High Levels Of Recall Research Articles

Comparison of Multi-Armed Bandit Algorithms in Advertising Recommendation Systems

In today's rapidly evolving online environment, advertising recommendation systems utilize multi-armed bandit algorithms like dynamic collaborative filtering Thompson sampling (DCTS), upper confidence bound based on recommender system (UCB-RS), and dynamic -greedy algorithm (DEG) to optimize ad displays and enhance click-through rates (CTR). These algorithms must adapt to limited information and update strategies based on immediate feedback.This study employs an experimental comparison to assess the performance of the DCTS, UCB-RS, and DEG algorithms using the click-through rate prediction database from Kaggle. Five experimental sets under varied parameter settings were analyzed, employing the Receiver Operating Characteristic (ROC) curve, accuracy, and area under the curve (AUC) metrics.Results show that the DEG algorithm consistently outperforms the others, achieving higher AUC values and demonstrating robust sample identification capabilities. DEG also exhibits superior precision at high recall levels, showcasing its potential in dynamic advertising environments. Its dynamic adjustment strategy effectively balances exploration and exploitation, optimizing ad displays.The findings suggest that DEG's adaptability and stability make it particularly suitable for dynamic ad recommendation scenarios. Future research should focus on optimizing DEG's parameter settings and possibly integrating UCB-RS's exploration mechanisms to enhance performance and develop more effective strategies for advertising recommendation systems.

The lasting impact of the Go for 2&5 campaign.

Little is known about the executional techniques that are most likely to make healthy eating messages memorable over the longer term. The aim of this exploratory study was to identify factors contributing to the ongoing salience of the Go for 2&5 social marketing campaign among older audience members. Interviews were conducted with 75 Western Australians aged 60+ years. A peer-interviewing method was used whereby eight older people recruited and interviewed members of their extended peer groups. The interview guide included questions about interviewees' recollections of healthy eating messages across their lifespans. Around one in five interviewees discussed the Go for 2&5 campaign that was on air in Western Australia between 2002 and 2011. These discussions focused on either specific advertisement features that were considered to be especially memorable (in particular, humour and the use of anthropomorphised depictions of produce) and/or their recollection of the number of daily servings of fruits and vegetables recommended in the campaign. Few were able to recall other healthy eating campaigns, and in some cases it was assumed that advertisements for specific food stores or products constituted health advice. Different campaign attributes may influence shorter-term attitudinal and behavioural responses versus longer-term recall. The characteristics of the Go for 2&5 campaign appear to have enabled it to achieve relatively high levels of recall among older people. SO WHAT?: The study results suggest that some advertising approaches that activate the peripheral route to persuasion may achieve recall over a longer period of time.

Stopping Methods for Technology-assisted Reviews Based on Point Processes

Technology-assisted Review (TAR), which aims to reduce the effort required to screen collections of documents for relevance, is used to develop systematic reviews of medical evidence and identify documents that must be disclosed in response to legal proceedings. Stopping methods are algorithms that determine when to stop screening documents during the TAR process, helping to ensure that workload is minimised while still achieving a high level of recall. This article proposes a novel stopping method based on point processes, which are statistical models that can be used to represent the occurrence of random events. The approach uses rate functions to model the occurrence of relevant documents in the ranking and compares four candidates, including one that has not previously been used for this purpose (hyperbolic). Evaluation is carried out using standard datasets (CLEF e-Health, TREC Total Recall, TREC Legal), and this work is the first to explore stopping method robustness by reporting performance on a range of rankings of varying effectiveness. Results show that the proposed method achieves the desired level of recall without requiring an excessive number of documents to be examined in the majority of cases and also compares well against multiple alternative approaches.

The role of intentionality in memory and learning: Comments on Popov and Dames (2022).

This commentary is a reply to the article "Intent matters: Resolving the intentional versus incidental learning paradox in episodic long-term memory" by Popov and Dames (2022). In their article, the authors question the view that once adequate deep, elaborate, and organizational processes have been induced incidentally, the intention to learn adds nothing further to the level of subsequent retention. Opposing this view, Popov and Dames conclude that intention to learn is always necessary for good memory performance and support this claim with the results of 11 experiments in which they find strong effects of intentionality using mixed-list designs in which all items are processed semantically but only half need be remembered later. The present commentary suggests that intentionality leads to selectively greater amounts of item processing and organizational processing of the to-be-remembered items in mixed lists, and that these further operations result in higher levels of recall. In light of this interpretation, the commentary argues for the validity of the original conclusion that retention is determined by the qualitative type of processing carried out on the items to be remembered, however that processing is induced. The commentary concludes by discussing various factors that modulate the effect of intentionality on memory and learning, and by suggesting a scheme that may aid the understanding of these effects, and serve as a framework for future studies. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Evaluating knowledge-based security questions for fallback authentication.

