Abstract

Binocular vision (BV) is the result of fusion between inputs from each eye to form a coherent image. BV anomalies are evaluated using different diagnostic tests and instruments. One such instrument is the Synoptophore, which evaluates three grades of BV. This equipment though efficient has certain limitations like ambient light while testing, bulky and expensive. We propose VR-Phore, application of a VR head-mounted display for diagnostics based on principle of the haploscope similar to Synoptophore. The proposed system addresses the limitations of Synoptophore with added advantage of a software platform to incorporate testing modules for a range of clinical conditions.

Abstract

Billboard advertisement is one of the dominant modes of traditional outdoor advertisements. Given a set of billboards, there is an opportunity to improve the revenue of the billboard operator by satisfying the advertising demands of an increased number of clients by exploiting the user trajectory data. Our main contributions are three-fold. First, we introduce the problem of billboard advertisement allocation for improving the billboard operator revenue. Second, we propose an efficient user trajectory-based transactional framework using coverage pattern mining for addressing the billboard advertisement allocation problem. Third, we conduct a performance study with a real dataset to show the effectiveness of the proposed framework.

Abstract

Placement of items on the shelf space of retail stores significantly impacts the revenue of the retailer. Given the prevalence and popularity of medium-to-large-size retail stores, several research efforts have been made towards facilitating item/itemset placement in retail stores for improving retailer revenue. However, they do not consider the issue of urgency of sale of individual items. Hence, they cannot efficiently index, retrieve and place high-revenue itemsets in retail store slots in an urgency-aware manner. Our key contributions are two-fold. First, we introduce the notion of urgency for retail itemset placement. Second, we propose the urgency-aware URI index for efficiently retrieving high-revenue and urgent itemsets of different sizes. We discuss the URIP itemset placement scheme, which exploits URI for improving retailer revenue. We also conduct a performance evaluation with two real datasets to demonstrate that URIP is indeed effective in improving retailer revenue w.r.t. existing schemes.

Abstract

Mining of coverage patterns from transactional databases is one of the data mining tasks. It has applications in banner advertising, search engine advertising and visibility computation. In general, most real-world transactional databases are typically large. Mining of coverage patterns from large transactional databases such as query log transactions on a single computer is challenging and time-consuming. In this paper, we propose Distributed Coverage Pattern Mining (DCPM) approach. In this approach, we employ a notion of the summarized form of Inverse Transactional Database (ITD) and replicate it at every node. We also employ an efficient clustering-based method to distribute the computational load of extracting coverage patterns among the Worker nodes. We performed extensive experiments using two real-world datasets and one synthetic dataset. The results show that the proposed approach significantly improves the performance over the state-of-the-art approaches in terms of execution time and data shuffled.

Abstract

In this paper, we propose beamformed energy detection (BFED) spectrum sensing schemes for a single secondary user (SU) or a cognitive radio to detect a primary user (PU) transmission in the presence of an interferer. In the millimeter wave (mmWave) band, due to high attenuation, there are fewer multipaths, and the channel is sparse, giving rise to fewer directions of arrivals (DoAs). Sensing in only these paths instead of blind energy detection can reap significant benefits. An analog beamforming weight vector is designed such that the beamforming gain in the true DoAs of the PU signal is maximized while minimizing interference from the interferer. To demonstrate the bound on the system performance, the proposed sensing scheme is designed under the knowledge of full channel state information (CSI) at the SU for the PU-SU and Interferer-SU channels. However, as the CSI may not be available at the SU, another BFED sensing scheme is proposed, which only utilizes the estimate the DoAs. To model the estimates of DoAs, perturbations are added to the true DoAs. The distribution of the test statistic for BFED with full CSI schemes is derived under the null hypothesis so that the threshold of the NeymanPearson detector can be found analytically. The performance of both schemes is also compared with the traditional energy detector for multi-antenna systems. Index Terms—Beamforming, direction of arrival (DoA), energy detection, mmWave, spectrum sensing.

