Interaction Strategies Research Articles

A novel interactive deep cascade spectral graph convolutional network with multi-relational graphs for disease prediction

Graph neural networks (GNNs) have recently grown in popularity for disease prediction. Existing GNN-based methods primarily build the graph topological structure around a single modality and combine it with other modalities to acquire feature representations of acquisitions. The complicated relationship in each modality, however, may not be well highlighted due to its specificity. Further, relatively shallow networks restrict adequate extraction of high-level features, affecting disease prediction performance. Accordingly, this paper develops a new interactive deep cascade spectral graph convolutional network with multi-relational graphs (IDCGN) for disease prediction tasks. Its crucial points lie in constructing multiple relational graphs and dual cascade spectral graph convolution branches with interaction (DCSGBI). Specifically, the former designs a pairwise imaging-based edge generator and a pairwise non-imaging-based edge generator from different modalities by devising two learnable networks, which adaptively capture graph structures and provide various views of the same acquisition to aid in disease diagnosis. Again, DCSGBI is established to enrich high-level semantic information and low-level details of disease data. It devises a cascade spectral graph convolution operator for each branch and incorporates the interaction strategy between different branches into the network, successfully forming a deep model and capturing complementary information from diverse branches. In this manner, more favorable and sufficient features are learned for a reliable diagnosis. Experiments on several disease datasets reveal that IDCGN exceeds state-of-the-art models and achieves promising results.

Elementary School Teachers’ Avoidance of Model-Based Physical Education Lessons: A Grounded Theory Approach

PURPOSE This study sought to explore elementary school (ES) teachers' avoidance of teaching model-based instruction (MBI) in physical education (PE) lessons.METHODS An open-ended questionnaire (n=93) and three focus group interviews (FGI ) were conducted with seven ES teachers. The collected data were analyzed using grounded theory analysis procedures (Strauss & Corbin, 1997).RESULTS Accordingly, we derived a grounded theory paradigm model composed of the core phenomenon (ES teacher’s avoidance of MBI in PE lessons), causal conditions (traditional difficulties of Elementary PE lessons, mismatch between MBI and ES teachers/PE lessons, lack of experience and teacher knowledge for/in MBI), contextual conditions (complex instructor organization, powerful trend of play), intervening conditions (value orientation for fun-focused PE, misunderstanding about PE curriculum), interactive strategies (focus on screening physical activities, preparing for PE lessons with YouTube rather than teacher guide book), and results (learner inclusive effects and de-curricularization).CONCLUSIONS ES teachers’ avoidance of MBI in PE lessons is a result of several reported problems with elementary PE lessons and is likely to be a recurring problem in the future. To encourage ES teachers’ MBI in PE lessons, efforts should be made to build practical knowledge of model use in pre- and in-service teacher education.

Virtual Simulation in Forensic Medicine Education in China

Virtual Simulation is an innovative interactive pedagogical strategy in higher education. This paper analyzes the virtual simulation teaching projects currently underway in forensic medicine education in China. It explores the characteristics, advantages, and limitations of virtual simulation teaching in forensic medicine in China. The first batch of 11 virtual simulation projects are all synthetic designing experiments of over 2 class hours, mainly targeting the undergraduate students in the second year and above. Most projects focus on forensic pathology education. They provide active immersive experiential learning with data-driven teaching and real-time assessment. Further efforts are needed to integrate advanced technologies, diversify teaching content, and promote collaboration and resource sharing.

A multimodal driver monitoring benchmark dataset for driver modeling in assisted driving automation

In driver monitoring various data types are collected from drivers and used for interpreting, modeling, and predicting driver behavior, and designing interactions. Aim of this contribution is to introduce manD 1.0, a multimodal dataset that can be used as a benchmark for driver monitoring in the context of automated driving. manD is the short form of human dimension in automated driving. manD 1.0 refers to a dataset that contains data from multiple driver monitoring sensors collected from 50 participants, gender-balanced, aged between 21 to 65 years. They drove through five different driving scenarios in a static driving simulator under controlled laboratory conditions. The automation level (SAE International, Standard J3016) ranged from SAE L0 (no automation, manual) to SAE L3 (conditional automation, temporal). To capture data reflecting various mental and physical states of the subjects, the scenarios encompassed a range of distinct driving events and conditions. manD 1.0 includes environmental data such as traffic and weather conditions, vehicle data like the SAE level and driving parameters, and driver state that covers physiology, body movements, activities, gaze, and facial information, all synchronized. This dataset supports applications like data-driven modeling, prediction of driver reactions, crafting of interaction strategies, and research into motion sickness.

