Reasonable Action Research Articles

Introduction to latent variable energy-based models: a path toward autonomous machine intelligence

Abstract Current automated systems have crucial limitations that need to be addressed before artificial intelligence can reach human-like levels and bring new technological revolutions. Among others, our societies still lack level-5 self-driving cars, domestic robots, and virtual assistants that learn reliable world models, reason, and plan complex action sequences. In these notes, we summarize the main ideas behind the architecture of autonomous intelligence of the future proposed by Yann LeCun. In particular, we introduce energy-based and latent variable models and combine their advantages in the building block of LeCun’s proposal, that is, in the hierarchical joint-embedding predictive architecture.

Storm in an Ice Cream Cone: Was Ben & Jerry's Decision to End Ice Cream Sales in Israeli Settlements a Responsible Corporate Exit from Occupied Territories?

Abstract Ben & Jerry's – the famous ice cream brand known for its quirky flavours and social justice ethos – announced in 2021 that it would withdraw its products from Israeli settlements in the Occupied Palestinian Territory (OPT). This announcement sparked controversy, with an impact on the company and its parent, Unilever. While the UN Guiding Principles on Business and Human Rights (2011) (UNGP) have established themselves as the leading international governance framework for the social responsibilities of businesses, their application to conflict-affected areas lacks clarity. Questions remain, including when is a company legally complicit in violations of international humanitarian law (IHL), and when is a corporate exit from an area under military occupation the appropriate and responsible thing to do. This article uses Ben & Jerry's withdrawal from the OPT as the basis for investigating these questions. It finds that while it is unlikely that Ben & Jerry's would be exposed to any legal liability for violating IHL through its product sales in Israeli settlements in the OPT, its withdrawal aligns with its responsibilities under the UNGP as a reasonable and prudent corporate action in response to its activities being linked to enduring and severe IHL violations.

The Value of Procedural Policy: Axiological and General Legal Aspects

This article examines the problems of identifying the values of procedural policy from theoretical and applied perspectives. Rooted in the general axiological principles of law, the values of procedural law guide law enforcement toward achieving positive outcomes by effectively resolving conflicts and disputes among the parties of legal procedures. It was shown that the values of procedural policy are best described by the axiological approach, which highlights its focus on meaningful, effective, rational, fair, and reasonable actions that align with the higher purpose and value of law. The conclusion was made that, in accordance with the strategies of legal axiology, procedural policy should be based on the doctrinal principles of legal science to enhance the efficiency of legal activities and uphold the value of lawmaking and law enforcement, including the administration of law, within today’s legal framework. The value of legal procedural policy was found to lie in setting new axiological standards, primarily associated with the dynamic development and use of new information technologies in legal practice. The value of jurisprudence in systematizing the legislative process and law enforcement was explored, thereby affirming the axiological nature of legal procedural policy.

Decentralized human-like control strategy of mixed-flow multi-vehicle interactions at uncontrolled intersections: A game-theoretic approach

A critical challenge that future autonomous driving systems face is improving the ability to cope with complex real-world interaction scenarios such as uncontrolled intersections. In the near future, a mixed traffic flow of human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs) will coexist in transport networks, which motivates us to explore the interaction between HDVs and CAVs to improve traffic efficiency and safety. To help CAVs better interact with HDVs and adapt to the mixed-flow environment, we propose a human-like decentralized control strategy for CAVs. First, a game-theoretic framework is proposed to model multi-vehicle interactions (including HDV-CAV, CAV-CAV interactions) in the mixed-flow environment. The existence of solutions is proven to ensure the feasibility of the proposed game-theoretic model. Next, a driving style recognition algorithm is embedded into the proposed model to help CAVs understand and predict human drivers’ actions. The proposed model is calibrated via a real-world dataset and used to simulate traffic in several testing scenarios. Real-world vehicle trajectories are used to verify the accuracy of generated vehicle trajectories in simulations. Experimental results indicate that 1) CAVs can take more reasonable actions to determine whether to yield while ensuring safety when competing for the right of way with HDVs using the proposed method compared with conservative driving strategies, 2) a higher penetration rate of CAVs can significantly enhance travel efficiency and lower collision risk at uncontrolled intersections.

