Implicit Behavior Research Articles

The Effects of Internalized Shame and Social Exclusion on Approach-Avoidance Behavior

Background: This study examined the effects of internalized shame and social exclusion on approach-avoidance behavior.Methods: A survey was conducted offline and online with 618 adult participants to assess their level of internalized shame. Among them, 59 were selected and assigned to either the high- or low-internalized shame group. These participants were then randomly assigned to either the social exclusion or inclusion condition. After experimental manipulation, explicit and implicit approach-avoidance behaviors were measured.Results: In terms of explicit behavioral choices, the high-internalized shame group exhibited approach behavior in the social inclusion condition and avoidance behavior in the exclusion condition. In terms of explicit behavioral preferences, the high-internalized shame group and social exclusion condition exhibited avoidance responses. In terms of implicit behavior, the low-internalized shame group exhibited approach responses in the social exclusion condition, whereas the high-internalized shame group exhibited avoidance responses.Conclusions: This study investigated the impact of internalized shame and social exclusion on social functioning and confirmed that individuals with high levels of internalized shame are more vulnerable to social exclusion. Finally, the implications, significance, and limitations of this study are discussed.

ANAGL: A Noise-Resistant and Anti-Sparse Graph Learning for Micro-Video Recommendation

In recent years, graph convolutional networks (GCNs) have seen widespread utilization within micro-video recommendation systems, facilitating the understanding of user preferences through interactions with micro-videos. Despite the commendable performance exhibited by GCN-based methodologies, several persistent issues demand further scrutiny. Primarily, most user-micro-video interactions involve implicit behaviors, such as clicks or abstentions, which may inadvertently capture irrelevant micro-video content, thereby introducing significant noise (false touches, low watch-ratio, low ratings) into users’ histories. Consequently, this noise undermines the efficacy of micro-video recommendations. Moreover, the abundance of micro-videos has resulted in fewer interactions between users and micro-video content. To tackle these challenges, we propose a noise-resistant and anti-sparse graph learning framework for micro-video recommendation. Initially, we construct a denoiser that leverages implicit multi-attribute information (e.g., watch-ratio, timestamp, ratings, and so on) to filter noisy data from user interaction histories. This process yields high-fidelity micro-video information, enabling a more precise modeling of users’ feature preferences. Subsequently, we employ a multi-view reconstruction approach and utilize cross-view self-supervised learning to gain insights into user and micro-video features. This strategic approach effectively mitigates the issue of data sparsity. Extensive experiments conducted on two publicly available micro-video recommendation datasets validate the effectiveness of our proposed method. For in-depth details and access to the code, please refer to our repository at “ https://github.com/kbk12/ANAGL.git .”

Do quality criteria of instructional explanatory videos in algebra influence student choice? A feasibility study using conjoint analysis to assess implicit reasoning in educational contexts

ABSTRACT Many students struggle with algebra and often turn to instructional explanatory videos (IEVs) as learning resources. This feasibility study explored whether students implicitly recognize quality indicators in algebra videos and how these influence their choices. Using Conjoint analysis in a 2×2 design, we examined the focus on subject-matter didactical and multimedia features among 34 students aged 14–15 years. We presented authentic IEVs on solving algebraic equations varying in didactical and multimedia features. Bayesian analysis revealed that both didactical and multimedia features influenced video choices with no notable differences in impact strength. Student characteristics (mathematics achievement, interest, and self-concept) affected preferences only for the didactical features. The study concludes that students can identify quality indicators in math videos, although this recognition is limited and may be further enhanced through targeted guidance. Methodologically, conjoint analysis demonstrated its efficacy in mathematics education research, effectively addressing social desirability bias and implicit behaviors.

