Formal Model Research Articles

IoV-6G+: A secure blockchain-based data collection and sharing framework for Internet of vehicles in 6G-assisted environment

The growing need for wide and ubiquitous accessibility to advanced Intelligent Transportation Systems (ITS) has led to the evolution of conventional Vehicle to Everything (V2X) paradigms into the Internet of Vehicles (IoVs). Next-generation IoVs establish seamless connections among humans, vehicles, Internet of Things (IoT) devices, and service platforms to enhance transit efficiency, road safety, and environmental conservation, with notable advancements in IoV technologies, specifically in secure data exchange and user privacy protection. Additionally, Unmanned Aerial Vehicles (UAVs) are envisioned as scalable and adaptable solutions for comprehensive IoV service coverage. The current approaches in IoV mainly concentrate on local infrastructure setups, overlooking the potential of infrastructure-less IoVs that lack extensive edge facilities. Also, the transmission of data over public channels by vehicles equipped with servers is susceptible to interception and tampering by malicious attackers. Moreover, the substantial amount of real-time data generated by vehicles, IoT devices, travelers, and social interactions pose a significant strain on servers as well as latency issues. To address these security concerns, this research suggests a secure-lightweight data collection and sharing framework for 6G-assisted smart transportation using blockchain and UAV, called IoV-6G+. The process entails drones collecting information and transmitting it to dedicated edge servers that securely aggregate the information and generate transactions on a cloud server. A cloud server securely collects data from edge servers, creates transactions, combines them into blocks, and then confirms and adds these blocks to the blockchain through a voting-based consensus method in a peer-to-peer network of cloud servers. Additionally, the security analysis of our proposed IoV-6G+ is assessed using the “Informal and formal (i.e., Scyther Tool, and Real-or-Random (RoR) Model) methods”, showcasing its efficacy in delivering a secure and efficient authentication mechanism for IoV. Moreover, the proposed IoV-6G+ exhibits lower computational and communication costs as well as energy consumption, along with enhanced security features, when compared to existing authentication frameworks.

Towards machine-readable semantic-based E-business contract representations using Network of Timed Automata (NTA)

The smart contract is generally stored and executed by a computer program in the blockchain platform. However, the representation of program code is unfriendly for non-software developers since the process of a smart contract involves multiple parties such as lawyers, which is not easy to understand for most humans. To consider a broad acceptance of contracts, we design a novel contract representation to narrow the gap between smart contracts and e-business contracts. This paper proposes a novel semantic-based e-business contract model - Simple Natural Contract (SimNC) to represent a universal contract created by a Supervised Sentence Contract (SSC), which is inputted via a Semantic Input Method (SIM) with strict grammar from a human-understandable natural language contract. Then, the SSC is analyzed through Machine Natural Language (MNL) to enhance contract semantic understanding by enabling case grammar for crossing language parties. In doing so, SimNC analyses various deontic components, and combines them with the operational aspects of a legal contract, to achieve a common and better understanding between hard-code and natural language. In addition, we apply the SimNC into a Network of Timed Automata (NTA) for supporting automation, which builds a formal model including temporal constraints and then translates into an executable SimNC-NTA model. This paper aims to provide a bridge between natural language contracts and e-business contracts, making them universal and intelligible.

Semantic Model for Fragment of Hindi (Part 2)

This paper proposes a formal model for semantic analysis of a fragment of the Hindi language. This paper uses referential noun phrases, transitive and intransitive verb phrases and logical constants to compute the meaning of its sentences generated from the Hindi part-of-speech-tagged corpus features. The paper presents cases of conjunction and negation enriched with idempotent laws that provide semantic computation of simple and complex well-formed formulas. Our system works for any model, with one such model described in our glossary. It deals with the set-theoretic study of essential syntactic categories of Hindi, suggesting the suitability of our rule-based syntactic arrangement and model-based semantic computation by implementing them through an in-house software tool.

Formal modelling and verification of autonomous reasoning based flight simulation system

The emerging trends towards intelligent computing have drastically changed the human lifestyle in every facet of their lives. Due to the swift escalation of smart systems and emerging technologies, human life has become much more dependent and even more addicted to fulfilling their desire using smart and tiny resource-bounded gadgets. This revolution has evolved towards autonomous decision support systems. Autonomous decision support systems have the ability to acquire information autonomously, reason the information, and adapt behavior accordingly. As these systems are deployed in a highly decentralized environment and exhibit complex adaptive behavior, however, the inconsistent nature of information may raise different challenging issues. This paper presents a multi-agent environmentally-aware framework for modeling and reasoning flight management systems. This system has a sound reasoning mechanism to execute and monitor flight control activities while considering liveness and safety-critical constraints. We use the UPPAAL model checker to formally analyze the system’s behavior and verify correctness properties.

