Abstract

We present the design and implementation of Two-Grid Preconditioned Bundle Adjustment (TPBA), a robust and efficient technique for solving the non-linear least squares problem that arises in bundle adjustment. Bundle adjustment (BA) methods for multi-view reconstruction formulate the BA problem as a non-linear least squares problem which is solved by some variant of the traditional LevenbergMarquardt (LM) algorithm. Most of the computation in LM goes into repeatedly solving the normal equations that arise as a result of linearizing the objective function. To solve these system of equations we use the Generalized Minimal Residual (GMRES) method, which is preconditioned using a deflated algebraic two-grid method. To the best of our knowledge this is the first time that a deflated algebraic twogrid preconditioner has been used along with GMRES, for solving a problem in the computer vision domain. We show that the proposed method is several times faster than the direct method and block Jacobi preconditioned GMRES.

Abstract

A behaviourally transformative experience via a strong theme, narrative, and gameplay is a powerful learning paradigm when presented coherently. The interplay of these factors facilitates learning and allows for an immersive experience. It enables players to explore their options and deliberate on the outcomes of their decisions. In this paper, we focus on the socially relevant theme of human trafficking. With Unlocked, we present a perspective that has not been addressed by similarly themed games so far. Understanding such an outlook is crucial to connect with the realities of human trafficking. Our game demonstrates the obstacles in real-life escapes and rescue operations while using well-established literature on learning as guiding principles to create an effective transformative experience. It allows the player to identify with victims and understand their mindset and thought processes. We measure our game’s efficacy in educating players about factors like socio-economic and cultural predicaments, and latent conditions that influence these crimes. Such factors are often overlooked and are hard to understand until they are experienced first-hand. Our study attempts to show that transformative games can help recognize the role that these factors play by placing the player in the victim’s position and building a strong narrative around it. We evaluate the learning process by having two phases of survey, pre-test, and post-test. The pre-test survey tests the player’s knowledge about the current state of human trafficking. In the post-test survey, players rate the experience, gameplay, and effectiveness of the game in educating them about the trafficking scenario. The game was tested and validated by social activists who work to rescue trafficked women and children. They acknowledged the potential impact our game could have on raising awareness. Our findings establish that transformative games grounded with a strong narrative and built with a meticulous design guided by principles of transformation and learning prove to be highly effective in propagating social messages on the perils of human trafficking. Index Terms—Transformative play, Game based learning, Serious games, Human Trafficking, Role playing

Abstract

As scientific communities grow and evolve, there is emergence of new techniques and decline of old ones. The tremendous amount of research publications available online aims to solve a lot of interesting problems. With time, some of the fields have been studied well and research problems solved to a great extent. However, there are few difficult research problems which are yet not solved completely and interests a lot of researchers. In this paper, we aim to find research fields which are saturated and research fields which need to be explored yet. We first extract research problems in a semi supervised manner using a proven bootstrap framework from scientific literature of the last fifty years. We show how a simple statistics based model on top of the research problems extracted can find the saturated fields and grand challenges in any domain of computer science.

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

Recent works have proposed several long term tracking benchmarks and highlight the importance of moving towards long-duration tracking to bridge the gap with application requirements. The current evaluation methodologies, however, do not focus on several aspects that are crucial in a long term perspective like Re-detection, Recovery, and Reliability. In this paper, we propose novel evaluation strategies for a more in-depth analysis of trackers from a long-term perspective. More specifically, (a) we test redetection capability of the trackers in the wild by simulating virtual cuts, (b) we investigate the role of chance in the recovery of tracker after failure and (c) we propose a novel metric allowing visual inference on the ability of a tracker to track contiguously (without any failure) at a given accuracy. We present several original insights derived from an extensive set of quantitative and qualitative experiments.

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.