Failed user authentication is a common event. Forgotten passwords and fingerprint non-recognition are the most common causes. Therefore, there is a need for efficient backup authentication methods, known as fallback authentication. However, fallback authentication methods suffer from different security and usability issues. This study aims to improve the security and usability of knowledge-based fallback authentication in the form of static security questions. The approach proposed in this study was designed considering different factors, such as question features, authentication mechanisms, and the use of tools to aid in composing memorable and secure answers. This study used a two-part experiment with 23 participants to evaluate the proposed approach based on security model testing. The results show that the proposed approach offered improved resistance to blind guess, focused guess, and observation guess attacks. While usability was clearly improved with questions that were based on recognition mechanisms, our results indicate that fallback authentication systems need a flexible level of security and avoidance of complexity in composing answers. In addition, our results indicate the effectiveness of using user behavioral details in the choice of topics for questions, where behavioral questions must have both high recall levels and resistance against guessing attacks. This work theoretically extends the knowledge of fallback authentication research by evaluating new security questions for fallback authentication considering replace of classical topics of security questions by introducing new topics of security questions based on user behavior and personal preferences. Also, this study applies methods of managing answers to security questions by encouraging users to compose answers based on free strict rules that inspire them to create strong and memorable answers based on their own rules. In addition, the findings of this study could support the deployment of knowledge-based authentication in fallback systems as a practical contribution to the user authentication field.

Not just intentions: predicting actual purchase behavior in sport sponsorship context

PurposeThe scope of this study was to underline the specific aspects of positive consumer attitude and behavior related to sports sponsorship. In more detail, the purpose of this study was to establish a comprehensive sponsorship framework integrating Beliefs about sponsorship, Sponsor's Image, Fans' Attachment with the team, Team's Performance, Fans' Sport Involvement, Sponsors' Sincerity, Awareness of Sponsors, Attitude toward Sponsors, Purchase intentions and Actual Purchases.Design/methodology/approachQuantitative method was utilized and a sum of 2,752 questionnaires were effectively assembled and analyzed by means of SPSS and AMOS. The results of a structural equation model provide an exceptional conceptual framework that underlines the significance of comprehending the role of important factors in sponsorship efficiency.FindingsThis paper provides the context for a discussion. It shows that various antecedents have a significant effect on real – instead of just intentions – purchase behavior regarding sponsors' products and services.Research limitations/implicationsVarious implications for future researches as well as strategies to boost the advantages for both sport clubs and sponsoring firms can be drawn from the suggested model.Originality/valueUp to present, only a handful of empirical studies have looked at the effect of sponsorship on the consumer. The majority of sponsorship studies measure the impact of the sponsorship effort on the recall and recognition of sponsors, with empirical results being inconclusive, with some studies showing high levels of recall and recognition while others show only marginal impact on recall and recognition. The present research provides a comprehensive framework, which can guide future studies by isolating the effect of sponsorship on actual purchase behavior, instead of just estimating individuals' purchase intentions.

Multimatcher Model to Enhance Ontology Matching Using Background Knowledge

Ontology matching is a rapidly emerging topic crucial for semantic web effort, data integration, and interoperability. Semantic heterogeneity is one of the most challenging aspects of ontology matching. Consequently, background knowledge (BK) resources are utilized to bridge the semantic gap between the ontologies. Generic BK approaches use a single matcher to discover correspondences between entities from different ontologies. However, the Ontology Alignment Evaluation Initiative (OAEI) results show that not all matchers identify the same correct mappings. Moreover, none of the matchers can obtain good results across all matching tasks. This study proposes a novel BK multimatcher approach for improving ontology matching by effectively generating and combining mappings from biomedical ontologies. Aggregation strategies to create more effective mappings are discussed. Then, a matcher path confidence measure that helps select the most promising paths using the final mapping selection algorithm is proposed. The proposed model performance is tested using the Anatomy and Large Biomed tracks offered by the OAEI 2020. Results show that higher recall levels have been obtained. Moreover, the F-measure values achieved with our model are comparable with those obtained by the state of the art matchers.

