Automated counter-terrorism

We present a holistic systems view of automated intelligence analysis for counter-terrorism with focus on the behavioural attributes of terrorist groups

Automated border control systems: biometric challenges and research trends

Automated Border Control (ABC) systems automatically verify the travelers\u2019 identity using their biometric information, without the need of a manual check, by comparing the data stored in the electronic document (e.g., the e-Passport) with a live sample captured during the crossing of the border. In this paper, the hardware and software components of the biometric systems used in ABC systems are described, along with the latest challenges and research trends

Supporting Cyber-Physical Systems with Wireless Sensor Networks: An Outlook of Software and Services

Sensing, communication, computation and control technologies are the essential building blocks of a cyber-physical system (CPS). Wireless sensor networks (WSNs) are a way to support CPS as they provide fine-grained spatial-temporal sensing, communication and computation at a low premium of cost and power. In this article, we explore the fundamental concepts guiding the design and implementation of WSNs. We report the latest developments in WSN software and services for meeting existing requirements and newer demands; particularly in the areas of: operating system, simulator and emulator, programming abstraction, virtualization, IP-based communication and security, time and location, and network monitoring and management. We also reflect on the ongoing efforts in providing dependable assurances for WSN-driven CPS. Finally, we report on its applicability with a case-study on smart buildings

Attempts to share information between public sector organisations over time: A case-based exploration of value conflicts

Despite the importance of inter-organisational information sharing (IOIS) in the public sector, such endeavours often fail. Existing research has shown that the values held by collaborating organisations are one important factor affecting these kinds of initiatives. However, research has sought only to a limited extent to address how value conflicts come into play over time. Therefore, this paper aims to explore how conflicting values shape an inter-organisational information-sharing practice in the public sector over time. Using the local/global network framework, we analyse four years’ worth of information sharing in an inter-organisational advisory group in the context of Swedish nuclear waste management. We conclude that different value conflicts are emphasised to different extents at different points in time. That is, values do not uniformly affect IOIS activities, and such conflicts over time reduce the set of potential IOIS activities. We also conclude that when IOIS activities are driven by an individual organisation’s values, individual value rational activities may co-exist with a dysfunctional long-term IOIS practice.publishedVersio

Deploying AI Applications to Multiple Environments: Coping with Environmental, Data, and Predictive Variety

Deploying Artificial Intelligence (AI) proves to be challenging and resource-intensive in practice. To increase the economic value of AI deployments, organizations seek to deploy and reuse AI applications in multiple environments (e.g., different firm branches). This process involves generalizing an existing AI application to a new environment, which is typically not seamlessly possible. Despite its practical relevance, research lacks a thorough understanding of how organizations approach the deployment of AI applications to multiple environments. Therefore, we conduct an explorative multiple-case study with four computer vision projects as part of an ongoing research effort. Our preliminary findings suggest that new environments introduce variety, which is mirrored in the data produced in these environments and the required predictive capabilities. Organizations are found to cope with variety during AI deployment by 1) controlling variety in the environment, 2) capturing variety via data collection, and 3) adapting to variety by adjusting AI models

ERP implementation methodologies and frameworks: a literature review

Enterprise Resource Planning (ERP) implementation is a complex and vibrant process, one that involves a combination of technological and organizational interactions. Often an ERP implementation project is the single largest IT project that an organization has ever launched and requires a mutual fit of system and organization. Also the concept of an ERP implementation supporting business processes across many different departments is not a generic, rigid and uniform concept and depends on variety of factors. As a result, the issues addressing the ERP implementation process have been one of the major concerns in industry. Therefore ERP implementation receives attention from practitioners and scholars and both, business as well as academic literature is abundant and not always very conclusive or coherent. However, research on ERP systems so far has been mainly focused on diffusion, use and impact issues. Less attention has been given to the methods used during the configuration and the implementation of ERP systems, even though they are commonly used in practice, they still remain largely unexplored and undocumented in Information Systems research. So, the academic relevance of this research is the contribution to the existing body of scientific knowledge. An annotated brief literature review is done in order to evaluate the current state of the existing academic literature. The purpose is to present a systematic overview of relevant ERP implementation methodologies and frameworks as a desire for achieving a better taxonomy of ERP implementation methodologies. This paper is useful to researchers who are interested in ERP implementation methodologies and frameworks. Results will serve as an input for a classification of the existing ERP implementation methodologies and frameworks. Also, this paper aims also at the professional ERP community involved in the process of ERP implementation by promoting a better understanding of ERP implementation methodologies and frameworks, its variety and history