Fake User Detection Based on Multi-Model Joint Representation

The existing deep learning-based detection of fake information focuses on the transient detection of news itself. Compared to user category profile mining and detection, transient detection is prone to higher misjudgment rates due to the limitations of insufficient temporal information, posing new challenges to social public opinion monitoring tasks such as fake user detection. This paper proposes a multimodal aggregation portrait model (MAPM) based on multi-model joint representation for social media platforms. It constructs a deep learning-based multimodal fake user detection framework by analyzing user behavior datasets within a time retrospective window. It integrates a pre-trained Domain Large Model to represent user behavior data across multiple modalities, thereby constructing a high-generalization implicit behavior feature spectrum for users. In response to the tendency of existing fake user behavior mining to neglect time-series features, this study introduces an improved network called Sequence Interval Detection Net (SIDN) based on Sequence to Sequence (seq2seq) to characterize time interval sequence behaviors, achieving strong expressive capabilities for detecting fake behaviors within the time window. Ultimately, the amalgamation of latent behavioral features and explicit characteristics serves as the input for spectral clustering in detecting fraudulent users. The experimental results on Weibo real dataset demonstrate that the proposed model outperforms the detection utilizing explicit user features, with an improvement of 27.0% in detection accuracy.

Asymmetry in political polarization at multiple levels of bias

AbstractWhile some studies show ideological asymmetry in outgroup bias between rightists and leftists, those studies often target an ideologically biased outgroup. Here, we bypass this issue by targeting the ideological outgroups (rightists for leftists, and leftists for rightists). We rely on a magnetoencephalography‐based approach delineating function‐specific neural mechanisms to test for ideological asymmetries at multiple levels: explicit psychological self‐reports, implicit behavioral bias, and neural oscillations. Using a computational model balancing the stimuli and screening 81 rightists and leftist Israeli individuals, we find ideological asymmetry with rightists being more biased at all three levels. Furthermore, the neural results add important insights by uncovering two underlying mechanisms: The first (late beta‐band motor activity) is strongly associated with implicit behavior, while the second (early alpha‐band dorsal anterior cingulate activity) reveal an antileftist bias for both groups. We discuss implications of the findings on bias, ideological asymmetry, their neural underpinnings, and social norms.

Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planning

Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planning

A holistic view on positive and negative implicit feedback for micro-video recommendation

Micro-video online platforms have become prevalent in recent years, necessitating effective recommender systems to help identify users’ preferences. Previous works have made significant efforts to leverage the multi-modal information and leverage the multi-interests of users to enhance the recommendation performance. Despite their effectiveness, they ignored valuable information implied in users’ negative feedback. Moreover, they did not consider the fact that the positive and negative implicit behaviors form a holistic sequence (exposure sequence) in the micro-video recommendation. Additionally, they failed to capture sequential and non-sequential dependencies among multi-type behaviors in users’ long interactive sequence. To address these issues, we devise a HOlistic framework to understand the natural distinction and inherent connection between positive and negative feedback for micro-video recommendation with Multiplex Networks, named HMNet. Specifically, the HMNet framework first builds a hierarchical architecture to distinguish two types of behaviors: the exposure layer represents observations (including skips and clicks), while the conversion layer is on behalf of clicks. We further propose two multiplex networks to capture dependencies within and across the two layers. In particular, a multiplex recurrent neural network is proposed to encode sequential cross-type dependencies, while a relation-aware multiplex graph convolutional network is to model the non-sequential semantic dependencies between multi-type behaviors. Extensive experiments on two public real-world datasets demonstrate the effectiveness of our model compared with the state-of-the-art methods.

Influence of Early Family Nurturing Environment on Children's Psychological and Emotional Social Development.

A good family environment and rearing mode play important roles in children's physical and psychological growth. This study aims to clarify the specific influence of early family nurturing environment on children's emotional and social development through longitudinal observation to provide theoretical reference for promoting the healthy development of children's bodies and minds. Overall, 1320 parents of children aged 2 to 6 yr old in Xinyang City, Henan Province in 2021, were selected by stratified sampling. The children's physical and mental development, family rearing environment and emotional and social development were investigated by questionnaire. 58.03% family rearing environment is good, 27.95% normal, and 14.02% bad. There were significant differences in positive rates of adaptive ability, social behavior, language, developmental quotient score and emotional sociality among children in different families (all P<0.05). Language/cognition was significantly positively correlated with adaptive ability, social behavior, language and developmental quotient (all P<0.05), and social adjustment/self-care was significantly positively correlated with adaptive ability, social behavior, language and developmental quotient (all P<0.05). Emotional warmth/environmental atmosphere was negatively correlated with implicit behavior and disorder and positively correlated with ability (all P<0.05). Language/cognition was positively correlated with explicit behavior and ability (all P<0.05). Social adjustment/self-care was negatively correlated with explicit behavior and positively correlated with ability (all P<0.05); Neglect/punishment was significantly negatively correlated with explicit behavior, implicit behavior and disorder and significantly positively correlated with ability (all P<0.05). The early family environment affects children's physical, mental, emotional and social development.