Investigating Factors Influencing Consumer Intention to Use Online Streaming Services

This paper investigates factors influencing consumer intention to use online streaming services from the technology acceptance perspective based on the extended Technology Acceptance Model with additional variables – perceived enjoyment and incorporation with human and social constructs from Theory Reasoned Action. The consumer intention to use online streaming services remains unclear due to the gap between expectations of online streaming service providers and the high unsubscribe rate. This study deployed an online questionnaire to collect data from 250 Facebook users. Thus, the data collected for this study were from consumers not using online streaming services in Malaysia to understand the factors that could trigger their intention to use. The data collected were analysed using SPSS version 26 and PLS-SEM version 3.0. The role of self-efficacy as a moderator was examined to better understand consumer behaviour in online streaming services. The result revealed that subjective norms and perceived enjoyment have significantly impacted consumer intention to use online streaming services. Regarding the moderating effect of self-efficacy on the relationship between perceived ease of use and perceived usefulness, the outcome evidenced that self-efficacy plays a moderator role for both constructs. This paper attempts to fill the gap by extending the extant literature on consumer intention to use online streaming services by assessing the direct and moderating effects. Furthermore, the outcome of this study also provides valuable practical contributions for online streaming service providers by improving their understanding of consumer behaviour on the intention to use online streaming services.

Target-constrained Bidirectional Planning for Generation of Target-oriented Proactive Dialogue

Target-oriented proactive dialogue systems aim at leading conversations from a dialogue context toward a pre-determined target, such as making recommendations on designated items or introducing new specific topics. To this end, it is critical for such dialogue systems to plan reasonable actions to drive the conversation proactively, and meanwhile, to plan appropriate topics to move the conversation forward to the target topic smoothly. In this work, we mainly focus on effective dialogue planning for target-oriented dialogue generation. Inspired by decision-making theories in cognitive science, we propose a novel target-constrained bidirectional planning (TRIP) approach, which plans an appropriate dialogue path by looking ahead and looking back. By formulating the planning as a generation task, our TRIP bidirectionally generates a dialogue path consisting of a sequence of <action, topic> pairs using two Transformer decoders. They are expected to supervise each other and converge on consistent actions and topics by minimizing the decision gap and contrastive generation of targets. Moreover, we propose a target-constrained decoding algorithm with a bidirectional agreement to better control the planning process. Subsequently, we adopt the planned dialogue paths to guide dialogue generation in a pipeline manner, where we explore two variants: prompt-based generation and plan-controlled generation. Extensive experiments are conducted on two challenging dialogue datasets, which are re-purposed for exploring target-oriented dialogue. Our automatic and human evaluations demonstrate that the proposed methods significantly outperform various baseline models.

Вопросы добросовестности сторон договора по английскому праву в условиях санкций

The article is devoted to the problems of implementing the principle of good faith in English contract law. Now the importance of the principle is based on the fact that Russian companies expand the cooperation with companies from Africa, Asia, and Latin America. Usually, English contract law is applicable law between those parties. However, in the modern conditions of sanctions, there are many issues which don’t regulate in English law. Problems related to providing information to the counterparty, performing reasonable actions not expressly provided for in the contract, are not clearly resolved by English law. These questions are the parts of the good faith standard under private international law. In this paper, the author makes an attempt to find the ways to solve the stated issues.