Secure cross-chain interaction solution in multi-blockchain environment

In the context of the increasingly diversified blockchain technology, interoperability among heterogeneous blockchains has become key to further advancing this field. Existing cross-chain technologies, while facilitating data and asset exchange between different blockchains to some extent, have exposed issues such as insufficient security, low efficiency, and inconsistent standards. Consequently, these issues give rise to significant obstacles in terms of both scalability and seamless communication among blockchains within a multi-chain framework. To address this, this paper proposes an efficient method for cross-chain interaction in a multi-chain environment. Building upon the traditional sidechain model, this method employs smart contracts and hash time-locked contracts (HTLCs) to design a cross-chain interaction scheme. This approach decentralizes the execution of locking, verifying, and unlocking stages in cross-chain transactions, effectively avoiding centralization risks associated with third-party entities in the process. It also greatly enhances the efficiency of fund transfers between the main chain and sidechains, while ensuring the security of cross-chain transactions to some extent. Additionally, this paper innovatively proposes a cross-chain data interaction strategy. Through smart contracts on the main chain, data from sidechains can be uploaded, verified, and stored on the main chain, achieving convenient and efficient cross-chain data sharing. The contribution of this paper is the development of a decentralized protocol that coordinates the execution of cross-chain interactions without the need to trust external parties, thereby reducing the risk of centralization and enhancing security. Experimental results validate the effectiveness of our solution in increasing transaction security and efficiency, with significant improvements over existing models. Our experiments emphasize the system's ability to handle a variety of transaction scenarios with improved throughput and reduced latency, highlighting the practical applicability and scalability of our approach.

Suggestive answers strategy in human-chatbot interaction: a route to engaged critical decision making.

In this study, we proposed a novel chatbot interaction strategy based on the suggestive ending of answers. This strategy is inspired by the cliffhanger ending narrative technique, which ends a story without specifying conclusions to spark readers' curiosity as to what will happen next and is often used in television series. Common chatbots provide relevant and comprehensive answers to users' questions. In contrast, chatbots with our proposed strategy end their answers with hints potentially interest-triggering users. The suggestive ending strategy aims to stimulate users' inquisition for critical decision-making, relating to a psychological phenomenon where humans are often urged to finish the uncompleted tasks they have initiated. We demonstrated the implication of our strategy by conducting an online user study involving 300 participants, where they used chatbots to perform three decision-making tasks. We adopted a between-subjects factorial experimental design and compared between the following UIs: (1) plain chatbot-it provides a generated answer when participants issue a question; (2) expositive chatbot-it provides a generated answer for a question, adding short summaries of a positive and negative person's opinion for the answer; (3) suggestive chatbot-it provides a generated answer for a question, which ends with a suggestion of a positive and negative person for the answer. We found that users of the suggestive chatbot were inclined to ask more questions to the bot, engage in prolonged decision-making and information-seeking actions, and formulate their opinions from various perspectives. These findings vary with the users' experience with plain and expositive chatbots.