E-commerce bookstore user alignment model based on multidimensional feature joint representation and implicit behavior compensation

The accurate identification of the same user across different e-commerce platforms is crucial for cross-platform recommendations. Given the complexity of the feature space of heterogeneous e-commerce platforms and the sparsity of explicit user behavior data, we propose a heterogeneous e-commerce network alignment model based on multidimensional joint representation and implicit behavior compensation. First, in response to the complexity of the feature space of heterogeneous e-commerce platforms, the UBN2vec model was constructed. The model generates independent embeddings for multidimensional features of “user attributes-behaviors-neighborhood”, and integrates them using an attention mechanism. Furthermore, through joint optimization of user multidimensional features, low-rank dense user overall embedding vectors are generated, effectively representing the complex features of e-commerce users. Second, to alleviate the sparsity issue of user behavior data on an e-commerce bookstore platform, we propose a method for compensating implicit user behavior data based on item association. This method predicts potential user behavior information by mining the latent correlations between items. It effectively captures user preferences and improves alignment accuracy. Finally, to reduce the computational complexity of the user-matching process, a user interest group block indexing method based on user interest preferences is developed. This method reduces matching times among users with significantly different interests, thereby reducing the computational complexity of the heterogeneous e-commerce bookstore user alignment algorithm. Experiments show that the proposed model can accurately and efficiently identify the same user across e-commerce bookstore platforms.

Interdependence analysis on heterogeneous data via behavior interior dimensions

Interdependent dimensions including categorical and continuous variables can be seen commonly as heterogeneous behavioral data in the real world. Mixed-type objects are more or less associated in terms of certain coupling relationships. The usual representation of such behavioral data is an information table with explicit behavior exterior dimensions (i.e. the original attributes to describe data heterogeneity), assuming the independence of dimensions and the independence of objects. However, both variables and objects are actually very often interdependent on one another either explicitly or implicitly in functional and semantic manners. Limited research has been done in analyzing such interactions among dimensions and those relationships among objects, leading to the learning results to be more local than global. This paper proposes the interdependence analysis to capture the functional multifarious relationships among attributes and among objects in heterogeneous data by addressing the coupling context and coupling weights in unsupervised learning. Such global couplings consider the interactions within discrete dimensions, within numerical attributes and across them, as well as the relationships within an individual object and between multiple objects, to form the attribute-based and object-based coupled data representation schemes based on feature conversion and neighborhood calculation. In addition, we interpret both the representation models via implicit behavior interior dimensions (i.e. the newly defined attributes to model data interdependence) to explain the intrinsic rationales for the superiority of our proposed methods. This work explicitly models the coupling of multiple attributes and the coupling of multiple objects for heterogeneous data sets, demonstrated by various data mining and machine learning applications, such as cluster structure analysis, data clustering evaluation, and data density comparison. Moreover, the sensitivity study is carried out to tune the neighborhood parameter and weight parameter, and the scalability analysis is explored to test the robustness of both models. Extensive experiments on a series of synthetic data sets and multiple UCI data sets show that our proposed framework can effectively capture the global couplings of both heterogeneous variables and mixed-type objects, and is superior to the traditional way as well as the state-of-the-art approaches, which is also verified by statistical analysis.

