Social Scenarios Research Articles

Assisting Multimodal Named Entity Recognition by cross-modal auxiliary tasks

Although the existing Multimodal Named Entity Recognition (MNER) methods have achieved promising performance, they suffer from the following drawbacks in social media scenarios. Firstly, most existing methods are based on a strong assumption that the textual content and the associated images are matched, which is not always valid in real scenarios; Secondly, current methods fail to filter out modality-specific random noise, which impedes models from exploiting modality-shared features. In this paper, a novel multi-task multimodal learning architecture is put forward, which aims to improve Multimodal Named Entity Recognition (MNER) performance by cross-modal auxiliary tasks (CMAT). Specifically, we first separate the shared and task-specific features for the main task and auxiliary tasks respectively, which is accomplished by cross-modal gate-control mechanism. Subsequently, without extra pre-processing or annotations, we utilize the cross-modal matching to address the issue of mismatched image-text pairs, and the cross-modal mutual information maximization to optimize the most relevant cross-modal features. Moreover, experimental results on the two widely used datasets confirm the superiority of our proposed approach.

Improving the Effectiveness of Eigentrust in Computing the Reputation of Social Agents in Presence of Collusion.

The introduction of trust-based approaches in social scenarios modeled as multi-agent systems (MAS) has been recognized as a valid solution to improve the effectiveness of these communities. In fact, they make interactions taking place in social scenarios much fruitful as possible, limiting or even avoiding malicious or fraudulent behaviors, including collusion. This is also the case of multi-layered neural networks (NN), which can face limited, incomplete, misleading, controversial or noisy datasets, produced by untrustworthy agents. Many strategies to deal with malicious agents in social networks have been proposed in the literature. One of the most effective is represented by Eigentrust, often adopted as a benchmark. It can be seen as a variation of PageRank, an algorithm for determining result rankings used by search engines like Google. Moreover, Eigentrust can also be viewed as a linear neural network whose architecture is represented by the graph of Web pages. A major drawback of Eigentrust is that it uses some additional information about agents that can be a priori considered particularly trustworthy, rewarding them in terms of reputation, while the non pre-trusted agents are penalized. In this paper, we propose a different strategy to detect malicious agents which does not modify the real reputation values of the honest ones. We introduce a measure of effectiveness when computing reputation in presence of malicious agents. Moreover, we define a metric of error useful to quantitatively determine how much an algorithm for the identification of malicious agents modifies the reputation scores of the honest ones. We have performed an experimental campaign of mathematical simulations on a dynamic multi-agent environment. The obtained results show that our method is more effective than Eigentrust in determining reputation values, presenting an error which is about a thousand times lower than the error produced by Eigentrust on medium-sized social networks.

Agricultural -News Visibility in Indian Newspapers: A Comparative Study

The coverage of agricultural news is of great significance in the Indian economic and social scenario. The present study explores the print visibility of agricultural news in three newspapers in India. Therefore, research was accomplished through a random selection of three national- newspapers, Hindi(one) and English (two), namely Dainik Jagran, The Hindu, and The Hindustan Times. The agricultural news in the three newspapers was collected from December 2020 to November 2021 and categorized into political news, advisories, policy, techno-innovation, entrepreneurship, and environmental. The articles were extracted from E- Papers and print PDFs for practical purposes based on keyword selection. The findings revealed that the agricultural news covered by the newspapers was feeble and under 5 % of the total news content. The total number of agricultural articles found from December 2020 to November 2021 was 6191. However, Dainik Jagran led in print visibility with a yearly average of 4.71 per cent, followed by The Hindustan Times at 4.30 percent and The Hindu at 3.23 percent. The print visibility ranged from 8.0 percent to a maximum, and the minimum was 1.8 percent. It was also found that Agri-political news had maximum visibility in two of the three newspapers (68.3 percent in the Hindu and 33 percent in Dainik Jagran).

