The most important problems of legal philosophy and the nature of law

The purpose of legal philosophy is frequently defined as the discovery or exploration of the nature of law. The nature of law is usually understood as a set of necessary properties of law. Such an identification of the purpose of legal philosophy raises some doubts. Irrespective of those doubts, I claim that that focusing exclusively on the nature of law may be detrimental to legal philosophy as a whole, as it may be an obstacle to the investigation of certain issues that seem important. Or, at least, not all fundamental problems of legal philosophy may be perceived as pertaining to the nature of the law. Two such problems are briefly discussed: (i) legal pluralism and (ii) certain new categories of non-human legal subjects, such as autonomous machines, environmental legal persons and animals. I argue that focusing on the nature of law does not help the exploration of those important topics.

Human but not robotic gaze facilitates action prediction

Interpreting the behaviour of autonomous machines will be a daily activity for future generations. Yet, surprisingly little is currently known about how people ascribe intentions to human-like and non-human-like agents or objects. In a series of six experiments, we compared people’s ability to extract non-mentalistic (i.e., where an agent is looking) and mentalistic (i.e., what an agent is looking at; what an agent is going to do) information from identical gaze and head movements performed by humans, human-like robots, and a non-human-like object. Results showed that people are faster to infer the mental content of human agents compared to robotic agents. Furthermore, the form of the non-human entity may differently engage mentalizing processes depending on how human-like its appearance is. These results are not easily explained by non-mentalizing strategies (e.g., spatial accounts), as we observed no clear differences in control conditions across the three different agents. Overall, results suggest that human-like robotic actions may be processed differently from both humans’ and objects’ behaviour. We discuss the extent to which these findings inform our understanding of the relevance of an agents’ or objects’ physical features in triggering mentalizing abilities and its relevance for human–robot interaction.

Sensing and Biosensing in the World of Autonomous Machines and Intelligent Systems

In this paper we discuss how nanotech-based sensors and biosensors are providing the data for autonomous machines and intelligent systems, using two metaphors to exemplify the convergence between nanotechnology and artificial intelligence (AI). These are related to sensors to mimic the five human senses, and integration of data from varied sources and natures into an intelligent system to manage autonomous services, as in a train station.

Artificial Intelligence Marketing (AIM) for Enhancing Customer Relationships

Based on the literature, we present an artificial intelligence marketing (AIM) framework that enables autonomous machines to receive big data and information, use artificial intelligence (AI) to create knowledge, and then disseminate and apply the knowledge to enhance customer relationships in a knowledge-based environment. To develop the AIM framework, we bring together and curate a wide range of relevant literatures including real-life examples and cases, and then understand how these literatures contribute to the framework in this research topic. We explain the AIM framework from the interdisciplinary perspective, which is an important role of both the artificial intelligence and marketing academia. The AIM framework includes three main components, including the pre-processor, the main processor, and the memory storage. The main processor, which is the key component, uses AI to process structured data processed by pre-processor in order to make real-time decisions and reasonings. The AI approach is characterized by its hypothetical abilities, learning paradigms, and operation modes with human. The strategic use of the developed AIM framework based on the literature to enhance customer relationships, including customer trust, satisfaction, commitment, engagement, and loyalty, is presented. Finally, future potential investigations are presented to drive forward this interdisciplinary research topic.

Testbed for LoRaWAN Security: Design and Validation through Man-in-the-Middle Attacks Study

The low-power wide-area (LPWA) technologies, which enable cost and energy-efficient wireless connectivity for massive deployments of autonomous machines, have enabled and boosted the development of many new Internet of things (IoT) applications; however, the security of LPWA technologies in general, and specifically those operating in the license-free frequency bands, have received somewhat limited attention so far. This paper focuses specifically on the security and privacy aspects of one of the most popular license-free-band LPWA technologies, which is named LoRaWAN. The paper’s key contributions are the details of the design and experimental validation of a security-focused testbed, based on the combination of software-defined radio (SDR) and GNU Radio software with a standalone LoRaWAN transceiver. By implementing the two practical man-in-the-middle attacks (i.e., the replay and bit-flipping attacks through intercepting the over-the-air activation procedure by an external to the network attacker device), we demonstrate that the developed testbed enables practical experiments for on-air security in real-life conditions. This makes the designed testbed perspective for validating the novel security solutions and approaches and draws attention to some of the relevant security challenges extant in LoRaWAN.

A Zero-Trust Methodology for Security of Complex Systems With Machine Learning Components

Fuelled by recent technological advances, Machine Learning (ML) is being introduced to safety and security-critical applications like defence systems, financial systems, and autonomous machines. ML components can be used either for processing input data and/or for decision making. The response time and success rate demands are very high and this means that the deployed training algorithms often produce complex models that are not readable and verifiable by humans (like multi layer neural networks). Due to the complexity of these models, achieving complete testing coverage is in most cases not realistically possible. This raises security threats related to the ML components presenting unpredictable behavior due to malicious manipulation (backdoor attacks). This paper proposes a methodology based on established security principles like Zero-Trust and defence-in-depth to help prevent and mitigate the consequences of security threats including ones emerging from ML-based components. The methodology is demonstrated on a case study of an Unmanned Aerial Vehicle (UAV) with a sophisticated Intelligence, Surveillance, and Reconnaissance (ISR) module.

Development of Sensing Algorithms for Object Tracking and Predictive Safety Evaluation of Autonomous Excavators

This article presents the sensing and safety algorithms for autonomous excavators operating on construction sites. Safety is a key concern for autonomous construction to reduce collisions and machinery damage. Taking this point into consideration, our study deals with LiDAR data processing that allows for object detection, motion tracking/prediction, and track management, as well as safety evaluation in terms of potential collision risk. In the safety algorithm developed in this study, potential collision risks can be evaluated based on information from excavator working areas, predicted states of detected objects, and calculated safety indices. Experiments were performed using a modified mini hydraulic excavator with Velodyne VLP-16 LiDAR. Experimental validations prove that the developed algorithms are capable of tracking objects, predicting their future states, and assessing the degree of collision risks with respect to distance and time. Hence, the proposed algorithms can be applied to diverse autonomous machines for safety enhancement.

Post Pandemic World and Challenges for E-Governance Framework

Emergence of cloud computing and rapid development of automation in terms of Internet of Things (IoT), it was evident in the wake of 2019 that world is gradually moving towards a complete digital system from individual to business and ultimately government level. Technology advancements start expanding from smart devices to smart cities, autonomous machines to autonomous cars and expert systems to intelligent robots. These advancements are supported by the swiftly growing communication and networking domain where 5G is introducing new range of expansion and more freedom to creativity and novelty. This new regime of advanced technologies has made the foundation on live digital or internet based structures that transformed into cloud computing with swiftly growing facilities and innovative competitors. Cloud computing penetrates in almost all digital domains from individuals to corporates and to governments as well due to its versatile services, economic modelling and ease of accessibility.