The increasing complexity of modern interlocking poses a major challenge to ensuring railway safety. This calls for application of formal methods for assurance and verification of their safety. We have developed an industry-strength toolset, called SafeCap, for formal verification of interlockings. Our aim was to overcome the main barriers in deploying formal methods in industry. The approach proposed verifies interlocking data developed by signalling engineers in the ways they are designed by industry. It ensures fully-automated verification of safety properties using the state of the art techniques (automated theorem provers and solvers), and provides diagnostics in terms of the notations used by engineers. In the last two years SafeCap has been successfully used to verify 26 real-world mainline interlockings, developed by different suppliers and design offices. SafeCap is currently used in an advisory capacity, supplementing manual checking and testing processes by providing an additional level of verification and enabling earlier identification of errors. We are now developing a safety case to support its use as an alternative to some of these activities.
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Hypertext Transfer Protocol Secure (HTTPS) protocol has become integral part of the modern internet technology. It is currently the primary protocol for commercialized web applications. It can provide a fast, secure connection with a certain level of privacy and integrity, and it has become a basic assumption on most web services on the internet. However, HTTPS cannot provide security assurances on the request data in compute, so the computing environment remains uncertain risks and vulnerabilities. A hardware-based trusted execution environment (TEE) such as Intel Software Guard Extension (SGX) provides in-memory encryption to help protect the runtime computation to reduce risks of illegal leaking or modifying private information. The central concept of SGX enables the computation happening inside the enclave, a protected environment that encrypts the codes and data pertaining to a security-sensitive computation. In addition, SGX provides provide security assurances via remote attestation to the web client, including TCB identity, vendor identity and verification identity. Here we propose a HTTP protocol, called HTTPS Attestable (HTTPA), by including remote attestation process onto the HTTPS protocol to address the privacy and security concerns on web and the access over the Internet. With HTTPA, we can provide security assurances to establish trustworthiness with web services and ensure integrity of request handling for web users. We expect that remote attestation will become a new trend adopted to reduce web services security risks, and propose the HTTPA protocol to unify the web attestation and accessing services in a standard and efficient way.
Threat modeling and risk assessments are common ways to identify, estimate, and prioritize risk to national, organizational, and individual operations and assets. Several threat modeling and risk assessment approaches have been proposed prior to the advent of the Internet of Things (IoT) that focus on threats and risks in information technology (IT). Due to shortcomings in these approaches and the fact that there are significant differences between the IoT and IT, we synthesize and adapt these approaches to provide a threat modeling framework that focuses on threats and risks in the IoT. In doing so, we develop an IoT attack taxonomy that describes the adversarial assets, adversarial actions, exploitable vulnerabilities, and compromised properties that are components of any IoT attack. We use this IoT attack taxonomy as the foundation for designing a joint risk assessment and maturity assessment framework that is implemented as an interactive online tool. The assessment framework this tool encodes provides organizations with specific recommendations about where resources should be devoted to mitigate risk. The usefulness of this IoT framework is highlighted by case study implementations in the context of multiple industrial manufacturing companies, and the interactive implementation of this framework is available at //iotrisk.andrew.cmu.edu.
Humankind faces many existential threats, but has limited resources to mitigate them. Choosing how and when to deploy those resources is, therefore, a fateful decision. Here, I analyze the priority for allocating resources to mitigate the risk of superintelligences. Part I observes that a superintelligence unconnected to the outside world (de-efferented) carries no threat, and that any threat from a harmful superintelligence derives from the peripheral systems to which it is connected, e.g., nuclear weapons, biotechnology, etc. Because existentially-threatening peripheral systems already exist and are controlled by humans, the initial effects of a superintelligence would merely add to the existing human-derived risk. This additive risk can be quantified and, with specific assumptions, is shown to decrease with the square of the number of humans having the capability to collapse civilization. Part II proposes that biotechnology ranks high in risk among peripheral systems because, according to all indications, many humans already have the technological capability to engineer harmful microbes having pandemic spread. Progress in biomedicine and computing will proliferate this threat. ``Savant'' software that is not generally superintelligent will underpin much of this progress, thereby becoming the software responsible for the highest and most imminent existential risk -- ahead of hypothetical risk from superintelligences. The analysis concludes that resources should be preferentially applied to mitigating the risk of peripheral systems and savant software. Concerns about superintelligence are at most secondary, and possibly superfluous.
The decoding of brain signals recorded via, e.g., an electroencephalogram, using machine learning is key to brain-computer interfaces (BCIs). Stimulation parameters or other experimental settings of the BCI protocol typically are chosen according to the literature. The decoding performance directly depends on the choice of parameters, as they influence the elicited brain signals and optimal parameters are subject-dependent. Thus a fast and automated selection procedure for experimental parameters could greatly improve the usability of BCIs. We evaluate a standalone random search and a combined Bayesian optimization with random search in a closed-loop auditory event-related potential protocol. We aimed at finding the individually best stimulation speed -- also known as stimulus onset asynchrony (SOA) -- that maximizes the classification performance of a regularized linear discriminant analysis. To make the Bayesian optimization feasible under noise and the time pressure posed by an online BCI experiment, we first used offline simulations to initialize and constrain the internal optimization model. Then we evaluated our approach online with 13 healthy subjects. We could show that for 8 out of 13 subjects, the proposed approach using Bayesian optimization succeeded to select the individually optimal SOA out of multiple evaluated SOA values. Our data suggests, however, that subjects were influenced to very different degrees by the SOA parameter. This makes the automatic parameter selection infeasible for subjects where the influence is limited. Our work proposes an approach to exploit the benefits of individualized experimental protocols and evaluated it in an auditory BCI. When applied to other experimental parameters our approach could enhance the usability of BCI for different target groups -- specifically if an individual disease progress may prevent the use of standard parameters.
