraph, providing further validation of our method. Our results show promise towards the feasibility of using mobile sensing to understand communities’ responses to public health interventions.

Attitudes, practices and experiences of medical specialists towards email communication with their patients.

The medical profession has been slow in embracing email as a means of improving communication with patients. To explore the attitudes, practices and experiences of senior medical specialists towards email communication with their patients. A cross-sectional qualitative interview of 30 medical staff specialists employed by Canberra Health Services, administered through one-on-one interviews conducted between June and August 2020 (several months into the COVID-19 pandemic). Main outcome measures are the comments extracted from the interviews that were categorised into five domains: (i) integration of email use with patients; (ii) issues addressed through email; (iii) benefits of email communication; (iv) concerns and barriers to email communication; and (v) practice pointers for email use with patients. Regular email correspondence with patients was not widespread. The main benefits identified were improved efficiency and flexibility, especially in the context of managing chronic disease and patient follow up. Participants also identified barriers, including time commitments, privacy and confidentiality, patient expectations and potential for misuse. Most participants were hesitant to endorse email with patients in their practices, citing concerns over the utility and safety of the medium and lack of established protocols and recommendations for email usage. There is a want and need for comprehensive and accessible professional guidance on email use with patients. Our results indicate opportunities to inform good clinical practice in respect of doctor-patient relationships, clinical workloads and risk management. There is also a need for formal guidelines on emailing with patients. The COVID-19 pandemic has heightened the need for such guidelines.

A Novel Approach for Send Time Prediction on Email Marketing

In the digital world, the demand for better interactions between subscribers and companies is growing, creating the need for personalized and individualized experiences. With the exponential growth of email usage over the years, broad flows of campaigns are sent and received by subscribers, which reveals itself to be a problem for both companies and subscribers. In this work, subscribers are segmented by their behaviors and profiles, such as (i) open rates, (ii) click-through rates, (iii) frequency, and (iv) period of interactions with the companies. Different regressions are used: (i) Random Forest Regressor, (ii) Multiple Linear Regression, (iii) K-Neighbors Regressor, and (iv) Support Vector Regressor. All these regressions’ results were aggregated into a final prediction achieved by an ensemble approach, which uses averaging and stacking methods. The use of Long Short-Term Memory is also considered in the presented case. The stacking model obtained the best performance, with an R2 score of 0.91 and a Mean Absolute Error of 0.204. This allows us to estimate the week’s days with a half-day error difference. This work presents promising results for subscriber segmentation based on profile information for predicting the best period for email marketing. In the future, subscribers can be segmented using the Recency, Frequency and Monetary value, the Lifetime Value, or Stream Clustering approaches that allow more personalized and tailored experiences for subscribers. The latter tracks segments over time without costly recalculations and handles continuous streams of new observations without the necessity to recompile the entire model.