Applying a Mathematical Model for Calculating the Ideal Nutrition for Sheep

The principal economic sector devoted to the breeding, raising, and production of farm animals is known as the livestock industry. There are precise standards for making high-quality feed in animal husbandry. Precision livestock feeding is a crucial component, with the potential to significantly impact the profitability of livestock; it permits the provision of diets to animals that are precisely tailored to their specific daily nutritional needs. Through simulation modeling, a single model can be created for automated systems to determine daily rations for farm animals. For the purposes of this document, precision livestock feeding refers to the practice of tailoring feed to individual animals or groups of animals, taking into account their changing nutritional needs over time and individual differences in terms of nutritional requirements. The practice aims to optimize animal health and performance while reducing feed waste. This paper presents a formal model for determining the quantities of components needed to achieve a minimum cost mixture that satisfies compositional and quantitative criteria. The present research calculates the amount of hay and silage required to feed an animal per day at the most economical cost by applying an optimization approach that involves defining and solving an optimization problem. The problem is solved using a well-known software package, which is necessary for the practical application of the resulting model. Real data from livestock production in Bulgaria are used to numerically test the model.

A rationale for the Right-to-Development climate policy stance?

We present a formal model that analyzes the trade-offs between environmental policy and economic growth in a developing economy. The adoption of restrictive environmental policies limits the use of abundant fossil energy resources, which may slow down economic development and thus violate the Right-to-Development. If faster economic growth allows a country to grow out of pollution sooner, less stringent policies are good for growth and even for the environment, having adopted a long-term horizon. Accounting for a ceiling on cumulative emissions can reinforce the argument by providing an additional rationale to phase out pollution. One assumption is crucial for the argument to hold: polluting fossil energy is an essential input over the early phase of economic development, but not in the later phases. Such a discontinuity could result from structural change. We provide empirical evidence for the plausibility of a discontinuity in the elasticity of carbon dioxide emissions with respect to aggregate output, using cross country data, even if it does not appear to be as strong as assumed in the model economy.

Life Expectancy Reversals in Low‐Mortality Populations

AbstractBehind the steady march of progress toward longer life expectancy in many low‐mortality countries, there have been setbacks even before the Covid‐19 pandemic. In this paper, we use an exploratory approach to describe the temporal structure, age patterns, and geographic aspects of life expectancy reversals. We find that drops in life expectancy are often followed by larger than average improvements, which tells us that most reversals are transitory with little long‐term influence. The age structure of mortality decline when life expectancy falls is tilted toward older ages, a pattern that is quite different from the general pattern of mortality improvement. Geographic analysis shows that mortality reversals are correlated across neighboring countries like Italy and France, or Canada and the United States. These findings are consistent with contagious disease and weather being important causes of life expectancy reversals. We conclude with a discussion of implications for formal modeling and forecasting of mortality to accommodate these patterns that violate some standard assumptions.

S2O: A smart objects orchestrator framework based on the web of things

The Web of Things (WoT) is a new paradigm in which everyday objects are connected to the Internet using popular Web technologies. The smart things are abstracted into RESTful Web services to facilitate their manipulation. The composition of these objects within the same business process creates an automated, time-synchronized routine that can perform even the most complex tasks. BPEL is an orchestration language that defines the process responsible for coordinating the Web services involved. However, this language presents some limitations in this context. BPEL 2.0 does not support RESTful Web services; its specification is incompatible with this architectural style of services. Moreover, its temporal expressivity is insufficient to cover all the constraints that may arise when composing services. This work aims to adapt BPEL to the requirements of the WoT environment, enabling it to create processes that invoke the smart things in precise time intervals. The solution is to exploit one of BPEL's strengths: its extensibility. The BPEL specification is enriched with four activities that reflect the REST uniform interface. They include the necessary attributes to send the request to the target object and receive the response in convened format representation. Also, temporal attributes are added to BPEL elements to schedule their start, end, and duration of execution. The manual addition of these temporal values requires a verification of their accuracy. The BPEL process must be reviewed to ensure its validity before its execution. A temporal Petri Net is proposed to detect any conflicts or inconsistencies between BPEL activities. As a result, associating the formal model with the extension allows BPEL to orchestrate smart things represented by RESTful Web services according to a well-defined temporal scenario. They respect the REST constraints and provide the BPEL activities with temporal attributes for efficient time management. The approach can be applied in all application areas to create temporal scenarios.