Superb green cycling strategies for microbe-Fe0 neural network-type interaction: Harnessing eight key genes encoding enzymes and mineral transformations to efficiently treat PFOA

To address time-consuming and efficiency-limited challenges in conventional zero-valent iron (ZVI, Fe0) reduction or biotransformation for perfluorooctanoic acid (PFOA) treatment, two calcium alginate-embedded amendments (biochar-immobilized PFOA-degrading bacteria (CB) and ZVI (CZ)) were developed to construct microbe-Fe0 high-rate interaction systems. Interaction mechanisms and key metabolic pathways were systematically explored using metagenomics and a multi-process coupling model for PFOA under microbe-Fe0 interaction. Compared to Fe0 (0.0076 day−1) or microbe (0.0172 day−1) systems, the PFOA removal rate (0.0426 day−1) increased by 1.5 to 4.6 folds in the batch microbe-Fe0 interaction system. Moreover, Pseudomonas accelerated the transformation of Fe0 into Fe3+, which profoundly impacted PFOA transport and fate. Model results demonstrated microbe-Fe0 interaction improved retardation effect for PFOA in columns, with decreased dispersivity a (0.48 to 0.20 cm), increased reaction rate λ (0.15 to 0.22 h−1), distribution coefficient Kd (0.22 to 0.46 cm3∙g−1), and fraction f´(52 % to 60 %) of first-order kinetic sorption of PFOA in microbe-Fe0 interaction column system. Moreover, intermediates analysis showed that microbe-Fe0 interaction diversified PFOA reaction pathways. Three key metabolic pathways (ko00362, ko00626, ko00361), eight functional genes, and corresponding enzymes for PFOA degradation were identified. These findings provide insights into microbe-Fe0 “neural network-type” interaction by unveiling biotransformation and mineral transformation mechanisms for efficient PFOA treatment.

Advancing Educational Practices: Implementation of the Course Review Horay Learning Model in Elementary Schools

Improving the quality of education is an inevitable challenge in the development of society. One approach to achieving this is by implementing innovative learning models. This research aims to investigate the implementation of the Course Review Horay Learning Model in Elementary Schools as an effort to improve educational practices. The research method used is classroom action research involving teachers and students in the learning change process. Over several cycles, data was collected through classroom observations, interviews, and student performance assessments. The course review model is applied with a focus on providing constructive feedback, collaboration between students, and the use of technology in learning. The research results showed a significant increase in student participation, concept understanding, and learning motivation. Teachers reported positive changes in classroom dynamics, increased student engagement, and implementation of more interactive learning strategies. Technological support in implementing the course review horay also makes a positive contribution to learning efficiency. This research provides new insights regarding the potential of the Course Review Horay Learning Model as an effective tool for overcoming challenges in learning in elementary schools. Practical implications and recommendations for further development are also discussed to create an educational environment that is progressive and responsive to the needs of today's students.

Synergistic morphology and feedback control for traversal of unknown compliant obstacles with aerial robots

Animals traverse vegetation by direct physical interaction using their entire body to push aside and slide along compliant obstacles. Current drones lack this interaction versatility that stems from synergies between body morphology and feedback control modulated by sensing. Taking inspiration from nature, we show that a task-oriented design allows a drone with a minimalistic controller to traverse obstacles with unknown elastic responses. A discoid sensorized shell allows to establish and sense contacts anywhere along the shell and facilitates sliding along obstacles. This simplifies the formalization of the control strategy, which does not require a model of the interaction with the environment, nor high-level switching conditions for alternating between pushing and sliding. We utilize an optimization-based controller that ensures safety constraints on the robot’s state and dampens the oscillations of the environment during interaction, even if the elastic response is unknown and variable. Experimental evaluation, using a hinged surface with three different stiffness values ranging from 18 to 155.5 N mm rad−1, validates the proposed embodied aerial physical interaction strategy. By also showcasing the traversal of isolated branches, this work makes an initial contribution toward enabling drone flight across cluttered vegetation, with potential applications in environmental monitoring, precision agriculture, and search and rescue.

Livestock and climate change frames and interaction strategies in East Africa: exploring tensions between adaptation and mitigation options

ABSTRACT East African livestock systems, which support many livelihoods, are suffering from climate change but also contribute a large portion of national greenhouse gas emissions. There are various ways to frame livestock and climate change problems and solutions. We use data from interviews, policy documents and participant observations of science-policy interfaces in Ethiopia, Kenya and Uganda to answer: (1) How do frames used by scientists and policymakers affect discussions about climate change and livestock keeping in East Africa? and (2) What framing interaction strategies are employed to deal with ambiguity in science-policy interfaces? Findings show emphasis is given to framings describing livestock and climate change problems and less to response framings. While adaptation and mitigation are both used as issue frames in general discussions, funding availability to address climate issues draws attention to the need for measurement, reporting and verification systems, leading to more concrete discussions on mitigation-related response options and less attention on adaptation. Actors use different interactional framing strategies to co-construct meaning around problems and response options. The findings highlight the need for governments and partners to co-create knowledge on how livestock interventions can address adaptation and mitigation simultaneously to move away from the adaptation-mitigation divide in response framings.

