With the rapid rise in Software Supply Chain (SSC) attacks, organisations need thorough and trustworthy visibility over the entire SSC of their software inventory to detect risks early and identify compromised assets rapidly in the event of an SSC attack. One way to achieve such visibility is through SSC metadata, machine-readable and authenticated documents describing an artefact’s lifecycle. Adopting SSC metadata requires organisations to procure or develop a Software Supply Chain Metadata Management system (SCM2), a suite of software tools for performing life cycle activities of SSC metadata documents such as creation, signing, distribution, and consumption. Selecting or developing an SCM2 is challenging due to the lack of a comprehensive domain model and architectural blueprint to aid practitioners in navigating the vast design space of SSC metadata terminologies, frameworks, and solutions. This paper addresses the above-mentioned challenge by presenting an empirically grounded Reference Architecture (RA) comprising of a domain model and an architectural blueprint for SCM2 systems. Our proposed RA is constructed systematically on an empirical foundation built with industry-driven and peer-reviewed SSC security frameworks. Our theoretical evaluation, which consists of an architectural mapping of five prominent SSC security tools on the RA, ensures its validity and applicability, thus affirming the proposed RA as an effective framework for analysing existing SCM2 solutions and guiding the engineering of new SCM2 systems.

The construction of an interactive dashboard involves deciding on what information to present and how to display it and implementing those design decisions to create an operational dashboard. Traditionally, a dashboard's design is implied in the deployed dashboard rather captured explicitly as a digital artifact, preventing it from being backed up, version-controlled, and shared. Moreover, practitioners have to implement this implicit design manually by coding or configuring it on a dashboard platform. This paper proposes Mod2Dash, a software framework that enables practitioners to capture their dashboard designs as models and generate operational dashboards automatically from these models. The framework also provides a GUI-driven customization approach for practitioners to fine-tune the auto-generated dashboards and update their models. With these abilities, Mod2Dash enables practitioners to rapidly prototype and deploy dashboards for both operational and research purposes. We evaluated the framework's effectiveness in a case study on cyber security visualization for decision support. A proof-of-concept of Mod2Dash was employed to model and reconstruct 31 diverse real-world cyber security dashboards. A human-assisted comparison between the Mod2Dash-generated dashboards and the baseline dashboards shows a close matching, indicating the framework's effectiveness for real-world scenarios.

Blockchain technologies have been increasingly adopted by enterprises to increase operational efficiency and enable new business models. These enterprise blockchain applications generally run on dedicated blockchain networks due to regulations and security requirements. The design process of these networks involves many decisions and trade-offs that impact security, governance, and performance of applications that run on them. The challenge is further exacerbated by the lack of a common architecture and concept map to communicate about blockchain networks, as blockchain technologies tend to use different concepts and architecture. This paper presents a concept map, an anatomy and the principal dimensions of the design space of blockchain networks. We applied the proposed design space in a case study about designing and deploying a blockchain network for an ad-hoc IoT infrastructure. We found that the design space brought structure to the design process and the analysis of design alternatives. The presented concept map, anatomy and design space are intended to help improve the blockchain network design practice and lay a foundation for future research on the design process and deployment automation of blockchain networks.

The content of the Internet of Things (IoT), notably sensor data and virtual representation of physical devices, has been increasingly delivered via Web protocols and available on the World Wide Web (WWW). Internet of Things Search Engine (IoTSE) systems are catalytic to utilize this influx of data. They enable users to discover and retrieve relevant IoT content. While a general IoTSE system - the next "Google" - is beyond the horizon due to the vast diversity of IoT content and types of queries for them, specific IoTSE systems that target subsets of query types and IoT infrastructure are feasible and beneficial. A component-based engineering approach, in which prior IoTSE systems and research prototypes are reassembled as building blocks for new IoTSE systems, could be a time-and cost-effective solution to engineering IoTSE systems. This paper presents the design, implementation, and evaluation of a framework to facilitate a component-based approach to engineering IoTSE systems. As an evaluation, we developed eight IoTSE components and composed them into eight proof-of-concept IoTSE systems, using a reference implementation of the proposed framework. An analysis on Source Line of Code (SLOC) revealed that the complexity handled transparently by the IoTSE framework could account for over 90% of the code base of a simple IoTSE system.

How to accurately forecast seasonal time series is very important for many business area such as marketing decision, planning production and profit estimation. In this paper, we propose a weighted gradient Radial Basis Function Network based AutoRegressive (WGRBF-AR) model for modeling and predicting the nonlinear and non-stationary seasonal time series. This WGRBF-AR model is a synthesis of the weighted gradient RBF network and the functional-coefficient autoregressive (FAR) model through using the WGRBF networks to approximate varying coefficients of FAR model. It not only takes the advantages of the FAR model in nonlinear dynamics description but also inherits the capability of the WGRBF network to deal with non-stationarity. We test our model using ten-years retail sales data on five different commodity in US. The results demonstrate that the proposed WGRBF-AR model can achieve competitive prediction accuracy compared with the state-of-the-art.

In the era of the Internet of Things, enormous number of sensors have been deployed in different locations, generating massive time-series sensory data with geo-tags. However, such sensory readings are easily missing due to various reasons such as the hardware malfunction, connection errors, and data corruption. This paper focuses on this challenge--how to accurately yet efficiently recover the missing values for corrupted time-series sensor data with geo-stamps. In this paper, we formulate the time-series sensor data as a 3-order tensor that naturally preserves sensors' temporal and spatial dependencies. Then we exploit its low-rank and sparse-noise structures by drawing upon recent advances in Robust Principal Component Analysis (RPCA) and tensor completion theory. The main novelty of this paper lies in that, we design a highly efficient optimization method that combines the alternating direction method of multipliers and accelerated proximal gradient to recover the data tensor. Besides testing our method using the synthetic data, we also design a real-world testbed by passive RFID (RadioFrequency IDentification) sensors. The results demonstrate the effectiveness and accuracy of our approach.

Semantic Ambiguation refers to the disagreement in meaning of terms used by parties in communication due to polysemy, leading to increased complexity and lesser accuracy in information integration, migration, retrieval and related activities. Semantic Disambiguation can be performed through establishing and reconciling relative positions of parties in communication by matching their terms' meanings to a common domain-specific ontology -- a knowledge representation showing concepts, organized into domains, and relationships between them. In this paper, based on the wide existence and limitations of established hierarchical ontology in a form of Classification Schemes, we proposed a novel ontology structure combining the structured nature of hierarchy with expressive capability of graphs, called Graph-embedded Tree (GeT), and a novel approach to construct a GeT-based Ontology. Evaluation was performed on United States Patent Classification System (USPC); the results showed that the information retrieval backed by GeT-based ontology yields better disambiguation capability than other typical patent search methods.

The world is going mobile. Explosive growth in popularity and functionality of mobile-computing allows more personal and professional tasks to be done on these portable devices, creating enormous opportunity for end-user development (EUD) -- a set of methods and techniques that allow application users to create and modify software products to support their works and hobbies. End-user developers face the same engineering challenges as professional developers, including (i) understanding their requirements, (ii) making design decisions, (iii) building application and (iv) debugging application; however, most of existing works on end-user development only focus on solving design and implementation problems. In this paper, we addressed the requirement problem of end-user developers by proposing a scenario-based iterative process for finding application requirements based on identifying different usage scenarios with the support of knowledge about background of the application topic. We evaluated our process with one comparative study and two case studies.