Creativity in Advertising: Impact on Communication-effect and Consumer purchase behaviour

Introduction and Background: Introduction of the triple mantra, Liberalization, Privatization and Globalization (LPG) in Indian Economy towards the beginning of 1990’s attracted many Multi-National Companies to Indian Consumer market. This new entry of MNC’s coupled with the struggle of traditional Indian corporate to survive in the market resulted in increased volume of advertisements and now the media are flooded with the advertisements of competing brands. Objectives: 1. To compare the level of Communication-Effect in terms of Advertising Effectiveness, among highly Creative advertising and Conventional Advertising. 2. To see the differences in Communication-Effect in terms of fulfilling Communication Objectives, in two groups of advertising. 3. To measure and compare the Attitude towards Advertisements generated by the two types of advertisements. Methods: The study reported here embodies a quantitative perspective and the type of research was designated as diagnostic research. The research method used to collect data was Sample Survey method. The sample size was determined as 200 and Multi-Stage Stratified Random Sampling method was resorted to as the sampling procedure. The research participants consisted of women, aged 18 to 49. Results: Among The Five Most Liked Ads by consumers, without any form of aid, Liril Toilet Soap’s commercial showing the fantasy of a dancing girl under waterfall gets the highest liking position. Other most liked ads are the commercials of Santhoor soap, Kanan Devan Tea, Close-up paste and Rasna soft drink. Highly Creative advertising has more Communication Effect in terms of Advertising Effectiveness when compared to Conventional advertising. Firstly, Highly Creative group gets higher level of Recall measured by both Brand and Ad Recall. Highly Creative advertising has significantly more Communication Effect in terms of other measures of Advertising Effectiveness. It has more Interest or Cognitive Affect (ICA), more Information value, lower Irritation level and higher positive Attitude towards Ads.

Animacy and animate imagery improve retention in the method of loci among novice users.

Recently, researchers have identified word animacy as a strong predictor of recall. In contrast, the method of loci is an ancient mnemonic technique which takes advantage of highly structured encoding and recall processes alongside a strong imagery component to create easily remembered "memory palaces." The present experiments examine the combined effectiveness of these techniques: Experiment 1 (N = 154) demonstrates that the method of loci and word animacy have additive effects, while Experiment 2 (N = 200) demonstrates that the additive effect of animacy is likely related to both the animate nature of words themselves and animate imagery associated with them. These results have implications for hypotheses about the proximate mechanism of animacy effects (ruling out temporal order and imagery as explanations), implications regarding the nature of animacy (as being both static and dynamic), and practical implications for memory athletes and educational settings alike: The method of loci and use of animate imagery can be taught easily, and they produce high levels of recall.

Consent in forefoot surgery; What does it mean to the patient?

AimsThis study aimed to assess patient risk recall and find risk thresholds for patients undergoing elective forefoot procedures. MethodsPatients were interviewed in the pre-assessment clinic (PAC) or on day of surgery (DOS); some in both settings. A standardised questionnaire was used for all interviews, regardless of setting. Patients were tested on which risks they recalled from their consent process, asked for thresholds for five pre-chosen risks and asked about a sham risk. ResultsAcross all interviews, risk recall on DOS (2.34 risks/patient interview) was significantly lower (p=.05) than in PAC (2.95 risks/patient interview) – this was repeated when comparing results from patients interviewed in both settings only with PAC mean recall of 2.93 risks/patient interview and DOS mean recall of 2.57 risks/patient interview. The mean reported risk thresholds greatly exceeded NHS Lothian’s observed complication rates for forefoot procedures. The five risks tested for thresholds produced the same order in each interview setting, suggesting a patient-perceived severity ranking. Patients answering the sham risk question incorrectly tended to recall fewer risks across all interviews. ConclusionsThis study shows that patient risk recall is poor, as previous literature outlines, reinforcing that consent process improvements could be made. It also illustrates the value of PAC visits in patient education, as shown by higher levels of recall when compared to DOS.

English

Preschool period covers the years when the child is in a rapid change and development in every aspect. The learning experiences in this period affect children’s development. The developments in knowledge technologies affect the child's environment and children's books. This rapid change brings with it some discussions. “Do either traditional methods or digital applications affect children's level of recalling more positively?” This question was the starting point of the research. Thus, the aim of this study is to compare the effect of stories told through e-books and picture books on children's level of recall. The research is based on random design with post-test control group from real experimental designs of quantitative research model. The groups were determined by simple random sampling method and each group consisted of 10 children. The research data were collected by asking the children various questions about the stories and the characters in the stories by the researchers after storytelling through e-book and pictures book. Mann Whitney U test was used to analyze the data. As a result of the study, it was discovered that the children in the group that the story was told by using pictures book have higher level of recall.   Key words: Preschool education, story, story book, e-book, recall.

