Abstract

Product placement in retail stores has a significant impact on the revenue of the retailer. Hence, research efforts are being made to propose approaches for improving item placement in retail stores based on the knowledge of utility patterns extracted from the log of customer purchase transactions. Another strategy to make any retail store interesting from a customer perspective is to cater to the varied requirements and preferences of customers. This can be achieved by placing a wider variety of items in the shelves of the retail store, thereby increasing the diversity of the items that are available for purchase. In this regard, the key contributions of our work are three-fold. First, we introduce the problem of concept hierarchy based diverse itemset placement in retail stores. Second, we present a framework and schemes for facilitating efficient retrieval of the diverse top-revenue itemsets based on a concept hierarchy. Third, we conducted a performance evaluation with a real dataset to demonstrate the overall effectiveness of our proposed schemes.

Abstract

Partial Periodic itemsets are an important class of regularities that exist in a temporal database. A Partial Periodic itemset is something persistent and predictable that appears in the data. Past studies on Partial Periodic itemsets have been primarily focused on centralized databases and are not scalable for Big Data environments. One cannot ignore the advantage of scalability by using more resources. This is because we deal with large databases in a real-time environment and using more resources can increase the performance. To address the issue we have proposed a parallel algorithm by including the step of distributing transactional identifiers among the machines and mining the identical itemsets independently over the different machines. Experiments on Apache Spark’s distributed environment show that the proposed approach speeds up with the increase in a number of machines.

Abstract

Driver gaze mapping is crucial to estimate driver attention and determine which objects the driver is focusing on while driving. We introduce DGAZE, the first large-scale driver gaze mapping dataset. Unlike previous works, our dataset does not require expensive wearable eye-gaze trackers and instead relies on mobile phone cameras for data collection. The data was collected in a lab setting designed to mimic real driving conditions and has point and object-level annotation. It consists of 227,178 road-driver image pairs collected from 20 drivers and contains 103 unique objects on the road belonging to 7 classes: cars, pedestrians, traffic signals, motorbikes, auto-rickshaws, buses and signboards. We also present I-DGAZE, a fused convolutional neural network for predicting driver gaze on the road, which was trained on the DGAZE dataset. Our architecture combines facial features such as face location and head pose along with the image of the left eye to get optimum results. Our model achieves an error of 186.89 pixels on the road view of resolution 1920×1080 pixels. We compare our model with state-of-the-art eye gaze works and present extensive ablation results.

Abstract

Representation learning on graphs has been gaining attention due to its wide applicability in predicting missing links and classifying and recommending nodes. Most embedding methods aim to preserve specific properties of the original graph in the low dimensional space. However, real-world graphs have a combination of several features that are difficult to characterize and capture by a single approach. In this work, we introduce the problem of graph representation ensemble learning and provide a first of its kind framework to aggregate multiple graph embedding methods efficiently. We provide analysis of our framework and analyze – theoretically and empirically – the dependence between state-of-the-art embedding methods. We test our models on the node classification task on four realworld graphs and show that proposed ensemble approaches can outperform the state-of-the-art methods by up to 20% on macroF1. We further show that the strategy is even more beneficial for underrepresented classes with an improvement of up to 40%. Index Terms—Graph Representation, Node Embedding, Ensemble Learning, Greedy Search, Node Classification, Distance Correlation, Prediction Diversity

Abstract

—Blockchain is a distributed ledger with wide applications. Due to the increasing storage requirement for blockchains, the computation can be afforded by only a few miners. Sharding has been proposed to scale blockchains so that storage and transaction efficiency of the blockchain improves at the cost of security guarantee. This paper aims to consider a new protocol, Secure-Repair-Blockchain (SRB), which aims to decrease the storage cost at the miners. In addition, SRB also decreases the bootstrapping cost, which allows for new miners to easily join a sharded blockchain. In order to reduce storage, coding-theoretic techniques are used in SRB. In order to decrease the amount of data that is transferred to the new node joining a shard, the concept of exact repair secure regenerating codes is used. The proposed blockchain protocol achieves lower storage than those that do not use coding, and achieves lower bootstrapping cost as compared to the different baselines

Abstract

Software Defined Networking (SDN) becomes a de facto standard for the future Internet. SDN decouples the control plane from the data plane of a proprietary network asset to ensure better programmability and security for designing more innovative future network applications. Presently, the SDN framework does not have proper access control mechanism among different entities, namely SDN applications, SDN controllers and switches. To achieve this goal, this paper proposes a blockchain-based access control scheme for the SDN framework. The proposed scheme has the capability to resist various wellknown attacks and alleviate the existing single point of controller failure issue in SDN. Index Terms—software-defined networking, blockchain, consensus, security, access control.

