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Murre, Jaap M. J., and Joeri Dros. "Replication and Analysis of Ebbinghaus’ Forgetting Curve." PLOS ONE 10, no. 7 (2015): e0120644. http://dx.doi.org/10.1371/journal.pone.0120644.
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Peng, Li, Xiao-yang Yu, Liu Yang, and Zhang Ting-ting. "Crowdsourcing Fraud Detection Algorithm Based on Ebbinghaus Forgetting Curve." International Journal of Security and Its Applications 8, no. 1 (2014): 283–90. http://dx.doi.org/10.14257/ijsia.2014.8.1.26.
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Rhomadonah, Siti Aliyah, Muhammad Rizki Abdurrahman, Muhamad Dodi Bokasa, Ihsan Hidayat, Mardhiyah Khoirunnisa, and Alla Asmara. "Analisis Impostor Syndrome dalam Aspek Religiositas terhadap Waqf Behavior Mahasiswa IPB dengan Pendekatan Ebbinghaus Forgetting Curve." AL-MUZARA'AH 11, no. 2 (2023): 187–99. http://dx.doi.org/10.29244/jam.11.2.187-199.
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In the context of productive waqf development in Indonesia, especially at Bogor Agricultural University (IPB), this research is rooted in the high potential of waqf followed by the tendency of students to contribute. With the Indonesian Waqf Board (BWI) as the main mover and IPB as an educational institution that acts as a nazir, increasing waqf literacy has had an impact on people's anxiety about post-life in the world, motivating them to actively waqf. Furthermore, this study links the phenomenon of Impostor Syndrome with the Ebbinghaus Forgetting Curve, highlighting the psychological and cognitive role in the retention of waqf-related information. This research uses a mixed method, namely quantitative derived from the results of CIPS and STAI tests of respondents to identify impostor syndrome and power function decline to find the new ebbinghaus forgetting curve. The qualitative method carried out is derived from interviews with key persons. Through a focus on IPB students, this study aims to understand how Impostor Syndrome feelings can affect aspects of religiosity and student waqf contributions, with the hope that the results can form emotionally stable, sincere, and balanced waqf behavior in achieving life balance.
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Li, Taoying, Linlin Jin, Zebin Wu, and Yan Chen. "Combined Recommendation Algorithm Based on Improved Similarity and Forgetting Curve." Information 10, no. 4 (2019): 130. http://dx.doi.org/10.3390/info10040130.
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The recommendation algorithm in e-commerce systems is faced with the problem of high sparsity of users’ score data and interest’s shift, which greatly affects the performance of recommendation. Hence, a combined recommendation algorithm based on improved similarity and forgetting curve is proposed. Firstly, the Pearson similarity is improved by a wide range of weighted factors to enhance the quality of Pearson similarity for high sparse data. Secondly, the Ebbinghaus forgetting curve is introduced to track a user’s interest shift. User score is weighted according to the residual memory of forgetting function. Users’ interest changing with time is tracked by scoring, which increases both accuracy of recommendation algorithm and users’ satisfaction. The two algorithms are then combined together. Finally, the MovieLens dataset is employed to evaluate different algorithms and results show that the proposed algorithm decreases mean absolute error (MAE) by 12.2%, average coverage 1.41%, and increases average precision by 10.52%, respectively.
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Huang, Yanbiao, Bo Fu, Yujing Lai, and Yujie Yao. "Design and Implementation of Memory Assistant Based on Ebbinghaus Forgetting Curve." IOP Conference Series: Earth and Environmental Science 687, no. 1 (2021): 012187. http://dx.doi.org/10.1088/1755-1315/687/1/012187.
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Yang, Songlin, and Min Zhang. "Application of Brain Neural Network in Personalized English Education System." International Journal of Emerging Technologies in Learning (iJET) 13, no. 10 (2018): 15. http://dx.doi.org/10.3991/ijet.v13i10.9488.
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The Personalized Education System (PES) provides appropriate counseling pro-gram as per the different demands and the natures of learners. Its education quali-ty depends on the individuality to a great extent. The Brain Neural Network (BNN) can automatically analyze the learners’ profiles from their feedback data. In light of the above, this paper analyzes the forgetting curve of the learners in the system by building the brain neural network. Take the word memory in English learning as a study case. This curve will help customize the learning content for those learners precisely to hit their strides with a new high-rise personalized edu-cation. Experiment bears out that the forgetting curve generated by the BNN more adapts to the learner's memory law than the traditional universal Ebbinghaus memory curve. The new memory curve makes it possible to improve the effect of PES more effectively and the teaching principle more scientifically.
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Xiao-Ming, Shi, and Zhang Jie-Fang. "Agreement Dynamics of Memory-Based Naming Game with Forgetting Curve of Ebbinghaus." Chinese Physics Letters 26, no. 4 (2009): 048901. http://dx.doi.org/10.1088/0256-307x/26/4/048901.
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Andressa, da Silva Costa Belo *1 Luciano Vieira Lima. "FAMILY AND SOCIAL REINTEGRATION GAMIFICATION FOR THE ELDERLY." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 864–68. https://doi.org/10.5281/zenodo.834567.