On Affect, Violence, Vulnerability, and Community: Jenisjoplin

Jenisjoplin (2017) by Uxue Alberdi is an unsettling and formally innovative novel, as it recalls—through the account by its protagonist Nagore Vargas, nicknamed Jenisjoplin—the convulsive socio-historical reality of the last four decades in the Basque Country. This article offers a contextualization of Alberdi’s novel in the landscape of contemporary Basque authors and bases its analysis on theories connected with the affective turn. It is within that theoretical framework that such notions as violence experienced at the social level in ETA and post-ETA times, illness and vulnerability, and the possibility of individual and social reconstruction are explored. The narrative discourse in Jenisjoplin intertwines the social and political scenario of violence with the subjective experiences of the protagonist, which focus on family, friends, lovers, and illness, but also with frequent analytical and critical reflections on the nature of the Basque community and on the fight for national independence, the medical diagnosis and treatment for AIDS, and the struggle for a society where social justice will eventually prevail. Contributions by Mari Jose Olaziregi, Sara Ahmed, Laurent Berlant, Judith Butler, Susan Sontag, and Roberto Esposito, among others, form the theoretical backdrop for the analysis of the novel.

Gateway-based Interoperability for Distributed Ledger Technology

Blockchain is a distributed ledger technology (DLT) to manage data in a decentralised way. During the last years, interoperability has become one of the main challenges within blockchain research as blockchains increasingly require integration between each other. Indeed, blockchains work by design in silos of information as interoperability is not a native feature. The main efforts in the field are focused on blockchains, such as Bitcoin and Ethereum. However, interoperability in DLT remains as an almost untouched area of work as they introduce additional requirements focusing on privacy and identity. Although there are some interoperability solutions for DLT, they are either high-level design proposals not providing concrete implementations or focus on interoperability issues between business applications and blockchain platforms. In this paper we propose a gateway-based platform-to-platform interoperability solution for DLT, which comprises a detailed solution design and a reference implementation. The proposal was assessed through the development of a social security case scenario and the evaluation through two interoperability frameworks. A reference implementation was built using two DLT: Hyperledger Fabric and Corda. The experimental results shows that it is possible to achieve technical interoperability between two heterogeneous DLT platforms using a gateway-based interoperability solution, relaxing decentralisation, data privacy, identity and authorisation management properties.

Give Me a Straight Answer: Response Ambiguity Diminishes Likability.

Across nine experiments (eight preregistered) involving Western and Asian samples, we showed that people providing ambiguous (vs. specific) responses to questions in various social scenarios are seen as less likable. This is because, depending on the social context, response ambiguity may be interpreted as a way to conceal the truth and as a sign of social disinterest. Consequently, people reported lower inclination to befriend or date individuals who appeared to provide ambiguous responses. We also identified situations in which response ambiguity does not harm likability, such as when the questions are sensitive and the responder may need to "soften the blow." A final exploratory study showed that response ambiguity also impacts personality perceptions-individuals providing ambiguous responses are judged as less warm, less extraverted, less gullible, and more cautious. We discuss theoretical implications for the language psychology and person perception literatures and practical implications for impression management and formation.

Multi-view social recommendation via matrix factorization with sub-linear convergence rate

Recently Recommender Systems (RS) have become crucial tools to deal with information retrieval and filtering in various applications, such as online business, social network and so on. However, with the ever-increasing scale of social network and information overload, RS still suffer from data sparsity and heavy computational complexity, and even confront a serious challenge to further improve the recommender performance and user experience. To this end, this article propose a novel multi-view social recommendation scenario referred to as MsRec, which takes account of available information for item recommendation from multiple perspectives. More specifically, MsRec tries to exploit intricate inner relationship within social network, and perform user-level preference learning for each user without overlapping, moreover, MsRec leverages available side information, such as contextual information, demographic characteristics and item attributes, to perform representation vector learning for items, which will be further utilized for item similarity calculation via Cosine method. With the learned social influence and item similarity coefficient, MsRec could perform representation vector learning for users and items, and further provide rating prediction and item recommendation. In addition, theoretical analysis indicates that MsRec could achieve convergence with sub-linear convergence rate during model training phase, and yield high performance in social recommendation, without heavy computational cost. Experimental analysis over Yelp, Epinions and DoubanMovie also demonstrate the superiority of the proposed MsRec, which can achieve significant improvements while compared to other benchmark recommender algorithms, and provide much better user experience.