We review key considerations, practices, and areas for future work aimed at the responsible development and fielding of AI technologies. We describe critical challenges and make recommendations on topics that should be given priority consideration, practices that should be implemented, and policies that should be defined or updated to reflect developments with capabilities and uses of AI technologies. The Key Considerations were developed with a lens for adoption by U.S. government departments and agencies critical to national security. However, they are relevant more generally for the design, construction, and use of AI systems.
Reconfigurable intelligent surface has attracted the attention of academia and industry as soon as it appears because it can flexibly manipulate the electromagnetic characteristics of wireless channel. Especially in the past one or two years, RIS has been developing rapidly in academic research and industry promotion and is one of the key candidate technologies for 5G-Advanced and 6G networks. RIS can build a smart radio environment through its ability to regulate radio wave transmission in a flexible way. The introduction of RIS may create a new network paradigm, which brings new possibilities to the future network, but also leads to many new challenges in the technological and engineering applications. This paper first introduces the main aspects of RIS enabled wireless communication network from a new perspective, and then focuses on the key challenges faced by the introduction of RIS. This paper briefly summarizes the main engineering application challenges faced by RIS networks, and further analyzes and discusses several key technical challenges among of them in depth, such as channel degradation, network coexistence, network coexistence and network deployment, and proposes possible solutions.
Dengue fever has been considered to be one of the global public health problems of the twenty-first century, especially in tropical and subtropical countries of the global south. The high morbidity and mortality rates of Dengue fever impose a huge economic and health burden for middle and low-income countries. It is so prevalent in such regions that enforcing a granular level of surveillance is quite impossible. Therefore, it is crucial to explore an alternative cost-effective solution that can provide updates of the ongoing situation in a timely manner. In this paper, we explore the scope and potential of a local newspaper-based dengue surveillance system, using well-known data-mining techniques, in Bangladesh from the analysis of the news contents written in the native language. In addition, we explain the working procedure of developing a novel database, using human-in-the-loop technique, for further analysis, and classification of dengue and its intervention-related news. Our classification method has an f-score of 91.45%, and matches the ground truth of reported cases quite closely. Based on the dengue and intervention-related news, we identified the regions where more intervention efforts are needed to reduce the rate of dengue infection. A demo of this project can be accessed at: //erdos.dsm.fordham.edu:3009/
The Internet of Things (IoT) is affecting national innovation ecosystems and the approach of organizations to innovation and how they create and capture value in everyday business activities. The Internet of Things (IoT), is disruptive, and it will change the manner in which human resources are developed and managed, calling for a new and adaptive human resource development approach. The Classical Internet communication form is human-human. The prospect of IoT is that every object will have a unique way of identification and can be addressed so that every object can be connected. The communication forms will expand from human-human to human-human, human-thing, and thing-thing. This will bring a new challenge to how Human Resource Development (HRD) is practiced. This paper provides an overview of the Internet of Things and conceptualizes the role of HRD in the age of the Internet of Things. Keywords:
The tourism industry is increasingly influenced by the evolution of information and communication technologies (ICT), which are revolutionizing the way people travel. In this work we want to nvestigate the use of innovative IT technologies by DMOs (Destination Management Organizations), focusing on blockchain technology, both from the point of view of research in the field, and in the study of the most relevant software projects. In particular, we intend to verify the benefits offered by these IT tools in the management and monitoring of a destination, without forgetting the implications for the other stakeholders involved. These technologies, in fact, can offer a wide range of services that can be useful throughout the life cycle of the destination.
Machine Learning models become increasingly proficient in complex tasks. However, even for experts in the field, it can be difficult to understand what the model learned. This hampers trust and acceptance, and it obstructs the possibility to correct the model. There is therefore a need for transparency of machine learning models. The development of transparent classification models has received much attention, but there are few developments for achieving transparent Reinforcement Learning (RL) models. In this study we propose a method that enables a RL agent to explain its behavior in terms of the expected consequences of state transitions and outcomes. First, we define a translation of states and actions to a description that is easier to understand for human users. Second, we developed a procedure that enables the agent to obtain the consequences of a single action, as well as its entire policy. The method calculates contrasts between the consequences of a policy derived from a user query, and of the learned policy of the agent. Third, a format for generating explanations was constructed. A pilot survey study was conducted to explore preferences of users for different explanation properties. Results indicate that human users tend to favor explanations about policy rather than about single actions.
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