Unsupervised Space Partitioning for Nearest Neighbor Search

Approximate Nearest Neighbor Search (ANNS) in high dimensional spaces is crucial for many real-life applications (e.g., e-commerce, web, multimedia, etc.) dealing with an abundance of data. This paper proposes an end-to-end learning framework that couples the partitioning (one critical step of ANNS) and learning-to-search steps using a custom loss function. A key advantage of our proposed solution is that it does not require any expensive pre-processing of the dataset, which is one of the critical limitations of the state-of-the-art approach. We achieve the above edge by formulating a multi-objective custom loss function that does not need ground truth labels to quantify the quality of a given data-space partition, making it entirely unsupervised. We also propose an ensembling technique by adding varying input weights to the loss function to train an ensemble of models to enhance the search quality. On several standard benchmarks for ANNS, we show that our method beats the state-of-the-art space partitioning method and the ubiquitous K-means clustering method while using fewer parameters and shorter offline training times. We also show that incorporating our space-partitioning strategy into state-of-the-art ANNS techniques such as ScaNN can improve their performance significantly. Finally, we present our unsupervised partitioning approach as a promising alternative to many widely used clustering methods, such as K-means clustering and DBSCAN.</p

Energy management of smart homes over fog-based IoT architecture

Existing research studies on home automation systems mostly conserve energy by modeling the occupancy of users within home. Some others apply statistical approaches on the survey data about usage of appliances. Consequently, these research works either reduce wastage of electricity through automation or achieve energy efficiency based on appliances’ usage estimations. However, they do not provide energy consumption modeling which is human comfort centric and also validated through practical implementation in real-world smart homes. We present a Markov-chain-based probabilistic model to obtain users stochastic activity patterns which are used to forecast the energy consumption in a smart home environment. These predictions are then leveraged by our novel comfort aware energy saving mechanism named as prediction- and feedback-based proactive energy conservation (PF-PEC) algorithm. The PF-PEC algorithm reduces the total energy consumption while ensuring standard human comfort. Furthermore, a fog-based Internet of Things (IoT) architecture is implemented and deployed in a smart home to efficiently incorporate the proposed algorithm in real-world scenarios. Experimental results show up to 36% energy conservation, marking substantial reduction in daily electricity usage.</p

Towards Crowd-aware Indoor Path Planning (Extended Version)

Indoor venues accommodate many people who collectively form crowds. Such crowds in turn influence people's routing choices, e.g., people may prefer to avoid crowded rooms when walking from A to B. This paper studies two types of crowd-aware indoor path planning queries. The Indoor Crowd-Aware Fastest Path Query (FPQ) finds a path with the shortest travel time in the presence of crowds, whereas the Indoor Least Crowded Path Query (LCPQ) finds a path encountering the least objects en route. To process the queries, we design a unified framework with three major components. First, an indoor crowd model organizes indoor topology and captures object flows between rooms. Second, a time-evolving population estimator derives room populations for a future timestamp to support crowd-aware routing cost computations in query processing. Third, two exact and two approximate query processing algorithms process each type of query. All algorithms are based on graph traversal over the indoor crowd model and use the same search framework with different strategies of updating the populations during the search process. All proposals are evaluated experimentally on synthetic and real data. The experimental results demonstrate the efficiency and scalability of our framework and query processing algorithms.Comment: The extension of a VLDB'21 paper "Towards Crowd-aware Indoor Path Planning

CommuNety: deep learning-based face recognition system for the prediction of cohesive communities

Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learning system for the prediction of cohesive networks using face images from photo albums. The proposed deep learning model consists of hierarchical CNN architecture to learn descriptive features related to each cohesive network. The paper also proposes a novel Face Co-occurrence Frequency algorithm to quantify existence of people in images, and a novel photo ranking method to analyze the strength of relationship between different individuals in a predicted social network. We extensively evaluate the proposed technique on PIPA dataset and compare with state-of-the-art methods. Our experimental results demonstrate the superior performance of the proposed technique for the prediction of relationship between different individuals and the cohesiveness of communities