Delay to Deal: Bargaining with Indivisibility and Round-Dependent Transfer

We examine a bargaining game in which players cannot make arbitrary offers. Instead, players alternately decide whether to accept or delay, and are rewarded with an indivisible portion and a perishable transfer that depends on the round. Our analysis demonstrates that when the initial transfer is large enough, the subgame perfect Nash equilibrium consists of a finite number of rounds of delay before an agreement is reached. The equilibrium delay is longer when the players are more patient, and when the transfer is initially higher and depreciates slower. Nevertheless, the game’s chaotic characteristic makes it arduous to forecast the exact number of delayed rounds or which player will make the ultimate decision. This game can be applied to many social scenarios, particularly those with exogenous costs.

How convenience and informational tools shape waste separation behavior: A social network approach

Geopolitical risks such as energy crises and local or regional instability aggravate the growing conflict between limited natural resources and increasing consumption, making waste management—using waste as a resource—one of the most urgent agendas in the sustainability domain. Considering individuals' dual attributes (i.e., attitudes and behaviors), we develop an interaction model to investigate waste separation behavior that accounts for convenience and informational tools. Multiagent simulations are performed using data collected from 1056 residents in the top ten Chinese cities by volume of household waste. The results indicate that convenience tools can induce residents to separate waste. In particular, a 160% increase in the adoption rate is observed in response to decreasing (increasing) the distance to collection sites (accessibility to temporary storage facilities for recyclables) from 10 (0) to 0 (10). Publicity and education promote waste separation, as approximately 72% of individuals choose to separate waste when increasing the intensity of popular science propaganda from 0 to 0.2. Interestingly, social interaction appears to have a two-sided effect on waste separation behavior. The adoption rate of such behavior increases with the number of one's connections in sparse social networks, whereas the opposite is found in close social networks. The findings reveal the differentiated impacts of convenience and informational tools on waste minimization and provide new ideas for formulating customized strategies to promote waste separation in sparse (increasing individuals' number of connections) or close (monitoring rumors to achieve a tightly controlled intensity of interaction) social network scenarios.

Investigating the dynamic decoupling relationship between regional social economy and lake water environment: The application of DPSIR-Extended Tapio decoupling model

The water environmental problems associated with rapid socioeconomic growth have drawn widespread attention from the government and the public. Revealing the decoupling mechanism between the social economy and lake water environment has become an important breakthrough point to seek the pathways of sustainable economic development. To investigate the decoupling process of the social economy‒lake water environmental system, this study proposes a comprehensive evaluation model, which integrates the Driving force-Pressure-State-Impact-Response (DPSIR) model, projection pursuit method, and Tapio decoupling model; and then applies it to the case study of Hefei City and Lake Chaohu in China in 2021–2035. Three typical scenarios of current, social economy, and water environment are designed and simulated using the DPSIR model to evaluate the dynamic decoupling relationships under various development patterns. We found that the DPSIR indexes had a fluctuating upward trend from 2009 to 2020, with a synchronous improvement trend of the social economy and lake water environment. Meanwhile, the Tapio decoupling analysis showed that the decoupling relationships between socioeconomic driver forces, response strategies and the status of lake water environment was mostly strongly decoupled and weakly decoupled during 2009–2020. However, there was still an inconsistency between the improvement rate of the lake water environment and the increase rate of the response strategies. During the 2021–2035 simulation period, the DPSIR indexes of all scenarios depicts an overall increasing trend. The decoupling states of S&I-D&P and S&I–R generally tend to be consistent under three regulation scenarios. Among them, the water environment scenario outperforms other scenarios, and the social economy scenario performs worst. Overall, the decoupling of the social economy and lake water environment can attribute to both the transformation of socioeconomic development patterns and the increase of water environmental protection efforts.