Strategi Guru dalam Meningkatkan Kemampuan Berbicara Bahasa Arab di Wilayah Al Mawaddah Pondok Pesantren Nurul Jadid

The purpose of this article is to find out the strategies used by teachers to improve Arabic speaking skills in the Arabic Language Development Program of Al Mawaddah area of Nurul Jadid Islamic Boarding School. This research is a qualitative descriptive research. Two data collection methods were used: observation and interview. The data sources studied were teachers, students, and various literatures relevant to the research object. A descriptive-analytic model was used for the data analysis. The results showed that Arabic teachers in the Al Mawaddah area of Nurul Jadid Islamic Boarding School used interactive and communicative strategies. The communicative strategy is carried out by requiring students to participate proactively in learning speaking and speaking skills. The communicative strategy requires students to understand and use communication directly from the language structure. With these two strategies, students become more communicative and better at speaking Arabic.

TETRIS: Towards Exploring the Robustness of Interactive Segmentation

Interactive segmentation methods rely on user inputs to iteratively update the selection mask. A click specifying the object of interest is arguably the most simple and intuitive interaction type, and thereby the most common choice for interactive segmentation. However, user clicking patterns in the interactive segmentation context remain unexplored. Accordingly, interactive segmentation evaluation strategies rely more on intuition and common sense rather than empirical studies (e.g., assuming that users tend to click in the center of the area with the largest error). In this work, we conduct a real-user study to investigate real user clicking patterns. This study reveals that the intuitive assumption made in the common evaluation strategy may not hold. As a result, interactive segmentation models may show high scores in the standard benchmarks, but it does not imply that they would perform well in a real world scenario. To assess the applicability of interactive segmentation methods, we propose a novel evaluation strategy providing a more comprehensive analysis of a model's performance. To this end, we propose a methodology for finding extreme user inputs by a direct optimization in a white-box adversarial attack on the interactive segmentation model. Based on the performance with such adversarial user inputs, we assess the robustness of interactive segmentation models w.r.t click positions. Besides, we introduce a novel benchmark for measuring the robustness of interactive segmentation, and report the results of an extensive evaluation of dozens of models.

Modeling Adaptive Inter-Task Feature Interactions via Sentiment-Aware Contrastive Learning for Joint Aspect-Sentiment Prediction

Aspect prediction (AP) and sentiment prediction (SP) are representative applications in fine-grained sentiment anal- ysis. They can be considered as sequential tasks, where AP identifies mentioned aspects in a sentence, and SP infers fine-grained sentiments for these aspects. Recent models perform the aspect-sentiment prediction in a joint man-ner, but heavily rely on the feature interactions of aspect and sentiment. One drawback is that they ignore correlation strength varies between aspect features and sentiment fea- tures across different sentences, and employ a fixed feature interaction strategy may limit effective knowledge transfer across tasks. To tackle this issue, in this paper, we propose an Adaptive Inter-task Feature Interaction framework, AIFI, for joint aspect-sentiment prediction. Specifically, we introduce a novel contrast-based alignment method based on contrastive learning. Our approach considers the AP-specific and SP-specific representations of a given sentence as a positive pair, while representation of another random sentence serves as a negative example. Moreover, we propose an inter-task feature correlation network to predict the contrast strength, which is determined by the temperature coefficient in the InfoNCE loss. This dynamic correlation adjustment enhances model’s ability to capture proper feature interactions more efficiently. Experimental results on three datasets validate the effectiveness of our approach.

