Reviewerly/exHarmony huggingface dataset:
Autonomous Robot Navigation System as Part of a Human-Machine Team Based on Self-organization of Distributed Neurocognitive Architectures

The relevance of this study is due to the solution of the problem of developing the basic principles and algorithms for providing adaptive settings for autonomous robots intelligent control systems as part of a human-machine team based on the general method of machine learning. To do this, the paper proposes to use a formalism based on multi-agent neurocognitive architectures. Implementation of the possibility of adaptation is considered on the example of performing the task of orientation and navigation of an autonomous robot in an unfamiliar environment. An autonomous robot navigation system based on self-organization of distributed neurocognitive architectures has been developed. A multi-agent neurocognitive architecture is presented, which forms an active map containing all the locative information necessary to ensure the orientation and navigation of an autonomous robot between loci. The use of a multi-agent architecture to provide the representation of locative information in the task of implementing an interface in human-machine team will make it possible to build an ontology responsible for representing the location of objects in the external environment, as well as provide interaction with the user in natural language, taking into account its semantics.
Study of Path Planning Methods in Two-Dimensional Mapped Environments

The article studies the problem of path planning in two-dimensional environments. The review and analysis of known planning algorithms are carried out. This article is devoted to the development of a modified rapidly growing random trees algorithm (RRT) and the study of its effectiveness in comparison with known methods. The presented modified RRT algorithm checks the path to some area near the specified node while planning the path to a new potential node of the tree, which reduces the constructed nodes of the tree. The developed algorithm is compared with the original RRT algorithm. The comparison criteria are the path calculation time, the amount of memory required, the path length, and the percentage of situations in which the trajectory to the target point was successfully found. Next, the developed algorithm is compared with the planning algorithms of other classes. The study uses representative samples of numerical experiments and various environments that differ in the density of obstacles and the presence of mazes. A study of planning algorithms using the results of experiments on a ground-based wheeled robot has also been conducted.
3D-CNNs-Based Touchless Human-Machine Interface

Interacting with machines via hand gestures is a common way for people to communicate with robots. Human utilize gestures in a regular talk to convey meaning and emotions to one another. Gesture-based interactions are utilized in a wide range of applied to a wide range of fields, as telephones, TVs, monitors, video games, and other electronic devices. By technological improvements, gesture recognition is now a more realistic and appealing approach in the context of human interaction. In this research, the relevant experiments are conducted using numerous types of convolutional neural networks, including the proposed customized model, to see which ones performs the best. Because of the introduction of such Microsoft Kinect sensor, increased depth and vision sensing has been widely important for several purposes. Given its ability to measure ranges to objects at a fast frame rate, these types of sensors are widely being employed for 3D acquisitions, as well as for other purposes in robotics and machine learning. This research made use of the Kinect sensor and the use of an RGB-D camera and a 3D convolution neural network, which offer a novel approach for fingertips identification and hand gesture classification in real time that is both accurate and fast (3DCNN).
Model-Based Policy Optimization with Neural Differential Equations for Robotic Arm Control

Applying learning-based control methods to real robots presents hard challenges, including the low sample efficiency of model-free reinforcement learning algorithms. The widely adopted approach to tackling this problem uses an environment dynamics model. We propose to use the Neural Ordinary Differential Equations to approximate transition dynamics as this allows for finer control of a trajectory generation process. NODE offers a continuous-time formulation that captures the temporal dependencies. We evaluate our approach on various tasks from simulation environment including learning 6-DoF robotic arm to open the door, which represents particular challenges for policy search. The NODE model is trained to predict movement of the arm and the door, and is used to generate trajectories for the model-based policy optimization. Our method shows better sample efficiency on this task comparing to the model-free and model-based baseline. It also shows comparable results on several other tasks. The application of NODE to model-based reinforcement learning enables more precise modeling of robotic system dynamics and enhances the sample efficiency of learning-based control methods. The empirical evaluation on various tasks demonstrates the efficacy of our approach, offering promising prospects for improving the performance and efficiency of real-world robotic systems.
Legal, Regulatory, and Ethical Considerations in E-Business