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This article presents a set of design in Quiz format for Smartphones set up for the gamification of elderly Effective Learning Reinforcement, in order to reintegrate them to their family and social environment. This study is based on theories such as the ‘Forgetting Curve´ created by Hermann Ebbinghaus and the teaching-learning theories named ‘Structured Knowledge Maps’, ‘Effective Exponential Memory Method in Binary Base’ and ‘Effective Memory Curve. The main goal of this article is to demystify the vision that several people have of elderly, a vision of inability, disinformation, disrespect, contempt, etc. and insert a game that can be used by the entire family in order to help the elderly .
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Hu, S. G., Y. Liu, T. P. Chen, et al. "Emulating the Ebbinghaus forgetting curve of the human brain with a NiO-based memristor." Applied Physics Letters 103, no. 13 (2013): 133701. http://dx.doi.org/10.1063/1.4822124.
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Jiang, Yanhuang, Qiangli Zhao, and Yutong Lu. "Adaptive Ensemble with Human Memorizing Characteristics for Data Stream Mining." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/874032.
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Combining several classifiers on sequential chunks of training instances is a popular strategy for data stream mining with concept drifts. This paper introduces human recalling and forgetting mechanisms into a data stream mining system and proposes a Memorizing Based Data Stream Mining (MDSM) model. In this model, each component classifier is regarded as a piece of knowledge that a human obtains through learning some materials and has a memory retention value reflecting its usefulness in the history. The classifiers with high memory retention values are reserved in a “knowledge repository.” When a new data chunk comes, most useful classifiers will be selected (recalled) from the repository and compose the current target ensemble. Based on MDSM, we put forward a new algorithm, MAE (Memorizing Based Adaptive Ensemble), which uses Ebbinghaus forgetting curve as the forgetting mechanism and adopts ensemble pruning as the recalling mechanism. Compared with four popular data stream mining approaches on the datasets with different concept drifts, the experimental results show that MAE achieves high and stable predicting accuracy, especially for the applications with recurring or complex concept drifts. The results also prove the effectiveness of MDSM model.
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Chmiel, Anthony, and Emery Schubert. "Using Psychological Principles of Memory Storage and Preference to Improve Music Recommendation Systems." Leonardo Music Journal 28 (December 2018): 77–81. http://dx.doi.org/10.1162/lmj_a_01045.
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This paper proposes a novel approach to automated music recommendation systems. Current systems use a number of methods, although these are generally based on similarity of content, contextual information or user ratings. These approaches therefore do not take into account relevant, well-established models from the field of music psychology. Given recent evidence of this field’s excellent capacity to predict music preference, we propose a function based on both the Ebbinghaus forgetting curve of memory retention and Berlyne’s inverted-U model to inform recommendation systems through “collative variables” such as exposure/familiarity. According to the model, an intermediate level of these variables should generate relatively high preference and therefore presents significant untapped data for music recommendation systems.
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Cao, Shuting, and Haiyuan Liu. "Effectiveness Analysis of Edmodo-Based Blended English Learning Mode." International Journal of Emerging Technologies in Learning (iJET) 14, no. 18 (2019): 64. http://dx.doi.org/10.3991/ijet.v14i18.11184.
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This paper attempts to overcome the defects of traditional English learning mode, including poor real-time communication ability, heavy reliance on individual self-discipline, and undesirable learning effect. Drawing on the theories of English learning motivation, the author analyzed the relationship between learning motiva-tion and learning behavior in light of the Edmodo-based blended English learning mode, and put forward the English learning persistence rate formula, thus reveal-ing the correlations between learning cost, objective external learning environ-ment, learning motivation, and learning persistent rate. In addition, the English courses were made more pertinent according to the Ebbinghaus forgetting curve. The course test data were fitted and weighted, outputting intuitive curves on learning effect. The research results show that the Edmodo-based blended Eng-lish learning mode has a significant learning effect, and a guiding significance for English learning and English teaching reform.
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Li, Kun, Xin Jing, and Chengang Jing. "Vector Storage Based Long-term Memory Research on LLM." International Journal of Advanced Network, Monitoring and Controls 9, no. 3 (2024): 69–79. http://dx.doi.org/10.2478/ijanmc-2024-0029.
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Abstract Current large language model (LLM) intelligences face the challenges of high inference cost and low decision quality when dealing with complex tasks, and are especially deficient in maintaining context coherence during long tasks. This research presents an innovative vector storage long-term memory mechanism model (VIMBank) to enhance the long-term context retention ability and task execution efficiency of LLM intelligences by storing and retrieving historical interaction data through a vector database. VIMBank utilizes a dynamic memory updating strategy and the Ebbinghaus forgetting curve theory to efficiently manage the memory of intelligences and reinforce critical information, forgetting unimportant data, and optimizing storage and reasoning costs. The experimental results show that VIMBank significantly improves the decision quality and efficiency of LLM intelligences in multi-tasking scenarios and reduces the computational cost. Compared with different agents, the success rate of task decision is increased by 10% to 20%, and the reasoning cost is reduced by about 23%, which provides an important theoretical basis and practical support for the future development of intelligences with long term memory and adaptive learning ability.
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Xu, Feng, Kang Chen, Maosheng Zhong, et al. "DKVMN&MRI: A new deep knowledge tracing model based on DKVMN incorporating multi-relational information." PLOS ONE 19, no. 10 (2024): e0312022. http://dx.doi.org/10.1371/journal.pone.0312022.