Overview of knowledge reasoning for knowledge graph

Knowledge graphs are large-scale semantic networks that considerably impact knowledge representation. Mining hidden knowledge from existing data, including triplet knowledge reasoning, is a primary objective of knowledge graphs. With the development of Neural Network (NN) and Deep Learning (DL), the interpretability of triplet knowledge reasoning gradually decreases; furthermore, what machines learn is not actual reasoning but digital reasoning shortcuts. To solve this problem, more background knowledge needs to be introduced into knowledge graphs: causal graphs can offer valuable causal logic knowledge for reasoning; temporal quadruples can provide essential temporal distribution details; and commonsense graphs can furnish pertinent commonsense understanding to support reasoning. In recent years, many scholars have incorporated additional background knowledge into knowledge graphs to construct more complex reasoning mechanisms. This paper reviews the basic concepts and definitions of knowledge reasoning and the reasoning methods used for knowledge graphs. Specifically, we dissect the reasoning methods into four categories: triplet reasoning, causal inference, temporal inference, and commonsense reasoning. Finally, we discuss the remaining challenges and research opportunities related to knowledge graph reasoning.

The Older Adults' Intention to Use Silver Diamine Fluoride for Dental Caries Treatment.

This study aimed to determine the intention of older adults to use silver diamine fluoride (SDF) for treating tooth decay. A group of Thai-speaking individuals between the ages of 60 and 90 were asked to participate in the study. They were provided with information about SDF and then asked to complete a questionnaire based on the Theory of Reason Action (TRA). The questionnaire consisted of 23 questions divided into seven constructs aimed at determining the determinants of the intention to use SDF. These constructs were behavioral belief, evaluation of behavioral outcome, normative belief, motivation to comply, attitude towards behavior, subjective norm, and intention. The responses were rated on a 4-point Likert scale ranging from 1 (Strongly disagree) to 4 (Strongly agree). The characteristics and TRA scores of the participants were analyzed and compared based on their intention to use SDF, using the Chi-square test and t-test. Multiple logistic regression was employed to determine the determinants of the intention to use SDF. The study involved 588 participants, with an average age of 65.3 years (SD= 5.53). Of the participants, 52.7% were women, 58.1% were unemployed or retired, 80.8% lived with family, 63.4% had less than sixth-grade education, 62.9% had a monthly family income of less than 10,000 baht, and 63.6% had underlying diseases. The study found that 82.7% of the participants intended to use SDF for dental caries treatment, with an average score of 2.86 out of 4. The study also found that age, family income, underlying diseases, dental health problems, and fear of the dentist significantly affected their intention. Behavioral beliefs and evaluations of behavioral outcomes significantly affected the intention to use SDF treatment. Most older adults in the study intended to use SDF for dental caries treatment.

Pengaruh Penggunaan Teknologi Informasi, Keahlian Pemakai, dan Intensitas Pemakaian terhadap Kualitas Informasi Akuntansi dengan Budaya Organisasi sebagai Variabel Moderasi

The aim of this research is to analyze the influence of the use of information technology, user expertise, and intensity of use on the quality of accounting information with organizational culture as a moderating variable in the Palembang city regional work unit (SKPD). The sample for this research was 266 respondents as government employees in SKPD Palembang City. Data was collected through a questionnaire and obtained 167 respondents. Data analysis in this research uses a PLS-based Structural Equation Modeling (SEM) approach using Smartpls. The tests carried out are convergent validity test, validity and reliability test, discriminant validity test, R-Square test. To test the hypothesis, the data from the research questionnaire were analyzed using bootstrapping calculations. The research results show that the use of information technology, user expertise, and intensity of use have a positive effect on the quality of accounting information. Meanwhile, organizational culture cannot moderate the use of information technology, user expertise, and intensity of use on the quality of accounting information. The implication of this research is that it can provide practical benefits for regional work units in the city of Palembang. Another implication is to support the theory of reason action in the study.

The Intention of Employee’s Relocation from Urban to Rural Area in China: The Practices of Telework within Metaverse Implementation

