Undesirable Messages Research Articles

Assessor discomfort and failure to fail in clinical performance assessments

BackgroundAssessment of trainee performance in the workplace is critical to ensuring high standards of clinical care. However, some supervisors find the task to be challenging, and may feel unable to deliver their true judgement on a trainee’s performance. They may ‘keep MUM’ (that is, keep mum about undesirable messages) and fail to fail an underperforming trainee. In this study, we explore the effect of discomfort on assessors.MethodsUsing a survey method, supervisors of trainees in the Australasian College of Dermatologists were asked to self-report experiences of discomfort in various aspects of trainee workplace assessment and for their engagement in MUM behaviours including failure to fail.ResultsSixty-one responses were received from 135 eligible assessors. 12.5% of assessors self-reported they had failed to fail a trainee and 18% admitted they had grade inflated a trainee’s score on a clinical performance assessment in the previous 12-month period. Assessors who reported higher levels of discomfort in the clinical performance assessment context were significantly more likely to report previously failing to fail a trainee. The study did not reveal significant associations with assessor demographics and self-reports of discomfort or MUM behaviours.ConclusionsThis study reveals the impact of assessor discomfort on the accuracy of assessment information and feedback to trainees, including as a contributing factor to the failure to fail phenomenon. Addressing assessor experience of discomfort offers one opportunity to impact on the complex and multifactorial issue that failure to fail represents.

Third-Person Perception as a Mediator of the Influence of Social Networking Sites Use & Personal Relevance to the 6M Preventive Behavioural Intention: A Study on the Online Covid-19 News Audience in Jakarta Special Capital Region

News coverage on Covid-19 is important as it provides people with important updates as well as crucial information for avoiding infection. Previous studies have shown undesirable messages such as Covid-19 news that circulates in social networking sites can trigger ‘third-person perception (TPP)’, which is a phenomenon in which audiences presume a message exerts more influence upon people other than themselves. It is crucial to examine whether audiences experience TPP during consumption of pandemic news – especially when there is a possible negative correlation between TPP and protective behavioural intentions. Previous studies showed empirical evidence when users used Covid-19 news in SNS (consume, contribute or create), such use could reduce TPP and was also correlated with preventive behavioural intentions. This suggests that three types of SNS usage and personal relevance have direct relationship with protective behavioural intentions, as well as indirect relationship with TPP, where it has a mediating role. To test these hypotheses, explanatory research of a positivistic paradigm and quantitative approach was administered. This research utilized quota sampling to collect data from 400 respondents through online questionnaire. Based on the structural analysis, there is no significant relationship between TPP and preventive behavioural intentions. Analyses also did not support the hypotheses that TPP mediated: 1.) SNS usage and preventive behavioural intensions; 2.) and personal relevance and preventive behavioural intensions. Personal relevance was found to be an important construct as it had a positive influence toward preventive behavioural intentions and a negative relationship with TPP. It is suggested that low level of interaction with Covid-19 news in Social Networking Sites is due to the respondents only incidentally consumed Covid-19 news. It is also suggested that non-existing relationship between third-person perception and preventive behavioural intention is due to the mandatory nature of health protocol in Indonesia.

Image SPAM Detection Using ML and DL Techniques

Abstract Since e-mail is one of the most common places to send messages, spammers have, in recent years, targeted it as a preferred way of distributing undesired messages (spam) to several users to spread viruses, cause destruction, and obtain user's information. Spam images are considered one of the known spam types. The spammer processes images and changes their characteristics, especially background colour, font type, or adding artefacts to the images to spread spam. In this paper, we proposed a spam detection model using Several ML (Random-Forest (RF), Decision-Tree (DT), KNearest Neighbor (KNN), Support-Vector Machine (SVM), NaïveBays (NB), and Convolutional Neural Network (CNN)). Several experiments evaluate the efficiency and performance of the (ML) algorithms for spam detection. Using the Image Spam Hunter Dataset extracted from real spam e-mails, the proposed model achieved over 99% accuracy on spam image detection. Keywords: SPAM, Machine Learning, Image Classification, Feature Extraction, Deep Learning.