Exploring Behavior Patterns for Next-POI Recommendation via Graph Self-Supervised Learning

Next-point-of-interest (POI) recommendation is a crucial part of location-based social applications. Existing works have attempted to learn behavior representation through a sequence model combined with spatial-temporal-interval context. However, these approaches ignore the impact of implicit behavior patterns contained in the visit trajectory on user decision making. In this paper, we propose a novel graph self-supervised behavior pattern learning model (GSBPL) for the next-POI recommendation. GSBPL applies two graph data augmentation operations to generate augmented trajectory graphs to model implicit behavior patterns. At the same time, a graph preference representation encoder (GPRE) based on geographical and social context is proposed to learn the high-order representations of trajectory graphs, and then capture implicit behavior patterns through contrastive learning. In addition, we propose a self-attention based on multi-feature embedding to learn users’ short-term dynamic preferences, and finally combine trajectory graph representation to predict the next location. The experimental results on three real-world datasets demonstrate that GSBPL outperforms the supervised learning baseline in terms of performance under the same conditions.

Role Of Parenting Dalam Menanggulangi Digresi Gadget Pada Anak

The purpose of this study was to describe the role of parents in responding to gadgets and their implicit behavior towards children in Pakistaji village, Wonoasih sub-district, Probolinggo city with continuous control and orgrib Koran organizations (GMM) as shields for children. deviant behavior in children. The method used is descriptive qualitative with a case study type approach. Data collection was carried out through observation, interviews, and supported by documentation. This research was conducted in Pakistaji Village, Wonoasih District, Probolinggo City. The subjects in the study were parents and some children who depend on gadgets. The results of this study are the following: The frequency of using gadgets needs to be limited. Children don't need too many gadgets, because the most important thing is that children can use them and know how to use them optimally. Excessive intensity of using gadgets can trigger irregularities. The role of parents in responding to the use of gadgets in their children is very important. This is done so that children do not abuse their parents' trust in them. The attitude that needs to be shown is to accompany children when playing gadgets and parents must be able to operate them. As for the consequences if parents do not supervise the use of gadgets on their children, then their children will engage in deviant behavior. Forms of deviation in the form of attitudes and speech that are not good. Usually in the form of erotic attitudes and dirty words.The purpose of this study was to describe the role of parents in responding to gadgets and their implicit behavior towards children in Pakistaji village, Wonoasih sub-district, Probolinggo city with continuous control and orgrib Koran organizations (GMM) as shields for children. deviant behavior in children. The method used is descriptive qualitative with a case study type approach. Data collection was carried out through observation, interviews, and supported by documentation. This research was conducted in Pakistaji Village, Wonoasih District, Probolinggo City. The subjects in the study were parents and some children who depend on gadgets. The results of this study are the following: The frequency of using gadgets needs to be limited. Children don't need too many gadgets, because the most important thing is that children can use them and know how to use them optimally. Excessive intensity of using gadgets can trigger irregularities. The role of parents in responding to the use of gadgets in their children is very important. This is done so that children do not abuse their parents' trust in them. The attitude that needs to be shown is to accompany children when playing gadgets and parents must be able to operate them. As for the consequences if parents do not supervise the use of gadgets on their children, then their children will engage in deviant behavior. Forms of deviation in the form of attitudes and speech that are not good. Usually in the form of erotic attitudes and dirty words.

The “Mixed” Reality of Virtual Brand Endorsers: Understanding the Effect of Brand Engagement and Social Cues on Technological Perceptions and Advertising Effectiveness

Within the context of virtual human influencers, the current study adds to the growing literature examining the impact of a social media–based mix of reality and virtuality on perceived anthropomorphism and authenticity, in addition to the subsequent effects on advertising perceptions and implicit behavior. Through an experimental design with 242 participants, data suggest that while some elements of realism and product engagement levels do increase anthropomorphic and authenticity perceptions, the effects significantly attenuate when the virtual influencer is engaging with too much reality in a single social media post, such as consuming a real-world branded product with a real human. Further, data suggest that presenting virtual influencers within a “mixed” reality social media–based environment can lead to implicit product selection through endorser and brand credibility and attitude toward the brand.

