Artificial intelligence detects awareness of functional relation with the environment in 3 month old babies

A recent experiment probed how purposeful action emerges in early life by manipulating infants’ functional connection to an object in the environment (i.e., tethering an infant’s foot to a colorful mobile). Vicon motion capture data from multiple infant joints were used here to create Histograms of Joint Displacements (HJDs) to generate pose-based descriptors for 3D infant spatial trajectories. Using HJDs as inputs, machine and deep learning systems were tasked with classifying the experimental state from which snippets of movement data were sampled. The architectures tested included k-Nearest Neighbour (kNN), Linear Discriminant Analysis (LDA), Fully connected network (FCNet), 1D-Convolutional Neural Network (1D-Conv), 1D-Capsule Network (1D-CapsNet), 2D-Conv and 2D-CapsNet. Sliding window scenarios were used for temporal analysis to search for topological changes in infant movement related to functional context. kNN and LDA achieved higher classification accuracy with single joint features, while deep learning approaches, particularly 2D-CapsNet, achieved higher accuracy on full-body features. For each AI architecture tested, measures of foot activity displayed the most distinct and coherent pattern alterations across different experimental stages (reflected in the highest classification accuracy rate), indicating that interaction with the world impacts the infant behaviour most at the site of organism~world connection

Coordination Dynamics meets Active Inference and Artificial Intelligence (CD + AI2):A multi-pronged approach to understanding the dynamics of brain and the emergence of conscious agency

How do humans discover their ability to act on the world? By tethering a baby’s foot to a mobile (Fig. 1a) and measuring the motion of both in 3D, we explore how babies begin to make sense of their coordinative relationship with the world and realize their ability to make things happen (N= 16; mean age = 100.33 days). Machine and deep learning classification architectures (e.g., CapsNet) indicate that functionally connecting infants to a mobile via a tether influences the baby movement most where it matters, namely at the point of infant∼world connection (Table 1). Using dynamics as a guide, we have developed tools to identify the moment an infant switches from spontaneous to intentional action (Fig. 1b). Preliminary coordination dynamics analysis and active inference generative modeling indicate that moments of stillness hold important epistemic value for young infants discovering their ability to change the world around them (Fig. 1c). Finally, a model of slow~fast brain coordination dynamics based on a 3D extension of the Jirsa-Kelso Excitator successfully simulated the evolution of tethered foot activity as infants transition from spontaneous to ordered action. By tuning a small number of parameters, this model captures patterns of emergent goal-directed action (Fig. 1d). Meshing concepts, methods and tools of Active Inference, Artificial Intelligence and Coordination Dynamics at multiple levels of description, the CD + AI2 program of research aims to identify key control parameters that shift the infant system from spontaneous to intentional behavior. The potent combination of mathematical modeling and quantitative analysis along with empirical study allow us to express the emergence of agency in quantifiable, lawful terms

Coordination Dynamics meets Active Inference and Artificial Intelligence (CD + AI2):A multi-pronged approach to understanding the dynamics of brain and the emergence of conscious agency

How do humans discover their ability to act on the world? By tethering a baby’s foot to a mobile (Fig. 1a) and measuring the motion of both in 3D, we explore how babies begin to make sense of their coordinative relationship with the world and realize their ability to make things happen (N= 16; mean age = 100.33 days). Machine and deep learning classification architectures (e.g., CapsNet) indicate that functionally connecting infants to a mobile via a tether influences the baby movement most where it matters, namely at the point of infant∼world connection (Table 1). Using dynamics as a guide, we have developed tools to identify the moment an infant switches from spontaneous to intentional action (Fig. 1b). Preliminary coordination dynamics analysis and active inference generative modeling indicate that moments of stillness hold important epistemic value for young infants discovering their ability to change the world around them (Fig. 1c). Finally, a model of slow~fast brain coordination dynamics based on a 3D extension of the Jirsa-Kelso Excitator successfully simulated the evolution of tethered foot activity as infants transition from spontaneous to ordered action. By tuning a small number of parameters, this model captures patterns of emergent goal-directed action (Fig. 1d). Meshing concepts, methods and tools of Active Inference, Artificial Intelligence and Coordination Dynamics at multiple levels of description, the CD + AI2 program of research aims to identify key control parameters that shift the infant system from spontaneous to intentional behavior. The potent combination of mathematical modeling and quantitative analysis along with empirical study allow us to express the emergence of agency in quantifiable, lawful terms