The rise of the digital economy has necessitated e-commerce companies to give significant attention to ethical, legal, and regulatory concerns. To ensure responsible and sustainable business practices, companies must navigate a complex landscape of rules and regulations while adhering to ethical principles and standards. This chapter provides an overview of the key legal, regulatory, and ethical considerations for e-businesses. It covers topics such as patents, copyrights, trademarks, and the protection of intellectual property rights in online commerce. It also delves into privacy and data protection laws, their impact on online commerce, and the importance of safeguarding consumers’ personal information and privacy. Furthermore, the chapter examines the laws and regulations governing advertising and marketing in e-commerce, as well as prevalent cybersecurity threats and fraud prevention techniques. It analyzes the potential for unethical behavior in online commerce, focusing on privacy, security, and social responsibility, and offers suggestions for overcoming these challenges. By thoroughly understanding the legal, regulatory, and ethical aspects of e-business, companies can earn the trust of their customers, operate transparently and accountably, and avoid potential legal and reputational risks.
Estimating Causal Responsibility for Explaining Autonomous Behavior

There has been growing interest in causal explanations of stochastic, sequential decision-making systems. Structural causal models and causal reasoning offer several theoretical benefits when exact inference can be applied. Furthermore, users overwhelmingly prefer the resulting causal explanations over other state-of-the-art systems. In this work, we focus on one such method, MeanRESP, and its approximate versions that drastically reduce compute load and assign a responsibility score to each variable, which helps identify smaller sets of causes to be used as explanations. However, this method, and its approximate versions in particular, lack deeper theoretical analysis and broader empirical tests. To address these shortcomings, we provide three primary contributions. First, we offer several theoretical insights on the sample complexity and error rate of approximate MeanRESP. Second, we discuss several automated metrics for comparing explanations generated from approximate methods to those generated via exact methods. While we recognize the significance of user studies as the gold standard for evaluating explanations, our aim is to leverage the proposed metrics to systematically compare explanation-generation methods along important quantitative dimensions. Finally, we provide a more detailed discussion of MeanRESP and how its output under different definitions of responsibility compares to existing widely adopted methods that use Shapley values.
ChorSSI: A BPMN-Based Execution Framework for Self-Sovereign Identity Systems on Blockchain

The digital age has made identity a crucial aspect of online activities due to our increasing reliance on digital platforms. This has led to the development of different identity management systems, relying on centralised infrastructures but exposed to security vulnerabilities. Self-Sovereign Identity (SSI) is a promising alternative, as it allows individuals to control their personal data and securely share it with others without relying on a central authority. However, developing such systems and executing the related operations is complex and challenging, especially for non-expert users. To simplify the development process, we propose ChorSSI, a BPMN-based framework that supports the modelling of an SSI system and the execution of the related interactions. The design relies on BPMN choreography diagrams, permitting the representation of SSI interactions between parties in a distributed manner. The proposed framework was implemented and tested over the real Chromaway property transaction case study.
Inherently Interpretable Deep Reinforcement Learning Through Online Mimicking

Although deep reinforcement learning (DRL) methods have been successfully applied in challenging tasks, their application in real-world operational settings - where transparency and accountability play important roles in automation - is challenged by methods’ limited ability to provide explanations. Among the paradigms for explainability in DRL is the interpretable box design paradigm, where interpretable models substitute inner closed constituent models of the DRL method, thus making the DRL method “inherently” interpretable. In this paper we propose a generic paradigm where interpretable DRL models are trained following an online mimicking paradigm. We exemplify this paradigm through XDQN, an explainable variation of DQN that uses an interpretable model trained online with the deep Q-values model. XDQN is challenged in a complex, real-world operational multi-agent problem pertaining to the demand-capacity balancing problem of air traffic management (ATM), where human operators need to master complexity and understand the factors driving decision making. XDQN is shown to achieve high performance, similar to that of its non-interpretable DQN counterpart, while its abilities to provide global models’ interpretations and interpretations of local decisions are demonstrated.
DHC-R: Evaluating “Distributed Heuristic Communication” and Improving Robustness for Learnable Decentralized PO-MAPF