Abstract

During the COVID-19 pandemic, India’s complete lockdown was implemented from March 24 to May 3 2020, to minimize the effects of community transfer and control the rapidly growing rate of the virus spread. In this paper, we focus on quantifying the change in air pollution due to Hyderabad’s lockdown, the capital of Telangana State. For this, two datasets are employed. The first dataset is from the Central Pollution Control Board (CPCB) stations in the city. In contrast, the second dataset is the dense IoT network of PM monitors deployed in the educational campus of IIITH in Gachibowli, Hyderabad. An analysis is done on the collected data to understand the effect of lockdown on PM values while considering the yearly and seasonal variations. It has been shown that while there has been a significant drop in PM values. However, through correlation analysis between the temperature and the PM values during the regular times, not all PM values decrease because of the lockdown. Index Terms

Abstract

Codes with locality are a class of codes which minimize the number of nodes accessed to repair a failed node. These codes can be used to handle single and multiple erasures. In an extension, codes with hierarchical locality have been proposed, which offer multiple levels of locality as the number of erasure increase. Given the constraints imposed by locality, maximally recoverable codes (MRC) are a class of codes which allow for recoverability from all information theoretically recoverable erasure patterns. In this work, we consider MRC for the case of codes with hierarchical locality. We derive a field size lower bound on MRC with hierarchical locality. We also give constructions of MRC with hierarchical locality for some parameters, whose field size is smaller than that of earlier known constructions.

Abstract

— Centrality measures on graphs have found applications in a large number of domains including modeling the spread of an infection/disease, social network analysis, and transportation networks. As a result, parallel algorithms for computing various centrality metrics on graphs are gaining significant research attention in recent years. In this paper, we study parallel algorithms for the percolation centrality measure which extends the betweenness-centrality measure by incorporating a time dependent state variable with every node. We present parallel algorithms that compute the source-based and source-destination variants of the percolation centrality values of nodes in a network. Our algorithms extend the algorithm of Brandes, introduce optimizations aimed at exploiting the structural properties of graphs, and extend the algorithmic techniques introduced by Sariyuce et al. [26] in the context of centrality computation. Experimental studies of our algorithms on an Intel Xeon(R) Silver 4116 CPU and an Nvidia Tesla V100 GPU on a collection of 12 real-world graphs indicate that our algorithmic techniques offer a significant speedup.

Abstract

Current aerial robots are increasingly adaptive; they can morph to enable operation in changing conditions to complete diverse missions. Each mission may require the robot to conduct a different task. A conventional learning approach can handle these variations when the system is trained for similar tasks in a representative environment. However, it may result in overfitting to the new data stream or the failure to adapt, leading to degradation or a potential crash. These problems can be mitigated with an excessive amount of data and embedded model, but the computational power and the memory of the aerial robots are limited. In order to address the variations in the model, environment as well as the tasks within onboard computation limitations, we propose a deep neuromorphic controller approach with variable topologies to handle each different condition and the data stream with a feasible computation and memory allocation. The proposed approach is based on a deep neuromorphic (multi and variable layered neural network) controller with dynamic depth and progressive layer adaptation for each new data stream. This adaptive structure is combined with a switching function to form a sliding mode controller. The network parameter update rule guarantees the stability of the closed loop system by the convergence of the error dynamics to the sliding surface. Being the first implementation on an aerial robot in this context, the results illustrate the adaptation capability, stability, computational efficiency as well as the real-time validation.

Abstract

Objective assessment of voice disorders is widely explored as an early diagnosis tool for the classification of voice disorders. Voice disorders affect the pitch, loudness and voice quality, which are perceived at the suprasegmental-level in the speech signal. For the detection and assessment of voice disorders, this study explores the effectiveness of Long Term Average Spectral (LTAS) features using four state-of-the-art filter banks designed with critical-band, constant-Q, gammatone, and singlefrequency filtering approaches. Moreover, the performance of the systems is compared with state-of-the-art statistical-average and openSMILE features. Voice disorder detection experiment was carried out on SVD and HUPA database, while only SVD database is used for assessment task. Assessment task is performed in clinical way, in which four binary classifiers were trained in our study. Voice disorder detection and assessment tasks were carried out using the support vector machine classifier. From the results, it was observed that constant-Q filter bank based LTAS features performed better among all LTAS features with classification accuracy of 78% and 81.4% for voice disorder detection task on SVD and HUPA database, respectively. Further, the combination of LTAS features with OpenSMILE features improved (89.6% and 86.6% for SVD and HUPA database, respectively) the performance.