Heat-health warnings in regional Australia: examining public perceptions and responses

ABSTRACTHeatwaves are an increasing environmental hazard and an important public health issue in Australia. Heat-health warnings are being adopted widely to promote protective behaviours, but there has been limited evaluation of public responses. This study used a household telephone survey to examine public attitudes and responses to heat-health warnings in regional areas in two Australian states, South Australia and Victoria. The results indicate a high level of recall of heat-health warnings and awareness about managing extreme heat. Respondents viewed heat-health warnings positively, but the effects on behaviour change were variable. Our findings suggest that the warnings may be reinforcing existing protective behaviours more than promoting change. Perceptions of heat risks were higher among women than men, but lower in older age groups. Evidence of this nature is important to identify ways to improve heat-health warnings and more effectively address the public health risks.

Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches

BackgroundLink prediction in biomedical graphs has several important applications including predicting Drug-Target Interactions (DTI), Protein-Protein Interaction (PPI) prediction and Literature-Based Discovery (LBD). It can be done using a classifier to output the probability of link formation between nodes. Recently several works have used neural networks to create node representations which allow rich inputs to neural classifiers. Preliminary works were done on this and report promising results. However they did not use realistic settings like time-slicing, evaluate performances with comprehensive metrics or explain when or why neural network methods outperform. We investigated how inputs from four node representation algorithms affect performance of a neural link predictor on random- and time-sliced biomedical graphs of real-world sizes (∼ 6 million edges) containing information relevant to DTI, PPI and LBD. We compared the performance of the neural link predictor to those of established baselines and report performance across five metrics.ResultsIn random- and time-sliced experiments when the neural network methods were able to learn good node representations and there was a negligible amount of disconnected nodes, those approaches outperformed the baselines. In the smallest graph (∼ 15,000 edges) and in larger graphs with approximately 14% disconnected nodes, baselines such as Common Neighbours proved a justifiable choice for link prediction. At low recall levels (∼ 0.3) the approaches were mostly equal, but at higher recall levels across all nodes and average performance at individual nodes, neural network approaches were superior. Analysis showed that neural network methods performed well on links between nodes with no previous common neighbours; potentially the most interesting links. Additionally, while neural network methods benefit from large amounts of data, they require considerable amounts of computational resources to utilise them.ConclusionsOur results indicate that when there is enough data for the neural network methods to use and there are a negligible amount of disconnected nodes, those approaches outperform the baselines. At low recall levels the approaches are mostly equal but at higher recall levels and average performance at individual nodes, neural network approaches are superior. Performance at nodes without common neighbours which indicate more unexpected and perhaps more useful links account for this.

Fast schemes for online record linkage

The process of integrating large volumes of data coming from disparate data sources, in order to detect records that refer to the same entities, has always been an important problem in both academia and industry. This problem becomes significantly more challenging when the integration involves a huge amount of records and needs to be conducted in a real-time fashion to address the requirements of critical applications. In this paper, we propose two novel schemes for online record linkage, which achieve very fast response times and high levels of recall and precision. Our proposed schemes embed the records into a Bloom filter space and employ the Hamming Locality-Sensitive Hashing technique for blocking. Each Bloom filter is hashed to a number of hash tables in order to amplify the probability of formulating similar Bloom filter pairs. The main theoretical premise behind our first scheme relies on the number of times a Bloom filter pair is formulated in the hash tables of the blocking mechanism. We prove that this number strongly depends on the distance of that Bloom filter pair. This correlation allows us to estimate in real-time the Hamming distances of Bloom filter pairs without performing the comparisons. The second scheme is progressive and achieves high recall, upfront during the linkage process, by continuously adjusting the sequence in which the hash tables are scanned, and also guarantees, with high probability, the identification of each similar Bloom filter pair. Our experimental evaluation, using four real-world data sets, shows that the proposed schemes outperform four state-of-the-art methods by achieving higher recall and precision, while being very efficient.

Integrated Deep Model for Face Detection and Landmark Localization From “In The Wild” Images

The tasks of face detection and landmark localisation are a key foundation for many facial analysis applications, while great advancements have been achieved in recent years there are still challenges to increase the precision of face detection. Within this paper, we present our novel method the Integrated Deep Model (IDM), fusing two state-of-the-art deep learning architectures, namely, Faster R-CNN and a stacked hourglass for improved face detection precision and accurate landmark localisation. Integration is achieved through the application of a novel optimisation function and is shown in experimental evaluation to increase accuracy of face detection specifically precision by reducing false positive detection’s by an average of 62%. Our proposed IDM method is evaluated on the Annotated Faces In-The-Wild, Annotated Facial Landmarks In The Wild and the Face Detection Dataset and Benchmark face detection test sets and shows a high level of recall and precision when compared with previously proposed methods. Landmark localisation is evaluated on the Annotated Faces In-The-Wild and 300-W test sets, this specifically focuses on localisation accuracy from detected face bounding boxes when compared with baseline evaluations using ground truth bounding boxes. Our findings highlight only a small 0.005% maximum increase in error which is more profound for the subset of facial landmarks which border the face.