Asymmetric Leaky Private Information Retrieval

Information-theoretic formulations of the private information retrieval (PIR) problem have been investigated under a variety of scenarios. Symmetric private information retrieval (SPIR) is a variant where a user is able to privately retrieve one out of K messages from N non-colluding replicated databases without learning anything about the remaining K-1 messages. However, the goal of perfect privacy can be too taxing for certain applications. In this paper, we investigate if the information-theoretic capacity of SPIR (equivalently, the inverse of the minimum download cost) can be increased by relaxing both user and DB privacy definitions. Such relaxation is relevant in applications where privacy can be traded for communication efficiency. We introduce and investigate the Asymmetric Leaky PIR (AL-PIR) model with different privacy leakage budgets in each direction. For user privacy leakage, we bound the probability ratios between all possible realizations of DB queries by a function of a non-negative constant ϵ. For DB privacy, we bound the mutual information between the undesired messages, the queries, and the answers, by a function of a non-negative constant δ. We propose a general AL-PIR scheme that achieves an upper bound on the optimal download cost for arbitrary ϵ and δ. We show that the optimal download cost of AL-PIR is upper-bounded as D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> (ϵ,δ) ≤ 1+\frac 1N-1-\frac δe <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϵ</sup> N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-1</sup> -1. Second, we obtain an information-theoretic lower bound on the download cost as D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> (ϵ,δ) ≥ 1+\frac 1Ne <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϵ</sup> -1-\frac δ(Ne <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϵ</sup> ) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-1</sup> -1. The gap analysis between the two bounds shows that our AL-PIR scheme is optimal when ϵ = 0, i.e., under perfect user privacy and it is optimal within a maximum multiplicative gap of \frac N-e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-ϵ</sup> N-1 for any ϵ > 0 and δ > 0.

Utilizing Interference by Network Coding for Simultaneous Wireless Information and Power Transfer

This letter puts forth a novel way to manage interference in simultaneous wireless information and power transfer (SWIPT) systems by utilizing physical-layer network coding (PNC). We consider multi-user multiple-input single-output (MU-MISO) broadcast channels. Unlike the conventional zero-forcing scheme, the PNC-SWIPT scheme introduced in this letter does not eliminate the interfering signals at the receivers. The PNC-SWIPT scheme cancels out the undesired messages over a finite field during PNC demodulation for information decoding (ID). It preserves the interfering signals which are beneficial energy sources for energy harvesting (EH). Simulation results show that the PNC-SWIPT scheme achieves superior EH performance than conventional interference mitigation schemes, such as zero-forcing (ZF) and maximizing EH precoding (Max EH), while maintaining the same ID performance. The performance improvement of the PNC-SWIPT scheme mainly comes from canceling out the interference at the receivers from the aspect of data by PNC, without eliminating the interfering signals which are beneficial energy sources.

Relevant SMS Spam Feature Selection Using Wrapper Approach and XGBoost Algorithm

In recent years with the widely usage of mobile devices, the problem of SMS Spam increased dramatically. Receiving those undesired messages continuously can cause frustration to users. And sometimes it can be harmful, by sending SMS messages containing fake web pages in order to steal users’ confidential information. Besides spasm number of hazardous actions, there is a limited number of spam filtering software. According to this paper, XGBoost algorithm used for handling SMS spam detection problem. Number of structural features was collected from previous studies. 15 structural features were extracted from Tiago’s dataset, which is the most frequently used dataset by researchers. For selecting the optimal relevant features, two different types of wrapper feature selection algorithms were used in order to reduce and select best relevant features. The accuracy and performance obtained by the selected features via sequential backward selection method was better comparing to sequential forward selection method. The extracted nine optimal features can be a good representation of a spam SMS message. Additionally, the classification accuracy obtained by the proposed method using nine optimal features with XGBoost algorithm is 98.64 using 10-fold cross validation.