An Axiomatic Theory for Reversible Computation

Undoing computations of a concurrent system is beneficial in many situations, such as in reversible debugging of multi-threaded programs and in recovery from errors due to optimistic execution in parallel discrete event simulation. A number of approaches have been proposed for how to reverse formal models of concurrent computation, including process calculi such as CCS, languages like Erlang, and abstract models such as prime event structures and occurrence nets. However, it has not been settled as to what properties a reversible system should enjoy, nor how the various properties that have been suggested, such as the parabolic lemma and the causal-consistency property, are related. We contribute to a solution to these issues by using a generic labelled transition system equipped with a relation capturing whether transitions are independent to explore the implications between various reversibility properties. In particular, we show how all properties we consider are derivable from a set of axioms. Our intention is that when establishing properties of some formalism, it will be easier to verify the axioms rather than proving properties such as the parabolic lemma directly. We also introduce two new properties related to causal-consistent reversibility, namely causal liveness and causal safety, stating, respectively, that an action can be undone if (causal liveness) and only if (causal safety) it is independent from all of the following actions. These properties come in three flavours: defined in terms of independent transitions, independent events, or via an ordering on events. Both causal liveness and causal safety are derivable from our axioms.

Multi-Agent Reinforcement Learning for Job Shop Scheduling in Dynamic Environments

In response to the challenges of dynamic adaptability, real-time interactivity, and dynamic optimization posed by the application of existing deep reinforcement learning algorithms in solving complex scheduling problems, this study proposes a novel approach using graph neural networks and deep reinforcement learning to complete the task of job shop scheduling. A distributed multi-agent scheduling architecture (DMASA) is constructed to maximize global rewards, modeling the intelligent manufacturing job shop scheduling problem as a sequential decision problem represented by graphs and using a Graph Embedding–Heterogeneous Graph Neural Network (GE-HetGNN) to encode state nodes and map them to the optimal scheduling strategy, including machine matching and process selection strategies. Finally, an actor–critic architecture-based multi-agent proximal policy optimization algorithm is employed to train the network and optimize the decision-making process. Experimental results demonstrate that the proposed framework exhibits generalizability, outperforms commonly used scheduling rules and RL-based scheduling methods on benchmarks, shows better stability than single-agent scheduling architectures, and breaks through the instance-size constraint, making it suitable for large-scale problems. We verified the feasibility of our proposed method in a specific experimental environment. The experimental results demonstrate that our research can achieve formal modeling and mapping with specific physical processing workshops, which aligns more closely with real-world green scheduling issues and makes it easier for subsequent researchers to integrate algorithms with actual environments.

Theoretical investigation of the conformational preference and spectroscopic analysis of cinnamoyl chloride and cinnamide

The rotational isomers of cinnamoyl chloride and cinnamide have been computed using the B3LYP method and the 6-311++G (d, p) basis set to calculate their molecular structure, conformational stability, and spectroscopic properties. Computations revealed that the s-cis rotamers of both compounds are the most stable rotamers in both the gas phase and in the solution. The s-trans rotational barrier is 8.76 kcal/mol and 5.63 kcal/mol for cinnamoyl chloride and cinnamide, respectively. Replacing the Cl group of the cinnamoyl chloride with the NH2 group in the cinnamide has reduced the highest occupied molecular orbital – lowest unoccupied molecular orbital (HOMO-LUMO) gap. The solvation effects on the conformational stability of rotamers have been studied in nine solvents (heptane, chloroform, tetrahydrofuran, dichloroethane, acetone, ethanol, methanol, dimethyl sulfoxide, and water) using the integral equation formalism of the polarizable continuum model. The chemical shifts of 13C and 1H NMR spectra have been simulated in the gas phase, dimethyl sulfoxide (DMSO), and chloroform using the gauge-independent atomic orbital (GIAO) method. The UV absorption spectral analysis has been computed in different solvents (chloroform, methanol, and water), and atomic charges calculated. The examination of vibrational wave numbers and their corresponding assignments revealed excellent agreement between simulated and experimental infrared spectra for the studied molecules.