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Knowledge tracing is a technology that models students’ changing knowledge state over learning time based on their historical answer records, thus predicting their learning ability. It is the core module that supports the intelligent education system. To address the problems of sparse input data, lack of interpretability and weak capacity to capture the relationship between exercises in the existing models, this paper build a deep knowledge tracing model DKVMN&MRI based on the Dynamic Key-Value Memory Network (DKVMN) that incorporates multiple relationship information including exercise-knowledge point relations, exercise-exercise relations, and learning-forgetting relations. In the model, firstly, the Q-matrix is utilized to map the link between knowledge points and exercises to the input layer; secondly, improved DKVMN and LSTM are used to model the learning process of learners, then the Ebbinghaus forgetting curve function is introduced to simulate the process of memory forgetting in learners, and finally, the prediction strategies of Item Response Theory (IRT) and attention mechanism are used to combine the similarity relationship between learners’ knowledge state and exercises to calculate the probability that learners would correctly respond during the subsequent time step. Through extensive experiments on three real-world datasets, we demonstrate that DKVMN&MRI has significant improvements in both AUC and ACC metrics contrast with the latest models. Furthermore, the study provides explanations at both the exercise level and learner knowledge state level, demonstrating the interpretability and efficacy of the proposed model.
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Wu, Bing, and Lixue Liu. "Personalized Hybrid Recommendation Algorithm for MOOCs Based on Learners’ Dynamic Preferences and Multidimensional Capabilities." Applied Sciences 13, no. 9 (2023): 5548. http://dx.doi.org/10.3390/app13095548.
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In the MOOCs context, learners experience information overload. Thus, it is necessary to improve personalized recommendation algorithms for learners. The current recommendation algorithm focuses mainly on the learners’ course ratings. However, the choice of courses is not only based on the learners’ interests and preferences. It is also affected by learners’ knowledge domains and learning capabilities, all of which change dynamically over time. Therefore, this study proposes a personalized hybrid recommendation algorithm combining clustering with collaborative filtering. First, data on learners’ course rating preferences, course attribute preferences, and multidimensional capabilities that match course traits are used based on multidimensional item response theory. Second, considering that learners’ preferences and multidimensional capabilities change dynamically over time, the Ebbinghaus forgetting curve is introduced by integrating memory weights to improve the accuracy and interpretation of the proposed recommendation algorithm for MOOCs. Finally, the performance of the proposed recommendation algorithm is investigated using data from Coursera, an internationally renowned MOOCs platform. The experimental results show that the proposed recommendation algorithm is superior to the baseline algorithms. Accordingly, relevant suggestions are proposed for the development of MOOCs.
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Lewis, Amy, Josep Call, and Dorthe Berntsen. "Non-goal-directed recall of specific events in apes after long delays." Proceedings of the Royal Society B: Biological Sciences 284, no. 1858 (2017): 20170518. http://dx.doi.org/10.1098/rspb.2017.0518.
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We examined if apes spontaneously remember one-time, distinctive events across long delays when probed by discriminant cues. Apes witnessed an experimenter hide a cache of food, which they could then retrieve. They retrieved one of two food types; one more distinctive than the other. Two, 10 or 50 weeks later, the apes returned to the same enclosure and found a piece of the previously hidden food on the ground. An experimenter who had not hidden the food was also present. Apes immediately searched the location where the food was previously hidden (no food was here), showing recall of the event. One week later, apes returned to the same enclosure, with the same food on the ground, but now the experimenter that had hidden the food was present. Again, apes immediately searched the hiding location. Apes that had not witnessed the hiding event did not search. There was no significant effect of food type, and retention declined from exposure to the two-week delay, then levelled, consistent with the forgetting curve in humans (Ebbinghaus, H. 1964 Memory: a contribution to experimental psychology (transl. H.A. Ruger & C.E. Bussenvis). New York, NY: Dover. (Original work published 1885.)). This is the first study to show apes can recall a one-time, non-goal-directed event longer than two weeks ago and that apes' recall declines in accordance with a standard retention function.
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Qiujun, Li. "Design of Personalized Push Algorithm of Hot Social News Based on User Interest Model." Security and Communication Networks 2022 (August 1, 2022): 1–10. http://dx.doi.org/10.1155/2022/9670775.
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As a result of rapid advances in information technology, the volume of information on the Internet is expanding at a breakneck rate. The World Wide Web has evolved into a vast and intricate information space. People have shifted from information deficiency to information overload. The characteristics of Internet information are dispersion, disorder, and mass. A challenging research topic is how to quickly, accurately, and efficiently extract vital information from vast information resources. Web search is becoming one of the Internet field’s study centers and focal points. Traditional web search algorithms focus on the link structure of the web and the hierarchical weight of web pages while ignoring the behavior of users, resulting in some search results that are insufficient and inaccurate. In addition, because each web page's hub value and authority value are calculated iteratively, web search is inefficient and susceptible to dispersion and generalization. This study fully integrates the user’s interest behavior and relevant, intelligent optimization algorithms to address the shortcomings of the traditional World Wide Web search algorithm, based on a synthesis and analysis of relevant domestic and international research. A method of user interest model construction and update for news recommendation is proposed to address the problem of user interest model construction and user interest drift in the news recommendation system. Initially, the original user interest model is constructed using a bisection K-means clustering algorithm and a vector space model. Subsequently, the forgetting function is constructed using the Ebbinghaus forgetting curve, and the user interest model is time-weighted to achieve the goal of updating the user interest model. User-based collaborative filtering recommendations and item-based collaborative filtering suggestions serve as the experiment’s baseline. The experimental results suggest that the recommendation performance of the original user interest model is enhanced, with the F value increasing by 4%. The modified model’s F value has increased by 1.3% compared to the previous version.