Strengthening Organisational Resilience Through Knowledge Management: The Santa Catarina´s Civil Defense Case

The constant changes in the political, social, and economic scenarios, as well as recent major crises and natural disasters that have affected Santa Catarina, Brazil, and the world, have increased the demand for organisations to provide more resilient responses. Civil Defence organisations are no exception, and they must prioritise the identification and management of critical knowledge to enhance their organisational resilience. In light of this, a case study was carried out in Santa Catarina´s Civil Defence (SCCD), a public government organisation that plays a critical role in the territorial resilience of SC state. The study aimed to evaluate SCCD’s organisational resilience and develop a continuous improvement plan based on critical knowledge. To achieve this, the Resilience Analysis Grid (RAG) was used as a tool to characterise the SCCD’s resilience potential. The analysis showed that the organisation has an appropriate level of resilience potential. However, to identify ways to enhance this potential, it was necessary to analyse critical knowledge for monitoring, anticipating and responding to unexpected events, as well as exploring how SCCD promotes learning in the face of new situations. To do so, a framework was used to analyse critical knowledge to organisational resilience capabilities. This framework was used to present an improvement plan for the organisation's resilience potential, based on the use of techniques and methods for knowledge management. The plan recommended several practical steps that could be taken to enhance the resilience of the SCCD, including the creation of a knowledge management system, the development of training programmes, and the identification and management of critical knowledge. In conclusion, the study highlighted the importance of critical knowledge in enhancing organisational resilience and demonstrated that a continuous improvement plan based on critical knowledge could lead to significant improvements in the resilience of organisations like the SCCD. By following the study's recommendations, organisations can become better equipped to respond to future challenges and maintain their resilience in the face of change and uncertainty.

Developing sustainable models of agroforest-landscape in Bogor, Indonesia

Abstract. Nurrochmat NA, Purnomo H, Muhdin, Erbaugh JT. 2023. Developing sustainable models of agroforest-landscape in Bogor, Indonesia. Biodiversitas 24: 4324-4333. Deforestation continues in Indonesia even though many regulations and precautions have been taken. It most likely happened due to the less economic contribution from the forestry sector, which caused the conversion from forest lands to more profitable land uses. The forest area covers 125 million hectares (66%) of 190 million hectares of country land but contributes only about 0.60% of the Gross Domestic Product (GDP). An agroforestry scheme is promoted to increase the economic value of the forest to increase both the community welfare and the environmental quality. This study indicates that the best agroforestry commodities for social scenarios are durian, ornamental plants, and laying hens, with the highest potential profit of IDR.143,857,559 (USD.9,652)/hectare/month and 9.19 tons/hectare carbon stock. While for the economic scenario is jackfruit, ornamental plants, and laying hens with a potential profit of IDR.146,908,379 (USD.9,789)/hectare/month and 18.66 tons/hectare carbon stock. In comparison, the ecology scenario promotes nutmeg and ornamental plants with a potential profit of IDR 36,814,280 (USD.2,470)/hectare/month, with the highest of 56.12 tons/hectare carbon stock. This study recommends the appropriate land use models for the government to address the conflict of interest between agriculture and forestry in contributing to Indonesia's FoLU (Forestry and other Land Uses) Net Sink in 2030.

Shared Perception Is Different From Individual Perception: A New Look on Context Dependency

Human perception is based on unconscious inference, where sensory input integrates with prior information. This phenomenon, known as context dependency, helps in facing the uncertainty of the external world with predictions built upon previous experience. On the other hand, human perceptual processes are inherently shaped by social interactions. However, how the mechanisms of context dependency are affected is to date unknown. If using previous experience - priors - is beneficial in individual settings, it could represent a problem in social scenarios where other agents might not have the same priors, causing a perceptual misalignment on the shared environment. The present study addresses this question. We studied context dependency in an interactive setting with a humanoid robot iCub that acted as a stimuli demonstrator. Participants reproduced the lengths shown by the robot in two conditions: one with iCub behaving socially and another with iCub acting as a mechanical arm. The different behavior of the robot significantly affected the use of prior in perception. Moreover, the social robot positively impacted perceptual performances by enhancing accuracy and reducing participants overall perceptual errors. Finally, the observed phenomenon has been modelled following a Bayesian approach to deepen and explore a new concept of shared perception.

Emotion regulation ofsocial pain: double dissociation oflateral prefrontal cortices supporting reappraisal anddistraction.