The surge in telecommuting during the COVID-19 pandemic is a recent and noteworthy development that could potentially influence patterns of population mobility. In order to limit the transmission of Covid-19, the movement restriction was implemented in the whole world. It involves the shutdown of all government and private sectors, save for those that offer essential services. Concurrently, governmental implementation of social distancing measures has led to the increased utilisation of information and communication technology (ICT) for remote cooperation in professional settings. Consequently, a large portion of Chinese were required to work remotely from their homes during this crisis in order to ensure the safety of employees and sustain economic activity. This has led to a significant rise in telework throughout Asia, Europe, and the United States. The primary objective of this research paper is to discuss the existing knowledge on telework practices and the emerging trend of metaverse in the application of human resource policies in private sectors in China. The study aims to examine the influence of metaverse telework and non-metaverse telework on an employee's inclination to move from an urban to a rural location. This study has been established the conceptual framework which constructed three variable which are Employee’s Intention for Relocate of Residency as endogenous variable, while metaverse telework and non-metaverse telework as exogenous variable. This produced conceptual framework backed by the theory of reason action (TRA) and expected to gather the data via questionnaire from the employees in private sectors in China.

Towards Modeling Uncertainties of Self-Explaining Neural Networks via Conformal Prediction

Despite the recent progress in deep neural networks (DNNs), it remains challenging to explain the predictions made by DNNs. Existing explanation methods for DNNs mainly focus on post-hoc explanations where another explanatory model is employed to provide explanations. The fact that post-hoc methods can fail to reveal the actual original reasoning process of DNNs raises the need to build DNNs with built-in interpretability. Motivated by this, many self-explaining neural networks have been proposed to generate not only accurate predictions but also clear and intuitive insights into why a particular decision was made. However, existing self-explaining networks are limited in providing distribution-free uncertainty quantification for the two simultaneously generated prediction outcomes (i.e., a sample's final prediction and its corresponding explanations for interpreting that prediction). Importantly, they also fail to establish a connection between the confidence values assigned to the generated explanations in the interpretation layer and those allocated to the final predictions in the ultimate prediction layer. To tackle the aforementioned challenges, in this paper, we design a novel uncertainty modeling framework for self-explaining networks, which not only demonstrates strong distribution-free uncertainty modeling performance for the generated explanations in the interpretation layer but also excels in producing efficient and effective prediction sets for the final predictions based on the informative high-level basis explanations. We perform the theoretical analysis for the proposed framework. Extensive experimental evaluation demonstrates the effectiveness of the proposed uncertainty framework.

The Influence of Perceived Value, And Trust on Wom and Its Impact on Repurchase Intention

Objective: to analyze the relationship between Perceived Value to WoM, trust to WoM and Repurchase Intention of Coffee Shop. Theoretical Framework: This study uses a consumer behavior framework, especially the theory of reason action and word of mouth. Method: This research uses a quantitative approach with a survey design using a sample of 165 with convenience sampling technique. The data analysis technique used is Structural equation modeling (SEM); with the Smart PLS device to test the outer model and inner model. Result: Based on the results of data analysis, it shows that perceived value, trust and word of mouth have a direct effect on repurchase intention. Apart from that, Perceive value and trust have an indirect effect on repurchase intention with word of mouth as a mediation. Implications: The results of this study show that to increase repeat purchases by consumers, it is necessary to increase consumer trust and perceived value to increase word of mouth Originality/Value: The results of the study contribute to coffee shop efforts to increase repurchase intentions in the future by increasing consumer value and trust

Quantitative analysis of ecological compensation in the Yangtze River Delta region based on the value of ecosystem services and ecological footprint