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Cao, Qi. "Design and implementation of English vocabulary orthographical revision application based on Matlab." Theoretical and Natural Science 31, no. 1 (2024): 336–45. http://dx.doi.org/10.54254/2753-8818/31/20241089.
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Apropos of English learning, vocabulary learning is one of the most essential parts. Thanks to modern technology, recent years has witnessed scads of vocabulary learning apps springing in the app market which include greatly comprehensive functions. However, among those popular apps, some universal handicaps still exist in the design of the review section, for example, the absence of the freedom for the users to classify the cognitive or priority grade of vocabulary and the low effect of some reviewing methods that could not satisfy the need of the language learners to fully master the orthography of words. This paper aims at contriving an app serving as a supplementary part of English vocabulary learning in order to remedy those inadequacies of mainstream applications or other self-learning methods. In this paper, literature study, graphology, and mathematical modelling are involved. Based on Matlab GUI/App Designer, this paper presents a new method to review English words in which users could customize their lexicon, as well as define the priority of each word and practice spelling of vocabulary on an hourly-based system instead of a daily-based one that most of the existing online platforms adopted. Through creating a discrete probability density function for each word, this app could forward a random word in the glossary for users to practice orthography, obeying the probability density distribution that has a mathematical relationship with familiarity and priority of the words as well as the time factor (Ebbinghaus forgetting curve). Thus, users could thoroughly grasp the orthography of target words.
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Минаев, В. А., and И. С. Стручков. "PASSWORD ENTRY BIOMETRICS: NEW AUTHENTICATION MODEL." ИНФОРМАЦИЯ И БЕЗОПАСНОСТЬ 26, no. 2(-) (2023): 277–84. http://dx.doi.org/10.36622/vstu.2023.26.2.011.
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В статье рассматривается возможность усиления метода аутентификации сочетанием пароля и клавиатурного почерка пользователя на входе в компьютерную систему. Для обоснования новой биометрической модели аутентификации рассматриваются результаты экспериментов по замерам забывания информации, полученные в XIX веке немецким психологом Г. Эббингаузом. Выдвигается и проверяется гипотеза, что процесс ввода пользователем пароля с клавиатуры характеризуется кривой экспоненциального типа, когда вначале скорость ввода минимальна, соответственно, время ввода – максимально, а затем по мере новых повторений скорость ввода увеличивается, а время ввода уменьшается, стремясь к некоторому пределу. При этом для каждого пользователя наблюдаются характерные только ему параметры экспоненциальной кривой. Для построения модели клавиатурного почерка с 30 участниками проведены эксперименты по изучению его динамических параметров. Мерой соответствия между теоретической и экспериментальной кривой, а также критерием аутентификации пользователя служит значение коэффициента детерминации R2. Анализ результатов моделирования показал, что участники эксперимента делятся на три типологические группы в пространстве параметров клавиатурного почерка. Группы значимо различаются гендерными и психологическими признаками, отражающими типохарактер пользователя. The article discusses the possibility of strengthening the authentication method by combining a password and a user's keyboard handwriting at the entrance to a computer system. To substantiate the new biometric authentication model, the results of experiments on measurements of forgetting information obtained in the XIX century by the German psychologist G. Ebbinghaus are considered. The hypothesis is put forward and tested that the process of entering a password by the user from the keyboard is characterized by an exponential curve, when at first the input speed is minimal, respectively, the input time is maximal, and then as new repetitions occur, the input speed increases and the input time decreases, tending to a certain limit. At the same time, for each user, the exponential curve parameters characteristic only of him are observed. To build a model of keyboard handwriting, experiments were conducted with 30 participants to study its dynamic parameters. The value of the determination coefficient R2 serves as a measure of the correspondence between the theoretical and experimental curve, as well as the user authentication criterion. Analysis of the simulation results showed that the participants of the experiment are divided into three typological groups in the space of keyboard handwriting parameters. The groups differ significantly in gender and psychological characteristics reflecting the type of character of the user.
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Zhong, Wanjun, Lianghong Guo, Qiqi Gao, He Ye, and Yanlin Wang. "MemoryBank: Enhancing Large Language Models with Long-Term Memory." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 19724–31. http://dx.doi.org/10.1609/aaai.v38i17.29946.