Dependency Structure-Enhanced Graph Attention Networks for Event Detection

Existing models on event detection share three-fold limitations, including (1) insufficient consideration of the structures between dependency relations, (2) limited exploration of the directed-edge semantics, and (3) issues in strengthening the event core arguments. To tackle these problems, we propose a dependency structure-enhanced event detection framework. In addition to the traditional token dependency parsing tree, denoted as TDG, our model considers the dependency edges in it as new nodes and constructs a dependency relation graph (DRG). DRG allows the embedding representations of dependency relations to be updated as nodes rather than edges in a graph neural network. Moreover, the levels of core argument nodes in the two graphs are adjusted by dependency relation types in TDG to enhance their status. Subsequently, the two graphs are further encoded and jointly trained in graph attention networks (GAT). Importantly, we design an interaction strategy of node embedding for the two graphs and refine the attention coefficient computational method to encode the semantic meaning of directed edges. Extensive experiments are conducted to validate the effectiveness of our method, and the results confirm its superiority over the state-of-the-art baselines. Our model outperforms the best benchmark with the F1 score increased by 3.5 and 3.4 percentage points on ACE2005 English and Chinese corpus.

Collective Cell Radial Ordered Migration in Spatial Confinement.

Collective cells, a typical active matter system, exhibit complex coordinated behaviors fundamental for various developmental and physiological processes. The present work discovers a collective radial ordered migration behavior of NIH3T3 fibroblasts that depends on persistent top-down regulation with 2D spatial confinement. Remarkably, individual cells move in a weak-oriented, diffusive-like rather than strong-oriented ballistic manner. Despite this, the collective movement is spatiotemporal heterogeneous and radial ordering at supracellular scale, manifesting as a radial ordered wavefront originated from the boundary and propagated toward the center of pattern. Combining bottom-up cell-to-extracellular matrix (ECM) interaction strategy, numerical simulations based on a developed mechanical model well reproduce and explain above observations. The model further predicts the independence of geometric features on this ordering behavior, which is validated by experiments. These results together indicate such radial ordered collective migration is ascribed to the couple of top-down regulation with spatial restriction and bottom-up cellular endogenousnature.

Shaping ideologies at the micro level: Stylization in EMI

Purpose: Research on bi/multilingual oral interactions in English-medium instruction (EMI) is often concerned with how languages the teacher and/or students speak can be used for pedagogical scaffolding processes. In contrast, this paper aims to investigate how stylization of languages, including those that teacher and students only speak minimally, can likewise be harnessed for important pedagogical and interpersonal purposes. Methodology: I conduct an interactional sociolinguistic analysis of transcripts from an EMI teaching simulation in a Master’s in Education course at a university in Hong Kong. Data and analysis: As a cross-border teacher from Cantonese-speaking Guangdong and a local teacher of Indian heritage engaged in these language stylization processes, they show that teachers can effectively use bi/multilingual stylization in EMI with attention to student uptake and response. Findings/Conclusions: While pedagogical scaffolding is important in EMI, these processes can put more and less societally dominant languages in a diglossic, unequal relationship. In contrast, stylization can reconfigure classroom language norms and hierarchies, attach positive meanings to unratified codes, and allow teachers to share linguistic authority with students. Originality: Little research to date has studied stylization as a pedagogical resource in EMI, focusing instead on the use of languages teachers/students already know for scaffolding. Significance/Implications: Teacher professional development should address the topic of how to use language stylization in EMI, especially since these interactional strategies do not require teachers to dramatically reconfigure curriculum or instruction, yet have pedagogical and interpersonal benefits. Limitations: Future research should aim to study stylization in EMI in an authentic classroom setting.