Short-Term Air Pollution Forecasting Using Embeddings in Neural Networks

Air quality is a highly relevant issue for any developed economy. The high incidence of pollution levels and their impact on human health has attracted the attention of the machine-learning scientific community. We present a study using several machine-learning methods to forecast NO2 concentration using historical pollution data and meteorological variables and apply them to the city of Erfurt, Germany. We propose modelling the time dependency using embedding variables, which enable the model to learn the implicit behaviour of traffic and offers the possibility to elaborate on local events. In addition, the model uses seven meteorological features to forecast the NO2 concentration for the next hours. The forecasting model also uses the seasonality of the pollution levels. Our experimental study shows that promising forecasts can be achieved, especially for holidays and similar occasions which lead to shifts in usual seasonality patterns. While the MAE values of the compared models range from 4.3 to 15, our model achieves values of 4.4 to 7.4 and thus outperforms the others in almost every instance. Those forecasts again can for example be used to regulate sources of pollutants such as, e.g., traffic.

On Robustness and Sensitivity of a Neural Language Model: A Case Study on Italian L1 Learner Errors

In this paper, we propose a comprehensive linguistic study aimed at assessing the implicit behavior of one of the most prominent Neural Language Models (NLM) based on Transformer architectures, BERT Devlin et al., when dealing with a particular source of noisy data, namely essays written by L1 Italian learners containing a variety of errors targeting grammar, orthography and lexicon. Differently from previous works, we focus on the pre-training stage and we devise two complementary evaluation tasks aimed at assessing the impact of errors on sentence-level inner representations in terms of semantic robustness and linguistic sensitivity. While the first evaluation perspective is meant to probe the model's ability to encode the semantic similarity between sentences also in the presence of errors, the second type of probing task evaluates the influence of errors on BERT's implicit knowledge of a set of raw and morpho-syntactic properties of a sentence. Our experiments show that BERT's ability to compute sentence similarity and to correctly encode multi-leveled linguistic information of a sentence are differently modulated by the category of errors and that the error hierarchies in terms of robustness and sensitivity change across layer-wise representations.

EEG measures of attention toward food-related stimuli vary with food neophobia

Humans differ strongly in their willingness to try novel foods. Hesitance to try new foods is referred to as food neophobia. Understanding food neophobia is important, as it can be a significant barrier to adopt a healthy, balanced or plant-based diet. We here use electroencephalogram (EEG) recordings to obtain insight in the early attentional processes towards food stimuli as a function of food neophobia. 43 Dutch participants completed the food neophobia scale after which they were presented with pictures of familiar and unfamiliar foods and a 15-minute movie about the origin and production of an unfamiliar food. We extracted two EEG-based metrics of attention: the late positive potential (LPP) amplitude in response to the food pictures, and inter-subject correlations (ISC-EEG) during the movie. The latter is a novel metric, based on similarities in EEG over time between individuals who are presented with the same stimulus, and suitable for examining attention towards continuous stimuli such as movies. Additionally, participants were asked to taste familiar and unfamiliar soups, and they were asked to rate the pictures and soups for valence and arousal. ISC-EEG and the LPP amplitude increased and sip size decreased with food neophobia, not only for unfamiliar food pictures, but also for familiar food pictures. Self-reported emotional experience was affected by food neophobia for unfamiliar food pictures or soups, but not for familiar ones. We conclude that food neophobia is associated with increased attentional processing and immediate implicit behavior, for all food stimuli and not only for unfamiliar food stimuli. This indicates that all food-related stimuli are of high importance to food neophobic individuals and that self-reported emotion does not capture the entire experience of food. The results also indicates that, unlike the name suggest, food neophobia does not only affect processing of novel foods, but of any food regardless of familiarity.