Multi-agent pathfinding (MAPF) is a problem of coordinating the movements of multiple agents operating a shared environment that has numerous industrial and research applications. In many practical cases the agents (robots) have limited visibility of the environment and must rely on local observations to make decisions. This scenario, known as partially observable MAPF (PO-MAPF), can be solved through decentralized approaches. In recent years, several learnable algorithms have been proposed for solving PO-MAPF. However, their performance is oftentimes not validated out-of-distribution (OOD), and the code is often not properly open-sourced. In this study, we conduct a comprehensive empirical evaluation of one of the state-of-the-art decentralized PO-MAPF algorithms, Distributed Heuristic Communication (DHC), Ma, Z., Luo, Y., Ma, H.: Distributed heuristic multi-agent path finding with communication. In: 2021 International Conference on Robotics and Automation (ICRA), pp. 8699–8705. IEEE, Xi’an, China (2021), which incorporates communication between agents. Our experiments reveal that the performance of DHC deteriorates when agents encounter complete packet loss during communication. To address this issue, we propose a novel algorithm called DHC-R that employs a similar architecture to the original DHC but introduces randomness into the graph neural network-based communication block, preventing the passage of some data packets during training. Empirical evaluation confirms that DHC-R outperforms DHC in scenarios with packet loss. Open-sourced model weights and the codebase are provided: https://github.com/acforvs/dhc-robust-mapf .
Towards an Understanding of Trade-Offs Between Blockchain and Alternative Technologies for Inter-organizational Business Process Enactment

Several studies have proposed the application of blockchains in Inter-organizational Business Processes (IOBPs), primarily citing the technology’s immutability, trust, and transparency as motivating factors. However, there is a notable lack of detailed comparisons between traditional, non-blockchain-based architectures and those incorporating this new technology. Such a comparison is critical for practitioners like software architects to fully comprehend blockchain-based solutions’ strengths and their potential trade-offs and suitable scenarios for alternative technologies. This paper endeavors to bridge this knowledge gap by contrasting the attributes of public and private blockchains with those of Trusted Third Parties (TTPs) and Electronic Data Interchange (EDI) – the latter being a widespread method for automated data exchange between organizations. We underscore less explored advantages of blockchains, such as the ability to provide non-equivocation. Conversely, we identify that TTPs offers lower complexity levels and superior flexibility.
Mining and Validating Belief-Based Agent Explanations

Agent explanation generation is the task of justifying the decisions of an agent after observing its behaviour. Much of the previous explanation generation approaches can theoretically do so, but assuming the availability of explanation generation modules, reliable observations, and deterministic execution of plans. However, in real-life settings, explanation generation modules are not readily available, unreliable observations are frequently encountered, and plans are non-deterministic. We seek in this work to address these challenges. This work presents a data-driven approach to mining and validating explanations (and specifically belief-based explanations) of agent actions. Our approach leverages the historical data associated with agent system execution, which describes action execution events and external events (represented as beliefs). We present an empirical evaluation, which suggests that our approach to mining and validating belief-based explanations can be practical.
Reinforcement Learning in Cyclic Environmental Changes for Agents in Non-Communicative Environments: A Theoretical Approach

Multi-agent Reinforcement Learning is required to adapt to the dynamic of the environment by transferring the learning outcomes in the case of the non-communicative and dynamic environment. Profit minimizing reinforcement learning with the oblivion of memory (PMRL-OM) enables agents to learn a co-operative policy using learning dynamics instead of communication information. It enables the agents to adapt to the dynamics of the other agents’ behaviors without any design of the relationship or communication rules between agents. It helps easily to add robots to the system with keeping co-operation in a multi-robot system. However, it is available for long-term dynamic changes, but not for the short-them changes because it used the outcome with enough trials. This paper picked up cyclic environmental changes as short-term changes and aimed to improve the performance in cyclic environmental changes and analyze theoretically the rationality of this approach. Specifically, we extend PMRL-OM based on an analysis of the PMRL-OM approach. Our experiments evaluated the performance of the proposed method for a navigation task in a maze-type environment undergoing cyclic environmental change, with the results showing that the proposed method gave an enhanced performance. Our method also enabled the adaptation to cyclic change to occur sooner than for the existing PMRL-OM method. In addition, the theoretical analysis not only investigates the PMRL-OM rationality but also suggests optimal parameter values for the proposed method. The proposed method contributed to XAI by showing the precise profits of the agents and the approach with rationality.
Leveraging Imperfect Explanations for Plan Recognition Problems

Open environments require dynamic execution of plans where agents must engage in settings that include, for example, re-planning, plan reusing, plan repair, etc. Hence, real-life Plan Recognition (PR) approaches are required to deal with different classes of observations (e.g., exogenous actions, switching between activities, and missing observations). Many approaches to PR consider these classes of observations, but none have dealt with them as deliberated events. Actually, using existing PR methods to explain such classes of observations may generate only so-called imperfect explanations (plans that partially explain a sequence of observations). Our overall approach is to leverage (in the sense of plan editing) imperfect explanations by exploiting new classes of observations. We use the notation of capabilities in the well-known Belief-Desire-Intention (BDI) agents programming as an ideal platform to discuss our work. To validate our approach, we show the implementation of our approach using practical examples from the Monroe Plan Corpus.
Accelerating the Support of Conversational Interfaces for RPAs Through APIs