Fully‐connected semantic segmentation of hyperspectral and LiDAR data

Semantic segmentation is an emerging field in the computer vision community where one can segment and label an object all at once, by considering the effects of the neighbouring pixels. In this study, the authors propose a new semantic segmentation model that fuses hyperspectral images with light detection and ranging (LiDAR) data in the three‐dimensional space defined by Universal Transverse Mercator (UTM) coordinates and solves the task using a fully‐connected conditional random field (CRF). First, the authors’ pairwise energy in the CRF model takes into account the UTM coordinates of the data; and performs fusion in the real world coordinates. Second, as opposed to the commonly used Markov random fields (MRFs) which consider only the nearby pixels; the fully‐connected CRF considers all the pixels in an image to be connected. In doing so, they show that these long‐term interactions significantly enhance the results when compared to traditional MRF models. Third, they propose an adaptive scaling scheme to decide the weights of LiDAR and hyperspectral sensors in shadowy or sunny regions. Experimental results on the Houston dataset indicate the effectiveness of their method in comparison to the several MRF based approaches as well as other competing methods

Improving the performance of construction project using green building principles

Construction projects need to be effectively managed to prevent conflicts and problems between the constructor, users, and the environment, and this requires considering the principles of eco-friendliness, especially for housing projects. This study was, therefore, conducted to set priorities to improve the performance of housing construction project management using the green building principle initiated in 2013 as a benchmark for the Greenship assessment for New Buildings by the Green Building Council of Indonesia. The Importance Performance Analysis was applied to measure the level of user interest and satisfaction on the performance of constructors in constructing housing projects. The results showed technical factors of construction management are dominant in evaluating constructor’s performance. The users expect their houses to be constructed on time according to planned and agreed schedules. Moreover, the factors associated with green building-based performance include reusable and recyclable building materials (mean score of satisfaction level = 3.682 and interest level = 3.882), availability of facilities for people with disabilities (mean score of satisfaction level = 3.420 and interest level = 3.874), eco-friendly materials and construction (mean score of satisfaction level = 3.634 and interest level = 3.840), friendly design and materials for people with disabilities, infants, seniors, and pregnant women (mean score of satisfaction level = 3.704 and interest level = 3.832), and the use of solar energy (mean score of satisfaction level = 3.688 and interest level = 3.825). This, therefore, means the green building is considered important by the users during the construction and implementation processe

ANÁLISE DO NÍVEL DE LEGENDA DE CLASSIFICAÇÃO DE AREAS URBANAS EMPREGANDO IMAGENS MULTIESPECTRAIS E HIPERESPECTRAIS COM OS MÉTODOS ÁRVORE DE DECISÃO C4.5 E FLORESTA RANDÔMICA

Ambientes urbanos representam uma das áreas mais desafiadoras do sensoriamento remoto devido à grande diversidade encontrada nos materiais presentes na sua superfície. O uso de imagens com alta resolução espacial e alta resolução espectral surge como uma alternativa para aplicações urbanas, pois a combinação destas duas características permite uma melhor detecção e discriminação de alvos. O presente trabalho tem um duplo objetivo: i) avaliar dois conjuntos de dados na classificação fina de alvos urbanos para dois níveis de legenda (com 11 e 38 classes de cobertura do solo): um deles composto exclusivamente por uma imagem orbital multiespectral (WV-2) e o outro conjunto composto exclusivamente por uma imagem aerotransportada hiperespectral (SpecTIR), ii) bem como testar o desempenho de dois métodos diferentes de classificação de imagens, Árvore de Decisão C4.5 e Floresta Randômica (Random Forest), para ambos os níveis de legenda. Oito experimentos de classificação foram realizados para atender a tais objetivos de investigar a eficácia dos sensores e dos métodos em dois níveis de detalhamento. Foram obtidas classificações de elevada acurácia. Demonstrou-se para todos os níveis de detalhamento e métodos que as classificações obtidas com dados do sensor SpecTIR apresentaram resultados significantemente superiores aos das classificações com dados do sensor WV-2

Role of project governance in managing projects sustainability: A theoretical perspective

Sustainability is incessantly posing challenges when it is studied juxtaposition with project management. To ensure sustainability companies undertaking projects ought to set strategic and operational plans that will add to the project sustainability. Research shows that existing project management structures do not effectively consider sustainability issues and therefore need revisions at strategic and operational levels. Similarly, while the theme of project governance is finding traction in the literature, the discussion that connects project governance and sustainability in projects is elusive. This research work is specifically focusing on developing a linkage between project governance and managing sustainability in projects. There is an acute scarcity of literature encircling linkage between project governance and sustainable project management, thus, based on the extensive review of the literature, this study is one of the pioneering studies to highlight the relationship between the themes of project governance and sustainable project management