The construction sector constitutes one of the most important sectors in the economy of any country. Many construction projects experience time and cost overruns due to the wrong choice of contractors. In this paper, the feedback integrated multi-criteria group decision support model for contractor selection was proposed. The proposed model consists of two modules; technical evaluation module and financial evaluation module. The technical evaluation module is employed to screen out the contractors to a smaller set of acceptable contractors and the functionality of the module is based on the Fuzzy Analytic Hierarchy Process (FAHP).

False alerts due to misconfigured/ compromised IDS in ICS networks can lead to severe economic and operational damage. To solve this problem, research has focused on leveraging deep learning techniques that help reduce false alerts. However, a shortcoming is that these works often require or implicitly assume the physical and cyber sensors to be trustworthy. Implicit trust of data is a major problem with using artificial intelligence or machine learning for CPS security, because during critical attack detection time they are more at risk, with greater likelihood and impact, of also being compromised. To address this shortcoming, the problem is reframed on how to make good decisions given uncertainty. Then, the decision is detection, and the uncertainty includes whether the data used for ML-based IDS is compromised. Thus, this work presents an approach for reducing false alerts in CPS power systems by dealing uncertainty without the knowledge of prior distribution of alerts. Specifically, an evidence theoretic based approach leveraging Dempster Shafer combination rules are proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from various supervised-learning classifiers. Using this model, a location-cum-domain based fusion framework is proposed and evaluated with different combination rules, that fuse multiple evidence from inter-domain and intra-domain sensors. The approach is demonstrated in a cyber-physical power system testbed with Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, plausibility, belief, pignistic, etc. metrics as decision functions are considered. To improve the performance, a multi-objective based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function.

In 2022 the covid-19 pandemic will continue to impact our lives in many ways. This means that we will continue to see an accelerated rate of digitization and virtualization of business and society. However, as we move into a new year, the need for sustainability, ever-increasing data volumes, and increasing compute and network speeds will begin to regain their status as the most important drivers of digital transformation. For many individuals and organizations, the most important lesson of the last two years or so has been that truly transformative change isn't as difficult to implement as might have once been thought, if the motivation is there! As a society, we will undoubtedly continue to harness this newfound openness to flexibility, agility, and innovative thinking, as the focus shifts from merely attempting to survive in a changing world to thriving in it. With that in mind, here are my predictions for the specific trends that are likely to have the biggest impact in 2022.

Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among others. Deep learning methods are particularly promising for modeling many remote sensing tasks given the success of deep neural networks in similar computer vision tasks and the sheer volume of remotely sensed imagery available. However, the variance in data collection methods and handling of geospatial metadata make the application of deep learning methodology to remotely sensed data nontrivial. For example, satellite imagery often includes additional spectral bands beyond red, green, and blue and must be joined to other geospatial data sources that can have differing coordinate systems, bounds, and resolutions. To help realize the potential of deep learning for remote sensing applications, we introduce TorchGeo, a Python library for integrating geospatial data into the PyTorch deep learning ecosystem. TorchGeo provides data loaders for a variety of benchmark datasets, composable datasets for generic geospatial data sources, samplers for geospatial data, and transforms that work with multispectral imagery. TorchGeo is also the first library to provide pre-trained models for multispectral satellite imagery (e.g. models that use all bands from the Sentinel 2 satellites), allowing for advances in transfer learning on downstream remote sensing tasks with limited labeled data. We use TorchGeo to create reproducible benchmark results on existing datasets and benchmark our proposed method for preprocessing geospatial imagery on-the-fly. TorchGeo is open-source and available on GitHub: https://github.com/microsoft/torchgeo.

Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high energy particle physics. In particular, particle tracking data is naturally represented as a graph by identifying silicon tracker hits as nodes and particle trajectories as edges; given a set of hypothesized edges, edge-classifying GNNs identify those corresponding to real particle trajectories. In this work, we adapt the physics-motivated interaction network (IN) GNN toward the problem of particle tracking in pileup conditions similar to those expected at the high-luminosity Large Hadron Collider. Assuming idealized hit filtering at various particle momenta thresholds, we demonstrate the IN's excellent edge-classification accuracy and tracking efficiency through a suite of measurements at each stage of GNN-based tracking: graph construction, edge classification, and track building. The proposed IN architecture is substantially smaller than previously studied GNN tracking architectures; this is particularly promising as a reduction in size is critical for enabling GNN-based tracking in constrained computing environments. Furthermore, the IN may be represented as either a set of explicit matrix operations or a message passing GNN. Efforts are underway to accelerate each representation via heterogeneous computing resources towards both high-level and low-latency triggering applications.

The design or simulation analysis of special equipment products must follow the national standards, and hence it may be necessary to repeatedly consult the contents of the standards in the design process. However, it is difficult for the traditional question answering system based on keyword retrieval to give accurate answers to technical questions. Therefore, we use natural language processing techniques to design a question answering system for the decision-making process in pressure vessel design. To solve the problem of insufficient training data for the technology question answering system, we propose a method to generate questions according to a declarative sentence from several different dimensions so that multiple question-answer pairs can be obtained from a declarative sentence. In addition, we designed an interactive attention model based on a bidirectional long short-term memory (BiLSTM) network to improve the performance of the similarity comparison of two question sentences. Finally, the performance of the question answering system was tested on public and technical domain datasets.

Graphs are ubiquitous in many applications, such as social networks, knowledge graphs, smart grids, etc.. Graph neural networks (GNN) are the current state-of-the-art for these applications, and yet remain obscure to humans. Explaining the GNN predictions can add transparency. However, as many graphs are not static but continuously evolving, explaining changes in predictions between two graph snapshots is different but equally important. Prior methods only explain static predictions or generate coarse or irrelevant explanations for dynamic predictions. We define the problem of explaining evolving GNN predictions and propose an axiomatic attribution method to uniquely decompose the change in a prediction to paths on computation graphs. The attribution to many paths involving high-degree nodes is still not interpretable, while simply selecting the top important paths can be suboptimal in approximating the change. We formulate a novel convex optimization problem to optimally select the paths that explain the prediction evolution. Theoretically, we prove that the existing method based on Layer-Relevance-Propagation (LRP) is a special case of the proposed algorithm when an empty graph is compared with. Empirically, on seven graph datasets, with a novel metric designed for evaluating explanations of prediction change, we demonstrate the superiority of the proposed approach over existing methods, including LRP, DeepLIFT, and other path selection methods.

In this article we drill into why Monitoring & Targeting often doesn't deliver on its true savings potential and how Machine Learning can overcome this. Net Zero has never been a bigger topic. Amongst the sea of Science Based Targets and long term reduction strategies, it's clear that there is no single answer to reaching net zero and for most organisations it's a marathon not a sprint. However many businesses are looking again at what they can do in the short term to make an immediate impact. Reducing demand is the first step of any energy hierarchy and utilising data in a commercially-viable way is critical to this.

GCube has launched a new data-powered insurance offering to support the growth of the renewable energy industry, with support from Clire, a company dedicated to maximizing project returns from renewable energy assets. The offering will leverage AI-led analytics and data sets to offer enhanced terms and reduced premiums for wind and solar operating companies. By having Clir onboard a wind portfolio's data set onto its platform, GCube will aim to uncover an asset's meteorological and operational loading, overall component health and reliability, and the impact of current operations and maintenance. These insights will give GCube clarity on its underwriting pricing, and offer more competitive terms where operating projects model with lower risk factors. "Insuring renewable energy has been a tumultuous process over the last decade," said Fraser McLachlan, Chief Executive Officer, GCube Insurance Inc. "Claims from equipment failure, natural catastrophe loss and contractor error have forced some underwriters to exit the market. To continue to offer insurance at sustainable rates for clients, we need to have deeper insights into the risk of failure and operational management of renewable energy equipment."

… to work together in identifying artificial intelligence (AI) applications that accelerate reaching the UN Sustainable Development Goals.