The dorsolateral prefrontal cortex (DLPFC) and ventrolateral prefrontal cortex (VLPFC) are both crucial regions involved in voluntary emotion regulation. However, it remains unclear whether the two regions show functional specificity for reappraisal and distraction. This study employed transcranial magnetic stimulation (TMS) to explore, in a real social interactive scenario, whether different lateral prefrontal regions play relatively specific roles in downregulating social pain via reappraisal and distraction. Participants initially took part in a social interactive game, followed by receiving either active (the DLPFC- or VLPFC-activated group, n = 100 per group) or control (the vertex-activated group, n = 100) TMS session. They were then instructed to use both distraction and reappraisal strategies to downregulate any negative emotions evoked by the social evaluation given by their peers who interacted with them previously. Results demonstrated that the TMS-activated DLPFC has a greater beneficial effect during distraction, whereas the activated VLPFC has a greater beneficial effect during reappraisal. This result investigated the direct experience of social pain and extended previous findings on empathy-related responses to affective pictures while also controlling for confounding factors such as empathic concern. Therefore, we are now confident in the double dissociation proposal of the DLPFC and VLPFC in distraction and reappraisal.

Integrating Cognitive-Behavioral Therapy With Compassion-Focused Therapy for the Treatment of Social Anxiety Disorder: An Evidence-Based Case Study

Cognitive-behavioral therapy (CBT) is an evidence-based treatment for social anxiety disorder that has been found to elicit significant changes in process-based mechanisms (e.g., emotion regulation strategies), which subsequently lead to reductions in social anxiety and associated functional impairment; however, CBT may be less effective for socially anxious individuals experiencing high self-criticism and low self-compassion. Compassion-focused therapy (CFT) has been used to address these tendencies in socially anxious individuals, but research in this area is limited. The present case study examined changes in social anxiety symptoms, self-compassion, emotion regulation strategies, and functional impairment throughout the course of treatment with a young, Chinese American woman using a novel integration of CBT and CFT for social anxiety disorder. The client experienced significant reductions in social anxiety and functional impairment across treatment, which was accompanied by reductions in fear and avoidance ratings for feared social scenarios. Significant mechanistic changes were also observed, including decreases in maladaptive emotion regulation strategies (i.e., suppression, rumination) and increases in self-compassion and adaptive emotion regulation strategies (i.e., expressive engagement, cognitive reappraisal). Findings support the efficacy of integrating compassion-focused strategies with CBT for social anxiety for a young adult with prominent tendencies of self-criticism and low self-compassion.

‘Orange Is The New Black’: discursive disputes over racial identity

The subject of this work is the TV series Orange is The New Black. We understand this media product as a social construction, permeated, therefore, by interactions and tensions present in society. We mobilize as a reference point Mediaculturas, which refuses to attribute to the media an essential, pre-established function of the kind of alienation. The questions that guided the investigation are as follows: What discursive disputes appear in the content of the series? What lines of discourse about race are appropriate for the show and what meanings do they have? The objectives to be achieved are: to analyze the content of the program looking for traits of reproduction and innovation of the lines of discourse regarding the racial issue in the USA; to observe the tensions, contradictions and negotiations in the appropriation of these contents; to identify the type of representation of Afro-descendants and of the racial question in the fifth season of the show; to verify the existence, or not, of proposals for normalization of behaviors and solution of social conflicts within the language and the television format; to observe if the propositions present in the show reflect the political and social scenario regarding the racial question in the USA.

Multi-scenario prediction and path optimization of industrial carbon unlocking in China