IntroductionThe Yangtze River Delta is where “the Belt and Road” and China’s Yangtze River Economic Belt converge, which plays a crucial strategic location. However, with the demands of economic growth, environmental issues are frequently present in the Yangtze River Delta as a result of human and exploitation of ecological resources, and there is an urgent need to coordinate the relationship between human development and ecology.MethodsThe Yangtze River Delta region is used as the research subject, quantitatively analyzing various ecosystems within the region from three dimensions: ecosystem services value, ecological carrying capacity, and ecological footprint, and analyzing various factors that affect economic and environmental balance. Then calculate eco-compensation and its payment order based on this information, in order to build a foundation for coordinating ecological interests and safeguarding the environment in the Yangtze River Delta region.Results and discussion(1) Jiangsu Province was the main provider of ecosystem services in the Yangtze River Delta; the Yangtze River Delta’s three main ecosystems-waters, forests, and wetlands-accounted for 90% of the ecosystem services valued in the area and must be preserved. (2) The Yangtze River Delta as a whole belonged to an ecological deficit area, showing varying degrees of unsustainable development; In terms of ecological deficits, cultivated land and fossil energy had the worst, and both need to be treated severely. (3) The Yangtze River Delta was a region that paid ecological compensation. The payment order and total amount was 6825.596 billion yuan in Shanghai, 6412.264 billion yuan in Jiangsu, 12088.852 billion yuan in Zhejiang, and 3675.637 billion yuan in Anhui. At the same time, considering the current state of Anhui, the amount of eco-compensation can be appropriately reduced. Finally, recommendations are made based on the findings above, in order to improve the overall ecological benefits of the Yangtze River Delta region and foster ecological integration, all provincial and municipal governments should take reasonable action to reverse the ecological deficit of fossil energy and cultivated land, and also aim to achieve an ecological surplus in both land uses.

Determining reasonable response actions following a fly ash spill in the headwaters of Banff National Park, Canada

It can be challenging for practitioners to determine reasonable response actions following an environmental spill because there are risks associated with the recovery process, acute constraints on time, and few case studies available from antecedent events. Here, we evaluate environmental risk using a screening level assessment (SLA) and describe risk management actions during the response phase of a train derailment that released 600 tonnes of fly ash into a headwater creek in Banff National Park, Canada. Trace metal concentrations and physico-chemical parameters from downstream of the derailment site were compared to Canadian environmental quality guidelines and upstream reference values. There was a 1–2.2-fold exceedance of sediment quality guidelines (As, Cd, and Se) as well as a 3.6–17.5-fold exceedance of water quality guidelines (Al, Cd, Fe, and turbidity) downstream of the train derailment. Despite uncertainty about site-specific toxicity when using a SLA, we did require the removal of the settled fly ash from the creek based on the multiple exceedances of guidelines, regulatory context, wilderness setting, and potential contribution to cumulative effects downstream. Case studies that evaluate risk and describe risk management actions help practitioners make consistent and efficient decisions during the response phase of a spill.

Determinants for university students’ location data sharing with public institutions during COVID-19: The Italian case

Abstract Data on real-time individuals’ location may provide significant opportunities for managing emergency situations. For example, in the case of outbreaks, besides informing on the proximity of people, hence supporting contact tracing activities, location data can be used to understand spatial heterogeneity in virus transmission. However, individuals’ low consent to share their data, proved by the low penetration rate of contact tracing apps in several countries during the coronavirus disease-2019 (COVID-19) pandemic, re-opened the scientific and practitioners’ discussion on factors and conditions triggering citizens to share their positioning data. Following the Antecedents → Privacy Concerns → Outcomes (APCO) model, and based on Privacy Calculus and Reasoned Action Theories, the study investigates factors that cause university students to share their location data with public institutions during outbreaks. To this end, an explanatory survey was conducted in Italy during the second wave of COVID-19, collecting 245 questionnaire responses. Structural equations modeling was used to contemporary investigate the role of trust, perceived benefit, and perceived risk as determinants of the intention to share location data during outbreaks. Results show that respondents’ trust in public institutions, the perceived benefits, and the perceived risk are significant predictor of the intention to disclose personal tracking data with public institutions. Results indicate that the latter two factors impact university students’ willingness to share data more than trust, prompting public institutions to rethink how they launch and manage the adoption process for these technological applications.

Examining the Current State of Reflective Teaching Practices Among Newly Appointed Primary School Teachers

This study focused on three newly appointed primary school teachers in City D as research subjects. Utilizing research methods such as interviews, text analysis, and observation, the findings revealed that these teachers exhibited a substantial grasp of teaching reflection. The predominant emphasis in their teaching reflection levels was observed at the technical reasonable reflection and practical action reflection stages. To enhance the reflection levels of these novice teachers, it is recommended to foster their professional ethics, institute a diverse training system, and enhance the school’s infrastructure and institutional support systems.