Altered recognition of fearful and angry facial expressions in women with fibromyalgia syndrome: an experimental case–control study

Evidence relative to facial emotion recognition and the role played by alexithymia in fibromyalgia syndrome is rare and heterogeneous. In this work, we investigated this ability in fibromyalgia investigating the implicit behaviour in the facial emotion recognition task, focusing on fear and anger. Twenty women with fibromyalgia and twenty healthy women as controls performed a facial emotion recognition of fearful and angry expressions. Their implicit behaviour was scored in accordance with the redundant target effect. The level of alexithymic traits through a standard psychological questionnaire and its effect on behavioral performance were also assessed. Participants affected by fibromyalgia reported a lower level of accuracy in recognizing fearful and angry expressions, in comparison with the controls. Crucially, such a difference was not explained by the different levels of alexithymic traits between groups. Our results agreed with some previous evidence suggesting an altered recognition of others’ emotional facial expressions in fibromyalgia syndrome. Considering the role of emotion recognition on social cognition and psychological well-being in fibromyalgia, we underlined the crucial role of emotional difficulties in the onset and maintenance of the symptoms life-span.

IIDQN: An Incentive Improved DQN Algorithm in EBSN Recommender System

Event-based Social Networks (EBSN), combining online networks with offline users, provide versatile event recommendations for offline users through complex social networks. However, there are some issues that need to be solved in EBSN: (1) The online static data could not satisfy the online dynamic recommendation demand; (2) the implicit behavior information tends to be ignored, reducing the accuracy of the recommendation algorithm; and (3) the online recommendation description is inconsistent with the offline activity. To address these issues, an Incentive Improved DQN (IIDQN) based on Deep Q-Learning Networks (DQN) is proposed. More specifically, we introduce the agents to interact with the environment through online dynamic data. Furthermore, we consider two types of implicit behavior information: the length of the user’s browsing time and the user’s implicit behavior factors. As for the problem of inconsistency, based on blockchain technology, a new activities event approach on EBSN is proposed, where all activities are recorded on the chain. Finally, the simulation results indicate that the IIDQN algorithm greatly outperforms in mean rewards and recommendation performance than before DQN.

Mobile Learning Platform in Cloud Computing with Information Security and Android System

In order to meet the real-time communication between teachers and students and improve students' interest and efficiency in learning, this paper designs a mobile learning communication system based on information security and Android system with cloud computing. By using the method of combining database records and web logs to mine the user's browsing records and behaviors, these implicit user behaviors are transformed into explicit user evaluations of the project. Then, the cosine similarity calculation method is used to calculate users and the similarity between the users. The users are clustered by the K-means clustering method, so that the users are automatically divided into several user clusters according to their behaviors. Finally, the user's nearest neighbor score is used to predict the pair. Based on the above method, a mobile learning communication system based on Android is realized, and the system basically meets the functional needs of users. The development of this system not only promotes the mutual communication between students but also facilitates the students' learning, which has a certain promoting effect on the improvement of their academic performance.

Tripartite Evolutionary Game Analysis of Trust Relationship between Enterprises in a Cloud Manufacturing Environment: A Service Composition Perspective

Due to the existence of limited rationality and information asymmetry, trust between enterprises in the cloud manufacturing environment is characterized by significant complexity. In this paper, to promote the formation of a better trust relationship between enterprises and to ensure the efficient service and stable operation of the manufacturing process to a greater extent, we take into account the subjective cognition of the game parties from the perspective of service combination, introduce the prospect theory, construct an evolutionary game model of “service demander‐service provider‐platform supervisor,” analyze the evolutionary stabilization strategy, and explore the influence of different factors on the interenterprise trust relationship in the cloud manufacturing environment through numerical simulation. The results show that enhancing the synergy between multiple service providers has a significant impact on improving trust between cloud manufacturing enterprises. The rewards and punishments of the cloud manufacturing platform are conducive to improving interenterprise trust. There exists a critical value dividing opportunistic behavior into explicit and implicit behaviors. When it is less than the critical value, opportunistic behavior is implicit and the service demander trusts the service provider and the transaction continues; when it is greater than the critical value, opportunistic behavior is explicit and the service demander no longer trusts until the service provider is fully trustworthy. The inhibitory effect of good transaction history on opportunistic behavior is evident. Finally, the reasons and effects of the above factors and the subjective perceptions of the game parties in cloud manufacturing are discussed, as well as their concrete manifestations in the CASICloud (China Aerospace Science&amp;Industry Cloud) and relevant suggestions are put forward.