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From an study of food delivery workers, we examine the precarity and consolidation of working conditions driven by the platform economy in the context of Urban India. The study considers the apparent threats in ‘gigging’, informalisation of labour and the obvious need to reconceptualise worker well-being, risks, stress and the eventuality of workplace and worker alignment in food delivery labour.

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Identifying user-defined keywords is crucial for personalizing interactions with smart devices. Previous approaches of user-defined keyword spotting (UDKWS) have relied on short-term spectral features such as mel frequency cepstral coefficients (MFCC) to detect the spoken keyword. However, these features may face challenges in accurately identifying closely related pronunciation of audio-text pairs, due to their limited capability in capturing the temporal dynamics of the speech signal. To address this challenge, we propose to use shifted delta coefficients (SDC) which help in capturing pronunciation variability (transition between connecting phonemes) by incorporating long-term temporal information. The performance of the SDC feature is compared with various baseline features across four different datasets using a cross-attention based end-to-end system. Additionally, various configurations of SDC are explored to find the suitable temporal context for the UDKWS task. The experimental results reveal that the SDC feature outperforms the MFCC baseline feature, exhibiting an improvement of 8.32% in area under the curve (AUC) and 8.69% in terms of equal error rate (EER) on the challenging Libriphrase-hard dataset. Moreover, the proposed approach demonstrated superior performance when compared to state-of-the-art UDKWS techniques.

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As urbanization continues to accelerate, the demand for energy-efficient and sustainable urban infrastructure has become paramount. Street lights are an elemental part of any city because they facilitate better night visibility, secured roads, and luminescence in public areas. Also, because street lights are present homogeneously on city roads, if they are used for wireless communication, other smart nodes can make use of their backhaul. In this paper, we present a smart street lighting system based on Wi-SUN (Wireless Smart Ubiquitous Network), which offers excellent redundancy owing to its use of a mesh topology. Wi-SUN is based on the IEEE 802.15.4g standard and uses license-free sub-GHz bands. We have used oneM2M as the middleware platform to enable possible interoperability with other smart city data verticals such as weather stations, environmental monitoring, crowd monitoring and so on. The proposed system provides a robust and low-cost method for controlling street lighting in any area, providing IP-addressable lighting. The proposed system additionally aims to provide consistent Wi-SUN coverage for future nodes in the area of deployment

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Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by varied social cognitive challanges and repetitive behaviour patterns. Identifying reliable brain imaging-based biomarkers for ASD has been a persistent challenge due to the spectrum's diverse symptomatology. Existing baselines in the field have made significant strides in this direction, yet there remains room for improvement in both performance and interpretability. We propose HyperGALE, which builds on the hypergraph by incorporating learned hyper graphs and gated attention mechanisms. This approach has led to substantial improvements in the model's ability to interpret complex brain graph data, offering deeper insights into ASD biomarker characterization. Evaluated on the exptensive ABIDE II dataset, HyperGALE not only improves interpretability but also demonstrates statistically significant enchancements in key performance metrics compared to both previous baselines and the foundation hypergraph model. The advancement HyperGALE brings to ASD research highlights the potential of sophisticated graph-based techniques in neurodevelopmental studies. The source code and implementation instructions are available at Github.

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The simultaneous occurrence of climate extremes significantly impacts ecosystems, increasing the vulnerability to physical risks. Despite extensive research on hot extremes and droughts globally, there remains a significant gap in comprehending the occurrence, magnitude, spatial extent, and associated risks of compound extremes, encompassing scenarios like warm/dry, cold/dry, warm/wet, and cold/wet. This study investigates various compound extreme scenarios by examining combinations of maximum temperature (Tx) and the Standardized Precipitation Evapotranspiration Index (SPEI) using monthly data from 1951 to 2014 acquired from India Meteorological Department (IMD) for Indian landmass. From the results, the spatial extent of warm/dry events have increased at 1.8% per decade, while cold/wet events decreased by -1.1% over India. The warm/wet events have shown an increased trend of about 0.3%, and cold/dry events at modest rise of 0.7% per decade. Furthermore, compound warm/dry and cold/wet extremes over India exhibit extreme frequency and shorter return periods, posing greater risk. Conversely, compound cold/dry and warm/wet extremes occur less often, indicating longer return periods and lower risk. Across much of the country, the frequency of warm/dry, cold/dry, warm/wet, and cold/wet extremes ranges from 30-45, 15-30, 20-30, and 25-45 months, respectively. Notably, warm/dry conditions exhibit increased frequency in the recent period (1983-2014) with 31 years compared to the base period (1951-1982) which had approximately 24 years for a spatial extent exceeding 5%. The findings of this study contribute to an enhanced understanding of the changes in compound climate extremes from a multivariate perspective.

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Mobile manipulators find applications in manufacturing, infrastructure inspection, and domestic services. While most mobile manipulators excel at pick and place, weaim to combine three distinct manipulation primitives: striking,Pushing, and pick-and-place. This integration enables versatile object manipulation and expands the robot’s operational range. Furthermore, we propose a new attachment that can be easily fitted with most industrial two-finger grippers, capable of making line contact while pushing or striking an object. The proposed framework includes a high-level action sequence planner, dedicated offline planners for each manipulation primitive (Strike, Push, and Pick & Place), and a whole-body controller. Preliminary results from simulations and hardware experiments demonstrate the potential for diverse applications in the field of mobile manipulation.

Abstract

Over the last decade, there has been growing interest in learning the mapping from structural connectivity (SC) to functional connectivity (FC) of the brain. The spontaneous fuctuations of the brain activity during the restingstate as captured by functional MRI (rsfMRI) contain rich non stationary dynamics over a relatively fxed structural connectome. Among the modeling approaches, graph difusionbased methods with single and multiple difusion kernels approximating static or dynamic functional connectivity have shown promise in predicting the FC given the SC. However, these methods are computationally expensive, not scalable, and fail to capture the complex dynamics underlying the whole process. Recently, deep learning methods such as GraphHeat networks and graph difusion have been shown to handle complex relational structures while preserving global information. In this paper, we propose a novel attentionbased fusion of multiple GraphHeat networks (AGHN) for mapping SCFC. AGHN enables us to model multiple heat kernel difusion over the brain graph for approximating the complex Reaction Difusion phenomenon. We argue that the proposed deep learning method overcomes the scalability and computational inefciency issues but can still learn the SCFC mapping successfully. Training and testing were done using the rsfMRI data of 1058 participants from the human connectome project (HCP), and the results establish the viability of the proposed model. On HCP data, we achieve a high Pearson correlation of 0.788 (DesikanKilliany atlas with 87 regions) and 0.773 (AAL atlas with 86 regions). Furthermore, experiments demonstrate that AGHN outperforms the existing methods in learning the complex nature of the structurefunction relation of the human brain.

Abstract

Univariate regression trees have representation problems for non-orthogonal regression functions. Ensembles of univariate regression trees have better representational power. In some cases, weighted ensembles have shown better performance than unweighted ensembles. In this paper, we study the properties of ensembles of regression trees by using regression classification models. We propose a theoretical framework to study the representational power of infinite-sized weighted ensembles, consisting of randomised finite-sized regression trees. We show for some datasets that the weighted ensembles may have better representational power than unweighted ensembles, but the performance is highly dependent on the weighting scheme and the properties of datasets. Our model cannot be used for all the datasets. However, for some datasets, we can accurately predict the experimental results of ensembles of regression trees.