Harmonizing Compliance: Coordinating Automated Verification Processes within Cloud-based AI/ML Workflows

The significance of ensuring security and upholding data privacy within cloud-based workflows is widely recognized in research domains. This importance is particularly evident in contexts such as safeguarding patients' private data managed within cloud-deployed workflows, where maintaining confidentiality is paramount, alongside ensuring secure communication among involved stakeholders. In response to these imperatives, our paper presents an architecture and formal model designed to enforce security measures within cloud workflow orchestration. Central to our proposed architecture is the emphasis on continuous monitoring of cloud resources, workflow tasks, and data streams to detect and preempt anomalies in workflow orchestration processes. To accomplish this, we advocate for a multi-modal approach that integrates deep learning, one-class classification, and clustering techniques. In essence, our proposed architecture offers a comprehensive solution for enforcing security within cloud workflow orchestration, harnessing advanced methodologies like deep learning for anomaly detection and prediction. This approach is particularly pertinent in critical sectors such as healthcare, especially during unprecedented events like the COVID-19 pandemic.

Harmonizing Compliance: Coordinating Automated Verification Processes within Cloud-based AI/ML Workflows

The significance of ensuring security and upholding data privacy within cloud-based workflows is widely recognized in research domains. This importance is particularly evident in contexts such as safeguarding patients' private data managed within cloud-deployed workflows, where maintaining confidentiality is paramount, alongside ensuring secure communication among involved stakeholders. In response to these imperatives, our paper presents an architecture and formal model designed to enforce security measures within cloud workflow orchestration. Central to our proposed architecture is the emphasis on continuous monitoring of cloud resources, workflow tasks, and data streams to detect and preempt anomalies in workflow orchestration processes. To accomplish this, we advocate for a multi-modal approach that integrates deep learning, one-class classification, and clustering techniques. In essence, our proposed architecture offers a comprehensive solution for enforcing security within cloud workflow orchestration, harnessing advanced methodologies like deep learning for anomaly detection and prediction. This approach is particularly pertinent in critical sectors such as healthcare, especially during unprecedented events like the COVID-19 pandemic.

Using Large Language Models for (De-)Formalization and Natural Argumentation Exercises for Beginner's Students

Using Large Language Models for (De-)Formalization and Natural Argumentation Exercises for Beginner's Students

When manufacturers turn into digital platform providers: A transformation model to understand the platformization pathway

Platformization has arrived in manufacturing. However, to enter the digital platform business manufacturers need to make a fundamental shift from product thinking to platform thinking. To master this shift, firms have to take the platformization pathway, which describes the process when manufacturers turn into digital platform providers. However, little is known about platformization on the firm level from a transformation perspective. Our study attempts to close this gap and develops a transformation model that frames the platformization pathway when established manufacturers become platform leaders. For this purpose, we adopt the concept of ambidexterity and develop a theoretical framework to conceptualize and investigate the transformation process as a result of sequential transitions and balancing between explorative and exploitative activities. Moreover, we use interviews from 29 large manufacturers and conduct an inductive case study analysis to identify the different stages, the contextual factors and the dynamics of transformation. By combining the theoretical framework and the results of our case study analysis, we develop a formal transformation model that contributes to understanding the manufacturers' platformization pathway. Our results also aim to support manufacturers in designing their strategies so they can turn into leading digital platform providers.

Enhanced performance prediction of ATL model transformations

Model transformation languages are domain-specific languages used to define transformations of models. These transformations consist of the translation from one modeling formalism into another or just the updating of a given model. Such transformations are often described declaratively and are often implemented based on very small models that cover the language of the input model. As a result, transformation developers are often unable to assess the time required to transform a larger model.Hence, we propose a prediction approach based on machine learning which uses a set of model characteristics as input and provides a prediction of the execution time of a transformation defined in the Atlas Transformation Language (ATL). In our previous work (Groner et al., 2023), we already showed that support vector regression in combination with a model characterization based on the number of model elements, the number of references, and the number of attributes is the best choice in terms of usability and prediction accuracy for the transformations considered in our experiments.A major weakness of our previous approach is that it fails to predict the performance of transformations that also transform attribute values of arbitrary length, such as string values. Therefore, we investigate in this work whether an extension of our feature sets that describes the average size of string attributes can help to overcome this weakness.Our results show that the random forest approach in combination with model characterizations based on the number of model elements, the number of references, the number of attributes, and the average size of string attributes filtered by the 85th percentile of their variance is the best choice in terms of the simple way to describe a model and the quality of the obtained prediction. With this combination, we obtained a mean absolute percentage error (MAPE) of 5.07% over all modules and a MAPE of 4.82% over all modules excluding the transformation for which our previous approach failed. Whereas, we obtained previously a MAPE of 38.48% over all modules and a MAPE of 4.45% over all modules excluding the transformation for which our previous approach failed.