GK-Tail+ An Efficient Approach to Learn Software Models

Inferring models of program behavior from execution samples can provide useful information about a system, also in the increasingly common case of systems that evolve and adapt in their lifetime, and without requiring large developers’ effort. Techniques for learning models of program behavior from execution traces shall address conflicting challenges of recall, specificity and performance: They shall generate models that comprehensively represent the system behavior (recall) while limiting the amount of illegal behaviors that may be erroneously accepted by the model (specificity), and should infer the models within a reasonable time budget to process industrial scale systems (performance). In our early work, we designed GK-tail, an approach that can infer guarded finite state machines that model the behavior of object-oriented programs in terms of sequences of method calls and constraints on the parameter values. GK-tail addresses well two of the three main challenges, since it infers guarded finite state machines with a high level of recall and specificity, but presents severe limitations in terms of performance that reduce its scalability. In this paper, we present GK-tail+, a new approach to infer guarded finite state machines from execution traces of object-oriented programs. GK-tail+ proposes a new set of inference criteria that represent the core element of the inference process: It largely reduces the inference time of GK-tail while producing guarded finite state machines with a comparable level of recall and specificity. Thus, GK-tail+ advances the preliminary results of GK-tail by addressing all the three main challenges of learning models of program behavior from execution traces.

Utilizing Verbal Intent in Semantic Contextual Advertising

Existing semantic approaches to contextual advertising effectively match relevant ads to webpages in terms of topical intent. The authors observe, however, that there's still much room for improvement. In this article, they seek to utilize the verbal intent, which complements topical intent, in semantic contextual advertising. Verbal intent describes what a user wants to do, that is, the action perspective, with topical intent. The authors propose a methodology that effectively identifies verbal intent in webpages and ads, and then incorporates the verbal intent into an ad-ranking framework. The results of performance evaluation on real-world datasets clearly show the efficacy of (verb, topic) associations. Compared with state-of-the-art techniques, the proposed methodology exhibits a significant improvement of precision at a high level of recall in ad ranking, as well as a precision improvement of 35 percent on average in verb identification.

Getting Real About Suicide Prevention in the Classroom and Beyond

Marketing faculties, as well as business schools in general, are placing increasing importance on finding ways to better tie theoretical concepts to real-world situations. In the article that follows, we describe a project wherein students were given an opportunity to apply core consumer behavior concepts to a simulated advertising project with the aim to prevent suicide. A post hoc qualitative survey was conducted, and a series of propositions were generated. We propose that students have high levels of recall when studying in a real-world simulation. Additionally, our findings suggested that when students use core concepts in real-world simulation projects, they are able to later apply those concepts in actual real-world situations. Finally, we propose that simulations are an effective way of teaching sensitive subject matter.

The recollective qualities of adolescents’ and adults’ narratives about a long-ago tornado

ABSTRACTThe recollective qualities of autobiographical memory are thought to develop over the course of the first two decades of life. We used a 9-year follow-up test of recall of a devastating tornado and of non-tornado-related events from before and after the storm, to compare the recollective qualities of adolescents’ (n = 20, ages 11 years, 11 months to 20 years, 8 months) and adults’ (n = 14) autobiographical memories. At the time of the tornado, half of the adolescents had been younger than age 6. Nine years after the event, all participants provided evidence that they recall the event of the tornado. Adults also had high levels of recall of the non-tornado-related events. Adolescents recalled proportionally fewer non-tornado-related events; adolescents younger than 6 at the time of the events recalled the fewest non-tornado-related events. Relative to adolescents, adults produced longer narratives. With narrative length controlled, there were few differences in the recollective qualities of adolescents’ and adults’ narrative reports, especially in the case of the tornado; the recollective qualities were stronger among adolescents older at the time of the events. Overall, participants in both age groups provided evidence of the qualities of recollection that are characteristic of autobiographical memory.

Don't be one more logo on the back of the T-shirt: Optimizing sponsorship recall

ABSTRACTThis article examines participant recall of a total of 87 sponsorships of a local running event over a 5-year period. Participant unaided recall was measured with an online survey administered shortly after the events. The study then used management interviews, event observation, and sponsorship data to provide an in-depth analysis of recall. The results indicate that sponsor engagement with event participants is the key factor in optimizing recall. Sponsors maximizing leveraging opportunities received much higher level of recall than those that did not. Lower level sponsors received almost no unaided recall except in specialized cases.