In the business automation world, APIs are everywhere. They provide access to enterprise tools such as customer relationship management solutions, or custom automations such as unattended RPA bots that automate repetitive tasks. Unfortunately, they may not be accessible to the business users who need them but are not equipped with the necessary technical skills to leverage them. Most recently, chatbots are becoming the go-to medium to make automation software accessible to business users. Since API specifications aren’t written with a chatbot use in mind, additional work is needed to make APIs accessible through a natural language interface. Making this process scalable to many APIs requires an automated data training pipeline for intent recognition models, a crucial component within chatbots to understand natural language utterances from users. More accurate intent recognition models lead to better user experience and satisfaction. Prior work proposed approaches to extracting intents from OpenAPI specifications. However, the resulting models tend to be brittle due to weaknesses in training data. In this work, we propose a data augmentation approach based on paraphrasing using large language models and propose a system to generate sentences to train intent recognition models. Experimental results highlight the effectiveness of our approach. Our system is deployed in a real world setting.
The Quarrel of Local Post-hoc Explainers for Moral Values Classification in Natural Language Processing

Although popular and effective, large language models (LLM) are characterised by a performance vs. transparency trade-off that hinders their applicability to sensitive scenarios. This is the main reason behind many approaches focusing on local post-hoc explanations recently proposed by the XAI community. However, to the best of our knowledge, a thorough comparison among available explainability techniques is currently missing, mainly for the lack of a general metric to measure their benefits. We compare state-of-the-art local post-hoc explanation mechanisms for models trained over moral value classification tasks based on a measure of correlation. By relying on a novel framework for comparing global impact scores, our experiments show how most local post-hoc explainers are loosely correlated, and highlight huge discrepancies in their results—their “quarrel” about explanations. Finally, we compare the impact scores distribution obtained from each local post-hoc explainer with human-made dictionaries, and point out that there is no correlation between explanation outputs and the concepts humans consider as salient.
Loose Collaborations on the Blockchain: Survey and Challenges

Blockchain technology has emerged as a promising infrastructure for enabling collaboration between mutually distrustful organizations. The enactment of blockchain-based collaborative processes typically requires a profound understanding of the process being executed, limiting support for flexible processes that cannot be fully prespecified at design time. To overcome this limitation, support for looseness, dealing with the configuration and execution of underspecified processes, is essential. In this paper, we conduct a systematic literature review to examine looseness support for blockchain-based collaborative processes from a behavioral and organizational perspective. In addition, we identify open research challenges to pave the way for further research in this area.
Counterfactual, Contrastive, and Hierarchical Explanations with Contextual Importance and Utility

Contextual Importance and Utility (CIU) is a model-agnostic method for post-hoc explanation of prediction outcomes. In this paper we describe and show new functionality in the R implementation of CIU for tabular data. Much of that functionality is specific to CIU and goes beyond the current state of the art.
A systematic literature review of undiscovered vulnerabilities and tools in smart contract technology

Abstract In recent years, smart contract technology has garnered significant attention due to its ability to address trust issues that traditional technologies have long struggled with. However, like any evolving technology, smart contracts are not immune to vulnerabilities, and some remain underexplored, often eluding detection by existing vulnerability assessment tools. In this article, we have performed a systematic literature review of all the scientific research and papers conducted between 2016 and 2021. The main objective of this work is to identify what vulnerabilities and smart contract technologies have not been well studied. In addition, we list all the datasets used by previous researchers that can help researchers in building more efficient machine-learning models in the future. In addition, comparisons are drawn among the smart contract analysis tools by considering various features. Finally, various future directions are also discussed in the field of smart contracts that can help researchers to set the direction for future research in this domain.
Patent Law at the Collision Point of Artificial Intelligence and Life Science Innovations

Artificial Intelligence (AI) is no longer a futuristic notion; rather it is very much at today’s forefront. With broad applications in communications, agriculture, education, finance, health care, medicine and beyond, AI technology is deeply embedded into our everyday lives and has been defined as “the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.” [1].