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Large Language Models (LLMs) have drastically reshaped our interactions with artificial intelligence (AI) systems, showcasing impressive performance across an extensive array of tasks. Despite this, a notable hindrance remains—the deficiency of a long-term memory mechanism within these models. This shortfall becomes increasingly evident in situations demanding sustained interaction, such as personal companion systems, psychological counseling, and secretarial assistance. Recognizing the necessity for long-term memory, we propose MemoryBank, a novel memory mechanism tailored for LLMs. MemoryBank enables the models to summon relevant memories, continually evolve through continuous memory updates, comprehend, and adapt to a user's personality over time by synthesizing information from previous interactions. To mimic anthropomorphic behaviors and selectively preserve memory, MemoryBank incorporates a memory updating mechanism, inspired by the Ebbinghaus Forgetting Curve theory. This mechanism permits the AI to forget and reinforce memory based on time elapsed and the relative significance of the memory, thereby offering a more human-like memory mechanism and enriched user experience. MemoryBank is versatile in accommodating both closed-source models like ChatGPT and open-source models such as ChatGLM. To validate MemoryBank's effectiveness, we exemplify its application through the creation of an LLM-based chatbot named SiliconFriend in a long-term AI Companion scenario. Further tuned with psychological dialog data, SiliconFriend displays heightened empathy and discernment in its interactions. Experiment involves both qualitative analysis with real-world user dialogs and quantitative analysis with simulated dialogs. In the latter, ChatGPT acts as multiple users with diverse characteristics and generates long-term dialog contexts covering a wide array of topics. The results of our analysis reveal that SiliconFriend, equipped with MemoryBank, exhibits a strong capability for long-term companionship as it can provide emphatic response, recall relevant memories and understand user personality.
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Zhang, Hongyan, Xiaoguang Luo, and Adnan Abbas. "THE RELATIONSHIP BETWEEN INDUSTRY UNIVERSITY RESEARCH COOPERATION MODE, KNOWLEDGE CREATION PERFORMANCE AND EMOTIONAL REGULATION AND MENTAL HEALTH OF COLLEGE TEACHERS AND STUDENTS." International Journal of Neuropsychopharmacology 25, Supplement_1 (2022): A39. http://dx.doi.org/10.1093/ijnp/pyac032.054.
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Abstract Background In order to solve the “two skins” of science and technology and economic development, as well as the lack of close cooperation between industry, University and research, and the low efficiency of cooperative knowledge creation. We should improve the knowledge creation performance of industry university research cooperation and select the appropriate mode of industry university research cooperation. Generally speaking, “Industry” in industry university research cooperation refers to enterprises relying on technological innovation; “Learning” means universities; “Research” mainly means scientific research institutes focusing on basic research, applied research and various technological development. But in a broad sense, industry university research cooperation usually expands the principal parts to government, intermediary institutions, financial and venture capital institutions in addition to universities, enterprises and scientific research institutes. Subjects and Methods Based on Ebbinghaus forgetting curve and SECI knowledge creation model, this paper makes a quantitative study by analyzing the knowledge creation mechanism of industry university research cooperation and using the method of difference analysis. By designing a questionnaire, this paper investigates the sample data of three representative northeast provinces in China, and draws a scientific conclusion. Results The results show that different industry university research cooperation modes have significant differences in the creation performance of explicit knowledge and tacit knowledge. This determines that different industry university research cooperation modes have significant differences in the total performance of knowledge creation. And the stronger the degree of cooperation embeddedness, the more significant the impact on knowledge creation performance. Conclusions This research result tell us when choosing the cooperation mode, enterprises, universities and scientific research institutes should choose the mode with as strong embeddedness as possible under their own acceptable conditions. And enterprises should send personnel to colleges and universities to learn advanced theoretical knowledge and increase the stock of tacit knowledge; Universities and scientific research institutes send personnel to enterprises to learn advanced practical experience and supplement practical knowledge. In order to avoid knowledge forgetting, try to convert tacit knowledge into explicit knowledge and record it for later use. It can also be passed on as data to later people for learning and use. This paper further consummate the relevant research on knowledge creation performance, makes up for the lack of research on the relationship between industry university research cooperation modes and knowledge creation performance, and provides a certain theoretical foundation and empirical reference for industry university research principal parts to strengthen cooperation and improve knowledge creation performance. Acknowledgements This research was funded by “The key project of Heilongjiang Education Science ‘13th five year plan’ in 2020”, (grant number: GJB1320109) and “Ministry of education industry university research collaborative education project”, (grant number: 201902067011).
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Gaydamakin, Nikolai A. "The model and metrics of awareness in confidential information. Part 2. Actual awareness." Prikladnaya Diskretnaya Matematika, no. 62 (2023): 55–70. http://dx.doi.org/10.17223/20710410/62/5.
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The actual awareness of users in confidential information is considered as the possession of relevant information, characterized by the degree of perception (assimilation) and the possibility of using information (“extraction” from memory). Within the subject-object class of access control models in computer systems, the concept of awareness is formalized as a result of user access to objects containing confidential information. Access to the object (by reading), having a time frame (duration), forms the user’s awareness of the confidential information of the corresponding object, the value of which is proportional to the volume of confidential information of the object, the index of complexity (readability) of the text of the object, the duration of access and and also depends on the user’s individual ability to perceive (reading speed) and master (understanding, processing) information. At the same time, the volume of confidential information of an object is defined as a value proportional to the number of words in the text and the coefficient of informativeness of the object. Over time, according to the Ebbinghaus forgetting curve, the user’s awareness of sensitive information decreases. The degree of decrease in awareness depends on the individual characteristics of the user and the level of confidentiality of the information. Subsequent accesses to the object can restore the degree of awareness depending on the duration of the accesses and the time elapsed since the previous access. The type and parameters of the function of reducing/restoring awareness over time and depending on the access history are considered. The user’s awareness of all confidential information contained (processed) in a computer system consists of awareness of all access objects, taking into account the synergetic effect, which can be either positive (knowledge about the system of objects is greater than the sum of knowledge about objects) or negative. The nature and features of users’ actual awareness in confidential information are illustrated by examples with various parameters of access objects, access history and individual characteristics of users.