Differences in perceived sources of uncertainty in natural hazards science advice: lessons for cross-disciplinary communication

IntroductionWe conducted mental model interviews in Aotearoa NZ to understand perspectives of uncertainty associated with natural hazards science. Such science contains many layers of interacting uncertainties, and varied understandings about what these are and where they come from creates communication challenges, impacting the trust in, and use of, science. To improve effective communication, it is thus crucial to understand the many diverse perspectives of scientific uncertainty.MethodsParticipants included hazard scientists (n = 11, e.g., geophysical, social, and other sciences), professionals with some scientific training (n = 10, e.g., planners, policy analysts, emergency managers), and lay public participants with no advanced training in science (n = 10, e.g., journalism, history, administration, art, or other domains). We present a comparative analysis of the mental model maps produced by participants, considering individuals’ levels of training and expertise in, and experience of, science.ResultsA qualitative comparison identified increasing map organization with science literacy, suggesting greater science training in, experience with, or expertise in, science results in a more organized and structured mental model of uncertainty. There were also language differences, with lay public participants focused more on perceptions of control and safety, while scientists focused on formal models of risk and likelihood.DiscussionThese findings are presented to enhance hazard, risk, and science communication. It is important to also identify ways to understand the tacit knowledge individuals already hold which may influence their interpretation of a message. The interview methodology we present here could also be adapted to understand different perspectives in participatory and co-development research.

A Formal Model of Affiliative Interpersonality

Capturing the complexity of interpersonal dynamics—emerging from approach and avoidance motives of two individuals in dyadic interplay—remains challenging. In line with calls for embracing complexity in psychological research using formal modeling, we employed evolutionary game theory to investigate the underlying mechanisms of affiliative interpersonality. We constructed a relational state space that represents the ways of relating available in the momentary state of an interpersonal relationship. Next, we modeled relationships as trajectories in that relational space. Qualitatively different interpersonal dynamics emerged: (a) global stability with only one relational attractor (e.g., pure reciprocal friendliness), (b) bistability with two mutually exclusive attractors (e.g., either pure friendliness or pure distance), and (c) cycles between friendliness and distance in the relational space. The bistable dynamics appear to resemble the phenomenon of interpersonal complementarity (e.g., friendliness invites friendliness). Furthermore, the model generates psychopathologically relevant dynamics (e.g., oscillating, unstable interpersonal relationships in borderline personality disorder).

A novel approach toward cyberbullying with intelligent recommendations using deep learning based blockchain solution.

Integrating healthcare into traffic accident prevention through predictive modeling holds immense potential. Decentralized Defense presents a transformative vision for combating cyberbullying, prioritizing user privacy, fostering a safer online environment, and offering valuable insights for both healthcare and predictive modeling applications. As cyberbullying proliferates in social media, a pressing need exists for a robust and innovative solution that ensures user safety in the cyberspace. This paper aims toward introducing the approach of merging Blockchain and Federated Learning (FL), to create a decentralized AI solutions for cyberbullying. It has also used Alloy Language for formal modeling of social connections using specific declarations that are defined by the novel algorithm in the paper on two different datasets on Cyberbullying and are available online. The proposed novel method uses DBN to run established relation tests amongst the features in two phases, the first is LSTM to run tests to develop established features for the DBN layer and second is that these are run on various blocks of information of the blockchain. The performance of our proposed research is compared with the previous research and are evaluated using several metrics on creating the standard benchmarks for real world applications.

Does Consumer’s Happiness and Other Emotions Signaling Affect Seller’s Prices? Theory and Evidence From Six Field Studies

The factors that determine the prices of goods and services are within the core inquiry of economic science. Do consumer’s emotions affect seller’s selling prices? The current study explores this issue through six field studies. The first four studies focus on happiness, demonstrating for the first time that happiness signals affect the prices of products and services. Happy customers are offered to pay lower price for goods–cellphones and bicycles–and for related services. The results are relevant both in face-to-face and online interactions and in between—and within—subject designs, implying that extended real income is another objective benefit of individual happiness. Two additional experiments do not show the same effect when other emotions–anger and disgust–are signaled by the consumer. We present a formal model for the positive happiness premium and discuss the welfare implications of our findings. JEL classification: C93, D01, D21.