Analysis of Technical Characteristics of Ren Mengqian, an Excellent Pole Vaulter

To advance the development of Chinese women's pole vaulting technique, three-dimensional photogrammetry and video analysis methods were utilized to conduct kinematic analysis on the technique employed by Ren Mengqian, an outstanding female pole vaulter in China, during the 2019 National Indoor Track and Field Championships finals from an energy transfer perspective. The findings indicate that during the energy acquisition stage, Ren Mengqian executed a reasonable jumping action with appropriate jumping angle and timing that met technical requirements. However, during the energy storage stage, her bow rod had suboptimal effects in her fourth trial jump while insufficient bending of the rod occurred in her fifth trial jump which affected pole energy storage. In terms of energy release stage, Ren Mengqian maximized elastic potential energy through stretching but required acceleration for maximum vertical speed.

Dynamic Scheduling for Large-Scale Flexible Job Shop Based on Noisy DDQN

Article Dynamic Scheduling for Large-Scale Flexible Job Shop Based on Noisy DDQN Tingjuan Zheng 1,2, Yongbing Zhou 1, Mingzhu Hu 1, and Jian Zhang 1,* 1 Institute of Advanced Design and Manufacturing, School of Mechanical Engineering Southwest Jiaotong University, Chengdu 610031, China 2 Guizhou Aerospace Electric Co., Ltd., Guiyang 550009, China * Correspondence: Jerrysmail@263.net Received: 3 July 2023 Accepted: 8 October 2023 Published: 21 December 2023 Abstract: The large-scale flexible job shop dynamic scheduling problem (LSFJSDSP) has a more complex solution space than the original job shop problem because of the increase in the number of jobs and machines, which makes the traditional solution algorithm unable to meet the actual production requirements in terms of the solution quality and time. To address this problem, we develop a dynamic scheduling model of a large-scale flexible job shop based on noisynet-double deep Q-networks (N-DDQNs), which takes the minimum expected completion time as the optimization objective and thoroughly takes into account the two dynamic factors (the new job arrival and the stochastic processing time). Firstly, a Markov decision process model is constructed for dynamic scheduling of a large-scale flexible workshop, and the corresponding reasonable state space, action space and reward function are designed. To address the problems (of solution stability and unsatisfactory scheduling strategy selection) in the conventional exploration method of DDQNs, learnable noise parameters are added to the DDQNs to create the N-DDQN algorithm framework, where the uncertainty weight is added. Secondly, the learnable noise parameters are added to the DDQNs to form the N-DDQN algorithm framework, and the uncertainty weight is added to realize automatic exploration. Hence, the issue is solved that the traditional DDQN exploration method may result in unsatisfactory solution stability and scheduling strategy selection. The proposed method, which has significant flexibility and efficacy, is demonstrated (by experimental findings) to be superior to the conventional method based on compound scheduling rules in tackling large-scale flexible job shop dynamic scheduling problems.

A heuristic-action-involved safe lane-change of autonomous vehicles with multiple constraints on road centerline and speed under highway environment

Lane-change (LC) is one of the most important topics in autonomous vehicles (AVs) on highways. To enhance the implementation of effective LC in AVs, this paper proposes a framework based on deep reinforcement learning, which takes into account heuristic actions and multiple constraints related to the centerline of the road and speed, to improve the overall performance of LC in AVs. Firstly, the influence of unreasonable vehicle actions on the algorithm training process is studied. To improve the rationality of the to-be-trained actions, a novel reasonable action screening mechanism is proposed. Secondly, to keep the vehicle on the centerline of the lane and avoid the collision with other vehicles, a method is designed to calculate the center position of the vehicle. Thirdly, a segmented speed reward mechanism is proposed to constrain vehicle speed. Subsequently, a dynamic reward function is established to train the control algorithm. Lastly, the proposed strategy is evaluated in two simulation scenarios of highways. The simulation results show that the proposed method can increase the number of reasonable actions by more than 30% and improve the success rate of obstacle avoidance with the increase of over 52% in both static and dynamic scenarios compared with the benchmark algorithms.