MATURITY AND READINESS MODELS FOR RESPONSIBLE ARTIFICIAL INTELLIGENCE (RAI): A SYSTEMATIC LITERATURE REVIEW

The development and deployment of Artificial intelligence (AI) in organizations is of growing interest to the Information systems (IS) discipline. This can be approached from a sociotechnical perspective contributing to managing the unintended outcomes of AI while extending AI use boundaries. This paper presents the findings from a systematic literature review on organizational maturity and readiness for AI development and development. A key result is that extant research has lost sight of AI systems\u27 humanistic and ethical aspects, and principles related to responsible AI are not sufficiently defined. This is a hurdle because principles for responsible AI are fundamental for AI development and deployment ensuring long-term benefits. Drawing from the literature review findings, we provide a conceptual maturity model with two main dimensions (responsible and instrumental), twelve conditions and thirty factors. The maturity and readiness factors for responsible AI are deduced from synthesizing 35 articles in related literature. Specifically, the paper identified six capabilities for responsible AI: AI model, Cooperative AI, ethical awareness, laws & regulations, data governance and continuous improvement; six instrumental capabilities were also identified: strategic alignment, technology, culture, data management, financial and human resource management

A study on IoT-related security issues, challenges, and solutions.

The Internet of Things is now being developed to be the most cutting-edge and user-centric technology in the works. Raising both an individual\u27s and society\u27s level of life is the goal of this endeavour. When a technology advances, it always acquires certain flaws, which are always open to being attacked and taken advantage of in some manner. In this work, the problems posed by the Internet of Things (IoT) based on the fundamental security principles of confidentiality, integrity, and availability are discussed. It has also been discussed how an overview of the security restrictions, requirements, processes, and solutions implemented for the challenges generated in secured communication inside the IoT ecosystem. In this paper, the vulnerabilities of the underlying Internet of Things network are brought to light, and many security concerns on multiple tiers of the Internet of Things ecosystem have been examined. Based on the findings of our research into the vulnerabilities that are now present, a variety of potential solutions have been proposed in order to solve the ongoing problems that are plaguing the IoT ecosystem. In addition to that, it provides an overview of the various protocols that are used for security in IoT

The Design of a System for Online Psychosocial Care: Balancing Privacy and Accountability in Sensitive Online Healthcare Environments

The design of sensitive online healthcare systems must balance the requirements of privacy and accountability for the good of individuals, organizations, and society. Via a design science research approach, we build and evaluate a sophisticated software system for the online provision of psychosocial healthcare to distributed and vulnerable populations. Multidisciplinary research capabilities are embedded within the system to investigate the effectiveness of online treatment protocols. Throughout the development cycles of the system, we build an emergent design theory of scrutiny that applies a multi-layer protocol to support governance of privacy and accountability in sensitive online applications. The design goal is to balance stakeholder privacy protections with the need to provide for accountable interventions in critical and well-defined care situations. The research implications for the development and governance of online applications in numerous privacy-sensitive application areas are explore

Spatio-temporal distribution of negative emotions in New York City after a natural disaster as seen in social media

Disasters have substantial consequences for population mental health. We used Twitter to (1) extract negative emotions indicating discomfort in New York City (NYC) before, during, and after Superstorm Sandy in 2012. We further aimed to (2) identify whether pre- or peri-disaster discomfort were associated with peri- or post-disaster discomfort, respectively, and to (3) assess geographic variation in discomfort across NYC census tracts over time. Our sample consisted of 1,018,140 geo-located tweets that were analyzed with an advanced sentiment analysis called "Extracting the Meaning Of Terse Information in a Visualization of Emotion" (EMOTIVE). We calculated discomfort rates for 2137 NYC census tracts, applied spatial regimes regression to find associations of discomfort, and used Moran's I for spatial cluster detection across NYC boroughs over time. We found increased discomfort, that is, bundled negative emotions after the storm as compared to during the storm. Furthermore, pre- and peri-disaster discomfort was positively associated with post-disaster discomfort; however, this association was different across boroughs, with significant associations only in Manhattan, the Bronx, and Queens. In addition, rates were most prominently spatially clustered in Staten Island lasting pre- to post-disaster. This is the first study that determined significant associations of negative emotional responses found in social media posts over space and time in the context of a natural disaster, which may guide us in identifying those areas and populations mostly in need for care