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Fukami, Takehiko. "“Ebbinghaus’s Forgetting Curve” and its Model Function." Proceedings of the Annual Convention of the Japanese Psychological Association 78 (September 10, 2014): 1EV—1–091–1EV—1–091. http://dx.doi.org/10.4992/pacjpa.78.0_1ev-1-091.
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Gan, Mingxin, and Ling Gao. "Discovering Memory-Based Preferences for POI Recommendation in Location-Based Social Networks." ISPRS International Journal of Geo-Information 8, no. 6 (2019): 279. http://dx.doi.org/10.3390/ijgi8060279.
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Point-of-interest (POI) recommendations in location-based social networks (LBSNs) allow online users to discover various POIs for social activities occurring in the near future close to their current locations. Research has verified that people’s preferences regarding POIs are significantly affected by various internal and external contextual factors, which are therefore worth extensive study for POI recommendation. However, although psychological effects have also been demonstrated to be significantly correlated with an individual’s preferences, such effects have been largely ignored in previous studies on POI recommendation. For this paper, inspired by the famous memory theory in psychology, we were interested in whether memory-based preferences could be derived from users’ check-in data. Furthermore, we investigated how to incorporate these memory-based preferences into an effective POI recommendation scheme. Consequently, we refer to Ebbinghaus’s theory on memory, which describes the attenuation of an individual’s memory in the form of a forgetting curve over time. We first created a memory-based POI preference attenuation model and then adopted it to evaluate individuals’ check-ins. Next, we employed the memory-based values of check-ins to calculate the POI preference similarity between users in an LBSN. Finally, based on this memory-based preference similarity, we developed a novel POI recommendation method. We experimentally evaluated the proposed method on a real LBSN data set crawled from Foursquare. The results demonstrate that our method, which incorporates the proposed memory-based preference similarity for POI recommendation, significantly outperforms other methods. In addition, we found the best value of the parameter H in the memory-based preference model that optimizes the recommendation performance. This value of H implies that an individual’s memory usually has an effect on their daily travel choices for approximately 300 days.
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Peng, Wei, Qianni Tan, Aiping Chen, and Chanjuan Zheng. "University Ideological and Political Learning Model Based on Statistical Memory Curve Mathematical Equation." Applied Mathematics and Nonlinear Sciences, July 15, 2022. http://dx.doi.org/10.2478/amns.2022.2.0098.
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Abstract In order to consolidate the effect of classroom teaching and broaden students' thinking, stimulate students' interest in learning, avoid forgetting more and more, so that students actively explore knowledge. The author proposes an adaptive memory model based on Ebbinghaus' forgetting curve theory. Firstly, the relationship between the mathematical equation of memory curve and Ebbinghaus forgetting curve theory is introduced. Select the forgetting curve fitting function, and then define the adaptive memory model, the software divides the user's initial cognition of words into three types: cognition, vagueness, and ignorance, the average memory times of these three types of content are counted separately, finally, the average memory times of the three types of content are comprehensively averaged, finally, a comparative experiment is carried out based on the intelligent memory model of the Ebbinghaus forgetting curve. The results show that while not affecting the memory effect, using the intelligent memory model, the number of memories is reduced by 37.12% compared with the New Oriental memory method, using the adaptive memory model reduces the number of memories by 43.35% compared to the New Oriental memory method. The experimental results show, the adaptive memory model further saves 6.31% of memory times compared to the intelligent memory model, not only has good adaptability to each user's memory situation, it also further improves memory efficiency. From this it can be seen that, university ideological and political learning model based on the mathematical equation of statistical memory curve: The adaptive memory model has certain reliability and feasibility.
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Sunny, Ashly, and R. Thamankar. "Emulating Ebbinghaus forgetting behavior in a neuromorphic device based on low dimensional h-BN." AIP Advances 14, no. 8 (2024). http://dx.doi.org/10.1063/5.0203002.
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Artificial synaptic devices that can mimic the biological synaptic functions of learning and forgetting are essential for the realization of neuromorphic computation, which could replace the von Neumann architecture. In this Letter, we have described a high-performing ultraviolet photodetector (wavelength 375 nm) using thin films of single-layer hexagonal boron nitride (h-BN) for potential use in fabricating a neuromorphic device. Furthermore, the classical Ebbinghaus forgetting curve can be optimized using various parameters such as the optical pulse width, number of pulses, and frequency of pulses. Our results show that the characteristic time constant (τ) has much more variability, indicating better performance control than the Ebbinghaus exponent (β). Furthermore, the performance of the optical synapse is very stable for low energy consumption, as low as 2–3 pJ.
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Murre, Jaap M. J., and Antonio G. Chessa. "Why Ebbinghaus’ savings method from 1885 is a very ‘pure’ measure of memory performance." Psychonomic Bulletin & Review, September 7, 2022. http://dx.doi.org/10.3758/s13423-022-02172-3.
