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With the enormous growth of wireless technology, and location acquisition techniques, a huge amount of spatio-temporal traces are being accumulated. This dataset facilitates varied location-aware services and helps to take real-life decisions. Efficiently handling and processing spatio-temporal queries are necessary to respond in real-time. Processing the vast spatio-temporal data requires scalable computing infrastructure. In this regard, an efficient query resolution system can be deployed if we predict the infrastructure requirement of the user query apriori along with the identification of the geospatial service chain. In this work, we propose a framework, namely LYRIC (deadLine and budget aware spatio-temporal querY pRocessing In Cloud), where the spatio-temporal queries are resolved efficiently considering user-defined deadline and budget constraint. Our framework shows high deadline completion accuracy in the range of 1.0 - 0.937, which is more accurate than SparkGIS, GeoSpark, GeoMesa and JUST. This also reduces the resource prediction error by 11 percent, considering the geospatial service chain than without it. The cost of the spatio-temporal query is reduced by $\approx$ ≈ 23% in LYRIC, further, the simulation study (using CloudSim) illustrates the efficacy and scalability of LYRIC in terms of optimal budget usage and execution time compared to four baseline approaches. [ABSTRACT FROM AUTHOR]

This article discusses the prediction of cohesiveness of a group of people in images. The cohesiveness of a group is an essential indicator of the emotional state, structure, and success of the group. We study the factors that influence the perception of group-level cohesion and propose methods for estimating the human-perceived cohesion on the group cohesiveness scale. To identify the visual cues (attributes) for cohesion, we conducted a user survey. Image analysis is performed at a group-level via a multi-task convolutional neural network. A capsule network is explored for analyzing the contribution of facial expressions of the group members on predicting the Group Cohesion Score (GCS). We add GCS to the Group Affect database and propose the ‘GAF-Cohesion database’. The proposed model performs well on the database and achieves near human-level performance in predicting a group's cohesion score. It is interesting to note that group cohesion as an attribute, when jointly trained for group-level emotion prediction, helps in increasing the performance for the later task. This suggests that group-level emotion and cohesion are correlated. Further, we investigate the effect of face-level similarity, body pose and subset of a group on the task of automatic cohesion perception. [ABSTRACT FROM AUTHOR]

The 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2021) took place from September 21–26 virtually, around the world; co-located with the International Symposium on Wearable Computers (ISWC). This article discusses some of the interesting research published in the Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies, as well as those accepted at ISWC this year—presented virtually with Live Q&A over Whova, Zoom, and Gather.Town. Furthermore, it covers some of the interesting plenary events, such as the Keynotes by Cynthia Breazeal (MIT Media Labs) and John LePore (Perception), as well as the panel discussion on the 30th anniversary of Mark Weiser's paper on the Vision of a Computer for the 21st Century. Finally, some of the interesting highlights from the ISWC gadget-show and design-exhibition, alongside multiple virtual workshops as part of the adjunct program have been presented. [ABSTRACT FROM AUTHOR]

This paper reports an optical “TURN OFF” aptasensor, which is comprised of a deoxyribonucleic acid aptamer attached to a quantum dot on the $5^{\prime }$ terminus and gold nanoparticle on the $3^{\prime }$ terminus. The photoluminescence intensity is observed to decrease upon progressive addition of the target protein tumor necrosis factor-alpha (TNF- $\alpha $) to the sensor. For PBS-based TNF- $\alpha $ samples, the beacon exhibited 19%–20% quenching at around 22 nM concentration. The photoluminescence intensity and the quenching efficiency showed a linear decrease and a linear increase, respectively, between 0 to 22.3 nM TNF- $\alpha $. The detection limit of the sensor was found to be 97.2 pM. Specificity test results determined that the sensor has higher selectivity toward TNF- $\alpha $ than other control proteins such as C—reactive protein, albumin, and transferrin. The beacon successfully detected different concentrations of TNF- $\alpha $ in human serum-based samples exhibiting around 10% quenching efficiency at 12.5 nM of the protein. [ABSTRACT FROM AUTHOR]