Abstract

Most of the popular explanations of the observed anomalies in the semileptonic B-meson decays involve TeV scale Leptoquarks (LQs). Among the various possible LQ models, two particular LQs—S1(3, 1, 1/3) and U1(3, 1, 2/3) seem to be most promising. Here, we use current LHC data to constrain the S1(3, 1, 1/3) and U1(3, 1, 2/3) parameter spaces relevant for the RD(∗) observables. We recast the latest ATLAS τ τ resonance search data to obtain new exclusion limits. For this purpose, we consider both resonant (pair and single productions) and non-resonant (t-channel LQ exchange) productions of these LQs at the LHC. For the limits, the most dominant contribution comes from the (destructive) interference of the nonresonant production with Standard Model backgrounds. The combined contribution from the pair and inclusive single production processes Mandal et al (JHEP, 07:028, 2015, [1]) is less prominent but non-negligible. The limits we get are independent and competitive to other known bounds. For both the models, we set limits on RD(∗) motivated couplings Mandal et al (Phys Rev D 99(5):055028, 2019, [2]), Bhaskar et al (Phys Rev D 104(3):035016, 2021, [3]).

Abstract

Synopsis: Angle recession, trabecular meshwork injury, increased trabecular pigmentation, and reduced Schlemm’s canal dimensions can cause reduced aqueous outflow and unilateral glaucoma in an eye, following blunt trauma. Also, these patients are possibly at a risk to develop raised intraocular pressure (IOP) in the normal fellow eye due to reduced Schlemm’s canal area. Aim: To identify and quantitatively evaluate Schlemm’s canal (SC) parameters by anterior segment optical coherence tomography (AS-OCT) in the patients with unilateral 360° angle recession glaucoma (ARG) and compare with the fellow normal eyes and age-related normal control. Materials and methods: Six patients with a history of unilateral ocular blunt trauma and unilateral 360° ARG, normal fellow eyes and 34 agematched normal controls underwent anterior chamber angle imaging with corneal line scan protocol using AS-OCT. Schlemm’s canal crosssectional area (SC-CSA) and meridional and coronal diameters were measured on temporal and nasal sections at 3 and 9 o’clock positions. Results: In the AS-OCT cross-sectional images, SC was observed as a horizontally oval or ellipsoidal translucent space. The mean SC-CSA (1,710 ± 376.1 μm2 vs 6,100 ± 2,700 μm2 , p < 0.0001), mean meridional diameter (243.6 ± 55.47 μm vs 474 ± 125.6 μm, p < 0.0001), and mean coronal diameter (32.68 ± 6.27 μm vs 57.42 ± 16.27 μm, p < 0.0001) of the SC were smaller in ARG eyes when compared with the normal eyes. The SC dimensions were reduced in the untraumatized fellow eyes of ARG patients when compared with the normal eyes (SC-SCA: 2350 ± 602.1 μm2 , p = 0.001, meridional diameter: 341.8 ± 88.8 μm, p = 0.012 and coronal diameter: 31.67 ± 3.8 μm, p < 0.0001). There was no difference in the measured SC dimensions between the ARG eyes and the normal fellow eyes. Conclusion: The reduced SC parameters in the eyes with unilateral 360° ARG and the normal fellow eyes could mean that these patients probably have an underlying structural abnormality in the SC. Trabecular meshwork injury, angle recession, and increased trabecular pigmentation are probably the predisposing factors in the ARG eyes for the increase in IOP. Whether the normal fellow eyes develop IOP rise in the future needs to be seen during regular follow-up. Keywords: Angle recession glaucoma, Anterior segment optical coherence tomography, Schlemm’s canal.

Abstract

he presence of switching/evolving topologies of the power system. In today’s smart grids, switching topologies often arise from reconfiguration and resilience against faults or from switching among different control ar

Abstract

Reliable guidance of fixed-wing Unmanned Aerial Vehicles (UAVs) is challenging, as their high maneuverability exposes them to several dynamical changes and parametric uncertainties. Reliability of state-of-the-art guidance methods is often at stake, as these methods heavily rely on precise UAV course dynamics, assumed in a decoupled first-order form with known time constant. To improve reliability of guidance for fixed-wing UAVs, this work proposes a novel vector field law that can handle uncertain course time constant and state-dependent uncertainty in the course dynamics arising from coupling. Stability is studied in the Lyapunov framework, while reliability of the proposed method is tested on a software-in-the loop UAV simulator. The simulations show that, in the presence of such uncertainty, the proposed method outperforms the standard vector field approaches.