China's industry is facing a carbon lock-in (CLI) dilemma. Based on the complexity and dynamics of CLI, exploring diversified unlocking paths is an important way to achieve emission reduction goals. This paper innovatively applies system dynamics (SD) to the study of CLI prediction and unlocking path, and systematically constructs an industrial CLI SD model, trying to break the positive feedback mechanism of CLI through multiple causality and feedback chains. This paper simulates and predicts the changing trend of the CLI level of China's industry and 8 major subsectors from 2020 to 2050. At the same time, the random forest algorithm is used to select the regulatory variables of policy analysis. Compared with the previous research methods, the accuracy and objectivity of the selection of regulatory variables are improved, so as to test the effectiveness of the unlocking path under each scenario. The results show that (1) the whole industry faces a serious CLI problem and is still in a deep lock-in state. From the perspective of subsectors, the light, electromechanical, and textile industries have achieved unlocking; the oil and extractive industries are in a moderately lock-in state; the chemical, steel, and power industries are in a deep lock-in state; and the CLI situation is serious. (2) The results of the random forest algorithm show that R&D investment, the proportion of fixed asset investments in the energy industry, car sales, energy structure, and carbon emission industry structure are the most significant factors affecting CLI in each subsystem. (3) Compared with the existing economic development model, the technological, institutional, social, and comprehensive unlocking scenarios can all enable industry to achieve carbon peak and carbon unlocking in advance. However, the effect of each scenario is as follows: comprehensive unlocking scenario > technological unlocking scenario > institutional unlocking scenario > social unlocking scenario. (4) The unlocking times of the oil, chemical, steel, and power industries are relatively late in each scenario. To realize carbon unlocking in industry, these four areas should be focused on in the future. This study helps policymakers formulate reasonable policies to accelerate industrial carbon unlocking and promote the implementation of the “dual carbon” strategy.

Negative interpretation bias in females with bulimia nervosa

AbstractPersons with bulimia nervosa (BN) often experience psychosocial difficulties, in particular heightened sensitivity to social rejection and a negative bias toward their social environment. Conversely, social competence and close friendships are protective against mental ill health. The aims of this study were to evaluate the interpretation of ambiguous social scenarios in females with and without BN and to assess the relationship between interpretation biases and clinical characteristics. Females with BN (n = 35) and controls (n = 35) were recruited via social media. Participants completed the Eating Disorder Examination Questionnaire (EDE‐Q), Adult Rejection Sensitivity Questionnaire, the Depression Anxiety and Stress Scales (DASS), and finished sentence stems depicting ambiguous social scenarios. Completed sentence stems were rated as positive, neutral, or negative by blinded researchers. Females with BN made fewer positive and more negative interpretations of sentence stems than controls. The frequency of negative interpretations correlated positively with clinical symptoms on the EDE‐Q, A‐RSQ, and DASS. A negative interpretation bias was found in females with BN, which aligns with the finding shown by Cardi et al. that females with anorexia nervosa have a negative interpretation bias toward ambiguous social scenarios. This bias was not only associated with eating disorder psychopathology but also with depression, anxiety, and stress, highlighting a potential transdiagnostic role. Interventions that address psychosocial difficulties might prevent the onset, reduce symptoms, and improve prognosis.

Training work-related social skills in adults with Autism Spectrum Disorder using a tablet-based intervention

ABSTRACT While multiple studies suggest that social skills are considered as essential in promoting employment, literature highlights a deficit in these skills among workers with Autism Spectrum Disorder (ASD) for whom difficulties in communication and social interaction are often reported. In this article, we explain how we used a User-Centered Design to identify and define in detail the work-related social skills that need to be trained. The aim was to develop social scenarios to train work-related social skills relevant for people with ASD using a digital app on a tablet. Participatory Design in our study included a needs analysis with questionnaires and interviews, as well as focus-groups and workshops with professionals of several disability employment services and workers or job seekers with ASD. Sixty-six social scenarios were written and 28 social skills were identified as relevant. This approach allowed us to design a tablet-based intervention which is an intelligent system by design since its content and structure target the social skills which require training for users with ASD.

The pictorial set of Emotional Social Interactive Scenarios between Chinese Adults (ESISCA): Development and validation.

Affective picture databases with a single facial expression or body posture in one image have been widely applied to investigate emotion. However, to date, there was no standardized database containing the stimuli which involve multiple emotional signals in social interactive scenarios. The current study thus developed a pictorial set comprising 274 images depicting two Chinese adults' interactive scenarios conveying emotions of happiness, anger, sadness, fear, disgust, and neutral. The data of the valence and arousal ratings of the scenes and the emotional categories of the scenes and the faces in the images were provided in the present study. Analyses of the data collected from 70 undergraduate students suggested high reliabilities of the valence and arousal ratings of the scenes and high judgmental agreements in categorizing the scene and facial emotions. The findings suggested that the present dataset is well constructed and could be useful for future studies to investigate the emotion recognition or empathy in social interactions in both healthy and clinical (e.g., ASD) populations.