The Capacity of Private Information Retrieval With Eavesdroppers

We consider the problem of private information retrieval (PIR) with colluding servers and eavesdroppers (abbreviated as ETPIR). The ETPIR problem is comprised of $K$ messages, $N$ servers where each server stores all $K$ messages, a user who wants to retrieve one of the $K$ messages without revealing the desired message index to any set of $T$ colluding servers, and an eavesdropper who can listen to the queries and answers of any $E$ servers but is prevented from learning any information about the messages. The information theoretic capacity of ETPIR is defined to be the maximum number of desired message symbols retrieved privately per information symbol downloaded. We show that the capacity of ETPIR is $C = \left( 1- \frac{E}{N} \right) \left(1 + \frac{T-E}{N-E} + \cdots + \left( \frac{T-E}{N-E} \right)^{K-1} \right)^{-1}$ when $E < T$, and $C = \left( 1 - \frac{E}{N} \right)$ when $E \geq T$. To achieve the capacity, the servers need to share a common random variable (independent of the messages), and its size must be at least $\frac{E}{N} \cdot \frac{1}{C}$ symbols per message symbol. Otherwise, with less amount of shared common randomness, ETPIR is not feasible and the capacity reduces to zero. An interesting observation is that the ETPIR capacity expression takes different forms in two regimes. When $E < T$, the capacity equals the inverse of a sum of a geometric series with $K$ terms and decreases with $K$; this form is typical for capacity expressions of PIR. When $E \geq T$, the capacity does not depend on $K$, a typical form for capacity expressions of SPIR (symmetric PIR, which further requires data-privacy, {\it i.e.,} the user learns no information about other undesired messages); the capacity does not depend on $T$ either. In addition, the ETPIR capacity result includes multiple previous PIR and SPIR capacity results as special cases.

SPIT Detection in Telephony over IP Using K-Means Algorithm

In few recently years, the number of unwanted messages and calls through IP telephony are increased rapidly. These undesirable messages and calls are booth annoy the receiver. However, the unsolicited calls are more annoying the callee, because he has to response to the call in real-time. SPIT tries to bring a loss in financial and to make ToIP non preferable for costumer. Thus, detection of SPIT is an indispensable challenge to solve if we want to enhance the quality of services in ToIP network. In this work, we give more details about previous proposed approaches to detect SPIT. Then, we present our proposed approach which is based on calculation the value of six features. In our work, we use K-means algorithm to cluster the caller as spammer or non-spammer. The experimental results show that our proposed approach can achieve True Positive Rate between 83.3% and 99.23%, depending on the number of SPIT in the network.

Private Information Retrieval for Secure Distributed Storage Systems

In this paper, we investigate a private information retrieval (PIR) problem for secure distributed storage systems in the presence of an eavesdropper. We design the secure distributed database and the corresponding PIR scheme, which protect not only user privacy (concealing the index of the desired message) from the databases, but also data security (concealing the messages themselves) from an eavesdropper. In our proposed scheme, we use a secret sharing scheme in storing the messages for data security at each of the databases. We consider two different scenarios on whether the databases are aware of the index sets of the secret shares stored in other databases. The key idea in designing an efficient PIR procedure is to exploit the secret shares of undesired messages as a side information by means of storing the secret shares at multiple databases. In particular, it is shown that the rates of the proposed PIR schemes are within a constant multiplicative factor from the derived upper-bound on the capacity of PIR problem.

Keeping mum in clinical supervision: private thoughts and public judgements.

The seemingly obvious claim that people prefer to keep mum about undesirable messages - termed 'the MUM effect' - was initially reported in the psychology literature in the 1970s. More recently, it has been discussed in contexts including performance appraisals and the reporting of unsuccessful projects in workplace settings, but only sparsely in educational ones. We wished to review the published literature on the MUM effect in order to understand the implications for clinical assessment. We performed a narrative literature review on the MUM effect and clustered findings together into three themes: those that describe what MUM behaviours look like, those that explore potential reasons for the MUM effect and those that consider factors that can influence MUM behaviours. This paper summarises the extensive literature on the MUM effect, including its manifestations and modifiers and discusses how the effect may be used to consider issues faced by many clinical supervisors faced with delivering 'negative' assessment messages to trainees. We suggest, that as a pervasive phenomenon, the MUM effect can both help to explain the difficulties that some assessors face when delivering undesirable messages (including feedback or ratings) and offer new insights in how to deal with such issues.