Диалог как приоритетная стратегия межкультурного взаимодействия в условиях новых ориентиров

The article considers the actual problems of the dialogue of cultures in the context of contradictory socio-cultural processes. Based on the study of the modern cultural situation, it is concluded that dialogue is the only possible way of interaction between cultures of the world space. As an example, the author cites Russia, which historically has a desire for dialogue with communities and cultures of the Eurasian space. The transformation-al processes taking place in the modern world have determined the inclusion of Russia in the format of Eura-sian intercultural cooperation. In this article, the author sets the task to consider the initial attitudes of the study of intercultural dialogue, to determine its practical potential in establishing intercultural interaction with the countries of the eastern continent; to show that in the context of a steady trend towards Eurasian cooperation between different cultures as independent entities acting from their own positions, dialogue becomes a promis-ing model of intercultural interaction. The scientific novelty consists in the following: the importance of intercul-tural dialogue for mutual understanding between Eastern and Russian cultures is explicated; intercultural dia-logue is analyzed as a priority strategy for intercultural interaction in the context of the Eurasian partnership; it is shown that the Eurasian peoples, which differ significantly from each other, have common values that have developed over a long period of joint existence. Being a phenomenon unique in its depth, dialogue is able to stimulate the optimal way of cultural interaction between Russia and the East in the context of the “change of epochs” and the search for a new spiritual paradigm.

Self‐suasion: agents of Jewish conversion in Israel in search of religious sincerity

AbstractThe title of this essay is intended as ironic. The irony resides in the fact that while the agents of state‐run Jewish conversion in Israel are preoccupied with the sincerity of conversion candidates, they are also troubled by the sincerity of their own religious belief and conduct. This essay will explore ethnographically how these religiopolitical actors engage with semiotic and interactive strategies of self‐suasion in order to reconcile their Zionist and religious commitments with the moral toll that they pay for knowingly facilitating less‐than‐ideal conversions. The essay demonstrates that while these actors embody the Israeli state's national missionary impetus to convert newcomers, they rely on a variety of communicative practices that help them project the constructed sincerity of converts onto their own self‐suaded religious sincerity.

PtZn/ZnO intermetallic catalyst prepared by reactive metal-support interaction for the semi-hydrogenation of 1,4-butynediol

The continuous flow selective hydrogenation of 1,4-butynediol to 2-butene-1,4-diol places high demands on the activity and stability of the catalyst. Herein, an intermetallic PtZn/ZnO catalyst is designed by using the coupling strategy of strong electrostatic adsorption and subsequent reactive metal-support interaction. The TOF value of the Pt/ZnO-H500 catalyst, including PtZn intermetallic compound with electron-rich Ptδ- active sites, reaches 1421.6 h−1, which is almost 8 times higher than that of Pt/ZnO-H200 catalyst with monometallic Pt as active sites. The activation energy of Pt/ZnO-H500 catalyst (35.5 kJ mol−1) is significantly lower than that of Pt/ZnO-H200 catalyst with 49.9 kJ mol−1. In addition, the Pt/ZnO-H500 presents high stability in the continuous flow selective hydrogenation of 1,4-butynediol to 2-butene-1,4-diol for 75 h. These results suggest the intermetallic PtZn/ZnO presents as an alternative Pt-based catalyst without modification by toxic metals or organic matter and is a promising catalyst for directional hydrogenation in fine chemical synthesis.

Factors Driving Consumer Impulse Buying On Fashion Products at Tiktok Live

The TikTok live-streaming commerce trend is swiftly gaining popularity in Indonesia, with numerous local fashion brands effectively capitalizing on this opportunity to boost their sales within a short timeframe. Previous research suggests that impulsive buying behavior serves as the underlying factor for this phenomenon. Hence, this research was carried out to investigate consumer behavior in live-streaming commerce, aiming to assist small-scale fashion businesses in enhancing their sales. The Stimulus Organism Response (SOR) Model uses price promotion, promotion time limit, streamer-consumer interaction, and consumer-consumer interaction to influence the consumer’ impulsive buying by perceived risk. The quantitative approach collected 445 data from Indonesia TikTok active users who have watched a fashion broadcast and have purchased during the broadcast. The result found that perceived risk significantly affects consumer impulsive buying behavior. The price promotion, promotion time limit, and consumer-consumer interaction were found to have a strong influence on perceived risk. Then, perceived risk-mediated promotion time limit and impulsive buying decision. It means that promotional time limits indirectly affect impulsive buying decisions. Thus, developing effective price promotion, promotion time limit, and consumer-consumer interaction strategy to drive their perceived risk making a purchase, and arrange an impactful promotion time limit tactic.