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AbstractThis paper analyzes the savings measures introduced by Ebbinghaus in his monograph of 1885. He measured memory retention in terms of the learning time saved in subsequent study trials relative to the time spent on the first learning trial. We prove mathematically that Ebbinghaus’ savings measure is independent of initial encoding strength, learning time, and relearning times. This theoretical model-free result demonstrates that savings is in a sense a very ‘pure’ measure of memory. Considering savings as an old-fashioned and unwieldy measure of memory may be unwarranted given this interesting property, which hitherto seems to have been overlooked. We contrast this with often used forgetting functions based on recall probability, such as the power function, showing that we should expect a lower forgetting rate in the initial portion of the curve for material that has been learned less well.
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Magdeline Singh. "AI, MEMORIZATION, AND FORGETTING: A CRITICAL ANALYSIS THROUGH THE LENS OF THE EBBINGHAUS CURVE." International Education and Research Journal 10, no. 9 (2024). https://doi.org/10.21276/ierj24719743498678.
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This study critically examines the role of artificial intelligence (AI) in enhancing memorization and reducing forgetting, through the lens of Ebbinghaus' Forgetting Curve. By analyzing AI tools like Anki and Duolingo, it explores how spaced repetition and adaptive learning technologies optimize memory retention compared to traditional methods. A comprehensive literature review and comparative analysis highlight the effectiveness of these tools in personalizing learning experiences. The study also delves into emerging trends such as neuroscience-inspired algorithms and real-time adaptive technologies, addressing challenges like accessibility and cognitive overload. Key findings suggest that AI-driven approaches offer notable improvements in memory retention over traditional techniques. The study concludes with practical strategies for integrating AI in educational contexts, advocating for a balanced approach to technology-enhanced learning.
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Kostadinov, O. "EFFECTIVE STRATEGIES FOR MEMORIZING FOREIGN LANGUAGE VOCABULARY." International independent scientific journal 63 (July 4, 2024). https://doi.org/10.5281/zenodo.12700654.
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<strong><em>Abstract</em></strong> <em>Learning and memorizing foreign language vocabulary is a multifaceted process influenced by various stages of language acquisition and the natural phenomenon of forgetting. This paper discusses effective strategies for overcoming the challenges associated with forgetting and consolidating foreign vocabulary. By examining the Ebbinghaus Forgetting Curve and the impact of repetition on memory retention, the paper highlights the importance of spaced repetition and other mnemonic techniques. The stages of foreign word learning, from initial recognition to natural active vocabulary use, are explored to provide insights into effective vocabulary acquisition. Key strategies such as spaced repetition, active use, mnemonics, and contextual learning are emphasized to enhance long-term retention and active use of foreign language vocabulary.</em>
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Li, Meng, Shi-Zhe Wang, Ya-Bo Zhao, et al. "Rac1 in parvalbumin neurons of the medial prefrontal cortex governs rapid forgetting of social memory." Molecular Psychiatry, March 29, 2025. https://doi.org/10.1038/s41380-025-02963-9.
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Abstract Social memory can undergo rapid forgetting at first according to the Ebbinghaus forgetting curve, for which the underlying mechanism remains entirely unknown. Here, we reported that rapid forgetting of social memory did not occur as indicated by social preference on stranger 2 (S2) over stranger 1 (S1) mouse, tested shortly after social interaction with S1. However, rapid forgetting of both social and object memories occurred as indicated by no social or object preference, respectively, when the constitutive active (CA) variant of Rac1 was knocked-in parvalbumin (PV) but not somatostatin (SST) neurons of the brain. Furthermore, rapid forgetting of only social memory occurred if this CA variant was knocked-in PV but not SST neurons of the medial prefrontal cortex (mPFC). By contrast, rapid forgetting of social memory was prevented by the dominant negative (DN) variant of Rac1 knocked-in PV neurons of the mPFC. Moreover, fiber photometry revealed that PV but not SST neurons of the mPFC generated dual calcium peaks to delineate each social interaction event. Thus, PV-specific Rac1 activity of the mPFC is both necessary and sufficient for controlling social behavior via rapid forgetting of social memory, providing a novel understanding of social behaviors under health and disease conditions.
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Liu, Feinan, and Supachai Areerungruang. "Chinese character cognitive cards: Factors affecting Chinese characters using forgetting curves." Humanities, Arts and Social Sciences Studies, August 27, 2024. http://dx.doi.org/10.69598/hasss.24.2.263646.
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Based on learning strategies, many children require increased accuracy in writing Chinese characters. To address this issue, Chinese character cognitive cards were introduced by the researchers, utilizing the Ebbinghaus forgetting curve (FC) to identify and analyze the factors influencing the retention of Chinese character memory. A questionnaire survey was conducted among 556 children aged 10 to 12 in China. In this study, the technology acceptance model (TAM) and the stimulus organ-response model (SOR) were used to study the factors affecting the intention and behavior of using Chinese character cognitive cards based on the function of the FC. The research also aimed to determine the relationship between the frequency of card usage and memory retention. Preliminary analysis indicated that repeated exposure to the cognitive cards significantly reduces the rate of forgetting. Additionally, the study examined the impact of the cognitive cards on different proficiency levels in Chinese character writing. The results show that the function of the FC in the Chinese character cognitive cards can make learners perceive the ease of use and usefulness of the Chinese character cognitive cards. The use of the FC positively influences Chinese character memory ability, and the influence is strong.