An Efficient &amp; Secure Content Contribution and Retrieval content in Online Social Networks using Level-level Security Optimization &amp; Content Visualization Algorithm

&lt;span lang="EN-US"&gt;Online Social Networks (OSNs) is currently popular interactive media to establish the communication, share and disseminate a considerable amount of human life data. Daily and continuous communications imply the exchange of several types of content, including free text, image, audio, and video data. Security is one of the friction points that emerge when communications get mediated in Online Social Networks (OSNs). However, there are no content-based preferences supported, and therefore it is not possible to prevent undesired messages. Providing the service is not only a matter of using previously defined web content mining and security techniques. To overcome the issues, Level-level Security Optimization &amp;amp; Content Visualization Algorithm is proposed to avoid the privacy issues during content sharing and data visualization. It adopts level by level privacy based on user requirement in the social network. It evaluates the privacy compatibility in the online social network environment to avoid security complexities. The mechanism divided into three parts namely like online social network platform creation, social network privacy, social network within organizational privacy and network controlling and authentication. Based on the experimental evaluation, a proposed method improves the privacy retrieval accuracy (PRA) 9.13% and reduces content retrieval time (CRT) 7 milliseconds and information loss (IL) 5.33%.&lt;/span&gt;

PRIVACY ANALYSIS OF COMMENT USING TEXT MINING IN OSN FRAMEWORK

The most interactive medium in today’s world is the Online Social Network(OSN) that are used to communicate, share and disseminate information considerable amount of human life information. Stretch out the work to execute data separating way to deal with be utilized to enable clients to naturally screen the messages composed without anyone else dividers, by sifting through undesirable messages and remarks about pictures. The point of the present work is outline structure, called Filtered Wall (FW), ready to channel undesirable messages from OSN client dividers. At that point misuse machine Learning (ML) way to deal with actualize content mining methods to naturally appoint with each short instant message an arrangement of classifications in light of its substance. The most critical exertion is to actualize short content classifier (STC) is accustomed to separating and choosing the tokens from remarks. At that point utilizing sifted guidelines and piece list ways to deal with takeout undesirable messages and furthermore obstruct the companions who are send the undesirable messages ceaselessly and they are naturally separated by server. This idea can be actualized continuously to send versatile implication at the season of client in disconnected mode about negative remarks and also block the friends who has send the negative message continously. So client can undoubtedly monitor the framework from protection infringement.

Using evolutionary computation for discovering spam patterns from e-mail samples

One of the most relevant problems affecting the efficient use of e-mail to communicate worldwide is the spam phenomenon. Spamming involves flooding Internet with undesired messages aimed to promote illegal or low value products and services. Beyond the existence of different well-known machine learning techniques, collaborative schemes and other complementary approaches, some popular anti-spam frameworks such as SpamAssassin or Wirebrush4SPAM enabled the possibility of using regular expressions to effectively improve filter performance. In this work, we provide a review of existing proposals to automatically generate fully functional regular expressions from any input dataset combining spam and ham messages. Due to configuration difficulties and the low performance achieved by analysed schemes, in this work we introduce DiscoverRegex, a novel automatic spam pattern-finding tool. Patterns generated DiscoverRegex outperform those created by existing approaches (able to avoid FP errors) whilst minimising the computational resources required for its proper operation. DiscoverRegex source code is publicly available at https://github.com/sing-group/DiscoverRegex.

Reducing the Bias: How Perspective Taking Affects First- and Third-Person Perceptions of Media Influence

Third- and first-person perceptions (TPPs/FPPs) are considered to be biased judgments of media influence on self and others. Research suggests that perspective taking, i.e., thinking from another person’s position, decreases perceptual gaps between self and others via assimilation. In a two-factorial experiment (n = 431), we test whether this effect of perspective taking (Factor 1) holds true for the presumed influence of desirable and undesirable messages (Factor 2). Results indicate that perspective taking significantly reduces TPPs in the case of an undesirable message but not FPPs that are provoked by the desirable message. The observable effect traces back to a change in presumed message influence on the self. Presumed influence on others was independent of both factors, desirability of message influence and perspective taking. These findings are discussed in the light of cognitive and motivational explanations for FPPs/TPPs.

A Review on Mobile SMS Spam Filtering Techniques

Under short messaging service (SMS) spam is understood the unsolicited or undesired messages received on mobile phones. These SMS spams constitute a veritable nuisance to the mobile subscribers. This marketing practice also worries service providers in view of the fact that it upsets their clients or even causes them lose subscribers. By way of mitigating this practice, researchers have proposed several solutions for the detection and filtering of SMS spams. In this paper, we present a review of the currently available methods, challenges, and future research directions on spam detection techniques, filtering, and mitigation of mobile SMS spams. The existing research literature is critically reviewed and analyzed. The most popular techniques for SMS spam detection, filtering, and mitigation are compared, including the used data sets, their findings, and limitations, and the future research directions are discussed. This review is designed to assist expert researchers to identify open areas that need further improvement.

How Perceived Persuasive Intent and Reactance Contribute to Third-Person Perceptions: Evidence from Two Experiments

In two experiments, this study presents a process model that explains third-person perceptions (TPP) as a function of perceived persuasive intent and reactance. Using two nonstudent samples, findings were internally replicated for two topics. The study shows that media messages evoking perceptions of persuasive intent also activate reactance, which in turn predicts TPP topic-independently. Remarkably, half of the total stimulus effect on TPP could be explained through reactance, which offers new implications for existing theoretical explanations of strong TPP after undesirable messages but weak effects after, for example, prosocial messages.

A Practical Approach for Emails Multiclass Classification according to Senders using Naïve Bayers Technique

are parts of everyday life. These messages have become increasingly important and widespread method of communication because of its time speed, where the amount of email messages received per day can range from tens for a regular user to thousands for companies. Everyone is overwhelmed with emails, including relational (structured) and non-relational (semi-structured or non-structured), quite a bit of which is repetitive, stale and of drastically differing quality. This large quantity is confounded. Not just spam messages are thought to be 'garbage', additionally undesirable messages (e.g. advertisements, lottery) individuals waste a lot of time unknowingly by surfing them. So there is much need to categorization of Emails. Classification can help to meet lawful and administrative necessities for recovering particular data inside of a set time span, and this is frequently the inspiration driving implementing data classification. This paper aims at examining on ways doing supervised and unsupervised grouping of messages as per email content. Keywordsunsupervised, classification.

Designing Weed out undesirable Messages from Social Networking System (SNS)

Designing Weed out undesirable Messages from Social Networking System (SNS)

Security Issues of Email Marketing Service

The paper deals in its first part with the topic of email marketing as well as with the topic of sending undesired messages when performing email marketing. The paper discusses industry standards and laws according which the email messages are to be compliant. Special attention is paid to the unwanted emails detection. Concept of solving the issues of spreading undesired emails when providing the service of email marketing is also described.

A Review on Filter Undesired Text from Social Networks

social Network (OSN) is a social networking service which is a platform to build relations among people who share comfort, actions, backgrounds or real-life connections. Hundreds of thousands of people are using these social networking services for personal use, marketing. Entertainment, Business purpose. User security is the main issue in present days from person to person interaction. Message filtering is main task of proposed system. An online social network provides the little support to the user to avoid unwanted messages displayed on their own private space. In this paper, we propose system which gives ability to user to control the unwanted messages posted on their wall. To filter undesired messages propose three tier architecture containing message classifier based on content and using machine learning techniques. User is able to customize the filtering rule as per his/her preferences.i.e grant access to allow user to insert messages on his/her wall.