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Qingyan, Meng, and Chen Zhenli. "Precise Push and Personalized Guidance of Ideological and Political Education in Colleges and Universities Based on Big Data Technology." International Journal of High Speed Electronics and Systems, June 2, 2025. https://doi.org/10.1142/s0129156425406084.
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The college students’ networked existence mode promotes the cyberspace to become a highly permeable field for ideological and political education content supply. However, the presentation forms of different fields and different subjects for college students’ personalized ideological needs are relatively simple and flat to some extent, lacking digital and three-dimensional development, which limits the content penetration to some extent. This situation leads to inaccurate similarity between students and users, which reduces the satisfaction of resource push. In order to improve the precise push quality, the research on precise push and personalized guidance based on big data technology is carried out this time. The ideological and political education resources are preprocessed, and the theme feature extraction of ideological and political education resources data is completed based on TF-IDF. Based on Ebbinghaus forgetting curve, the user similarity calculation is completed considering the user’s needs. Based on the results of similarity calculation, a model of precise push and personalized guidance is constructed to complete resource push and realize personalized guidance of users. The experimental results show that the area under ROC curve (AUC) value of the proposed method is higher, the loss value is lower than 0.01, and the satisfaction of resource push is higher than 0.93.
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Wen, Shengkai, Jun Li, Zexin Wang, Zhenzhao Zhang, and Jianhua Zhang. "Low-power and high PPF feature synaptic transistors with 3D interface based on SnO2 nanofibers for neuromorphic application." Applied Physics Letters 123, no. 4 (2023). http://dx.doi.org/10.1063/5.0159012.
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Featuring excellent computational rates and highly parallel computing, human brain-based neuromorphic devices have attracted the attention of more and more researchers. There have been numerous reports investigating the use of transistors to simulate synaptic functions; however, the majority of the dielectric and channel layers in these devices are layer-stacked structures, which are not conducive to the modulation of the active layers. Moreover, optimizing the paired pulse facilitation index is a critical factor in enhancing the short-term memory of synaptic devices and constructing high-precision synaptic systems, but it has received inadequate attention. In this study, we present a low-cost electrolyte-gated synaptic transistor with three-dimensional (3D) interfacial contacts, in which the channel layer is SnO2 nanofibers, and the 3D interface reduces the power consumption to 9.6 fJ. This study has simulated some important synaptic behaviors; importantly, the PPF value is as high as 223%, which is related to the slow kinetics of sodium ions. In addition, the Ebbinghaus forgetting curve and its application to image memory are also simulated. These findings provide valuable insight for the future development of complex neuromorphic systems.
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Sunny, Ashly, and R. Thamankar. "Persistent photoconductivity and Emulating Ebbinghaus forgetting curve via characterization of excitatory synaptic transmission in a ZnO-based optoelectronic synapse with ultra-low power (∼ fJ) consumption." Applied Surface Science, November 2024, 161767. http://dx.doi.org/10.1016/j.apsusc.2024.161767.
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Po'latova, Ruxshonaxon. "10 PROVEN STRATEGIES TO MASTER A FOREIGN LANGUAGE FASTER." February 24, 2025. https://doi.org/10.5281/zenodo.14915525.
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<em>Mastering a foreign language is a transformative process that opens up a world of opportunities for cultural understanding, professional growth, and personal development. However, achieving fluency in a foreign language within a short period remains a challenge for many learners. This article explores ten proven strategies to accelerate the language acquisition process, grounded in theoretical and practical insights from linguistics, cognitive science, and technology. These strategies include immersion, spaced repetition, active practice, context-based learning, the use of technology, and the cultivation of motivation and a positive mindset. Immersion is highlighted as one of the most effective methods for rapid learning, as it provides authentic exposure to the target language in real-world settings. Spaced repetition, supported by Ebbinghaus’s Forgetting Curve, ensures long-term retention of vocabulary and grammatical structures. Active practice, particularly in speaking and writing, reinforces language use and builds confidence. Context-based learning enables learners to focus on relevant vocabulary and phrases, making language acquisition practical and applicable. Additionally, technological advancements, such as language learning apps, virtual reality, and online platforms, have made learning more accessible and engaging for learners of all levels. The discussion emphasizes the interplay of these strategies, showing how their integration creates a holistic approach to mastering a foreign language efficiently. Motivation and mindset are identified as critical factors, with intrinsic goals driving sustained engagement and progress. This multifaceted framework addresses common challenges such as vocabulary retention, fear of making mistakes, and plateaus in learning. By combining cognitive and practical approaches, learners can enhance their ability to acquire, retain, and apply a foreign language effectively. This article contributes to the field of language learning by providing evidence-based techniques and actionable recommendations for learners aiming to achieve fluency faster. It is particularly relevant for students, professionals, and language enthusiasts seeking efficient ways to overcome barriers to language learning. Through the implementation of these strategies, learners can not only achieve fluency but also deepen their cultural awareness and global communication skills, equipping them for success in a multilingual world.</em>