Abstract

We focus on the problem of symmetric key distribution for securing shared resources among large groups of users in distributed applications like cloud storage, shared databases, and collaborative editing, among others. In such applications, resources such as data, are sensitive in nature and it is necessary that only authorized users are allowed access without the presence of on-line monitoring system. The de-facto approach is to encrypt a shared resource and deploy a key distribution mechanism, which enables only authorized users to generate the respective decryption key for the resource. The key distribution approach has two major challenges: first, the applications are dynamic i.e., users might join and leave arbitrarily, and second, for a large number of users, it is required that the cryptographic technique be scalable and efficient. In this work, we describe an approach that overcomes these challenges by using two key techniques: first, flattening the access structure and applying efficient symmetric key distribution techniques. By flattening the access structure, we reduce the problem to that of key distribution of a resource among all the users sharing that resource. We consider this smaller flattened access structure and devise a unified key distribution technique that is sufficient for key distribution across all such structures. Our key distribution techniques have an important feature of a public secret and a private secret, which allows the group controller to publish updates to the keying material using the public secret and therefore, does not necessitate the users to be in constant communication with the group controller. Using this model we describe two efficient key distribution techniques that scale logarithmically with the group size and also handle group additions and removals. Furthermore, a user can be off-line for any amount of time and need not be aware of the dynamics of the system, which is important as it overcomes the problems posed by lossy channels. We have performed an experimental evaluation of our scheme against a popular existing scheme and show that they perform better for this scheme with the same security guarantees. As our approaches are easy to implement they are especially suitable for practical applications where security is viewed as an overhead rather than as a necessity.

Abstract

Building accurate lexicon free handwritten text recognizers for Indic languages is a challenging task, mostly due to the inherent complexities in Indic scripts in addition to the cursive nature of handwriting. In this work, we demonstrate an end-to-end trainable CNN-RNN hybrid architecture which takes inspirations from recent advances of using residual blocks for training convolutional layers, along with the inclusion of spatial transformer layer to learn a model invariant to geometric distortions present in handwriting. In this work we focus building state of the art handwritten word recognizers for two popular Indic scripts – Devanagari and Bangla. To address the need of large scale training data for such low resources languages, we utilize synthetically rendered data for pre-training the network and later fine tune it on the real data. We outperform the previous lexicon based, state of the art methods on the test set …

Abstract

Weighted Frequent Itemset (WFI) mining is animportant model in data mining. It aims to discover all itemsetswhose weighted sum in a transactional database is no less than theuser-specified threshold value. Most previous works focused onfinding WFIs in a transactional database and did not recognizethe spatiotemporal characteristics of an item within the data.This paper proposes a more flexible model of Spatial WeightedFrequent Itemset (SWFI) that may exist in a spatiotemporaldatabase. The recommended patterns may be found very useful inmany real-world applications. For instance, an SWFI generatedfrom an air pollution database indicates a geographical regionwhere people have been exposed to high levels of an air pollutant,sayPM2.5. The generated SWFIs do not satisfy the anti-monotonic property. Two new measures have been presentedto effectively reduce the search space and the computationalcost of finding the desired patterns. A pattern-growth algorithm,called Spatial Weighted Frequent Pattern-growth, has also beenpresented to find all SWFIs in a spatiotemporal database.Experimental results demonstrate that the proposed algorithmis efficient. We also describe a case study in which our modelhas been used to find useful information in air pollution database.

Abstract

In this paper, we present an algorithmic frame-work which computes the collision-free velocities for the robotin a human shared dynamic and uncertain environment. Weextend the concept of Inverse Velocity Obstacle (IVO) to aprobabilistic variant to handle the state estimation and motionuncertainties that arise due to the other participants of the en-vironment. These uncertainties are modeled as non-parametricprobability distributions. In our PIVO: Probabilistic InverseVelocity Obstacle, we propose the collision-free navigation asan optimization problem by reformulating the velocity condi-tions of IVO as chance constraints that takes the uncertaintyinto account. The space of collision-free velocities that resultfrom the presented optimization scheme are associated to aconfidence measure as a specified probability. We demonstratethe efficacy of our PIVO through numerical simulations anddemonstrating its ability to generate safe trajectories underhighly uncertain environments

Abstract

n this article, we propose exact passive-aggressive(PA) online algorithms for ordinal regression. The proposedalgorithms can be used even when we have interval labelsinstead of actual labels for example. The proposed algorithmssolve a convex optimization problem at every trial. We find anexact solution to those optimization problems to determine theupdated parameters. We propose a support class algorithm (SCA)that finds the active constraints using the Karush–Kuhn–Tucker(KKT) conditions of the optimization problems. These active con-straints form a support set, which determines the set of thresholdsthat need to be updated. We derive update rules for PA, PA-I, and PA-II. We show that the proposed algorithms maintainthe ordering of the thresholds after every trial. We provide themistake bounds of the proposed algorithms in both ideal andgeneral settings. We also show experimentally that the proposedalgorithms successfully learn accurate classifiers using intervallabels as well as exact labels. The proposed algorithms also dowell compared to other approaches.

Abstract

The commercial sector accounts for 8.6% of total electricity consumption of India and increasing with 5% rate annually due to rapid urbanization. The building's major energy consuming end-uses are air-conditioning including heating, cooling, lighting, and equipment. The development and implementation of energy efficiency codes and measures will provide sustainable future. The development process requires analyses of current building construction and operation practices. The current building construction practices can be analyzed by developing reference buildings representative of national building stock. In this research, the development methodology of reference building, developed reference office buildings for India and its application have been presented. The reference buildings are developed for low-rise and high-rise office buildings with 8-h and 24-h operation based on data collection of 230 buildings constructed in last ten years. This methodology can be applied to other buildings types for development of reference buildings with the appropriate data. From the analyses of reference buildings, it has found that the primary energy conservation measures would be envelope thermal properties for 8-h operational buildings while internal loads (occupancy, lighting, and equipment) in 24-h operational building. The energy performance index (EPI) of reference office buildings are better than ECBC 2017. Besides, some of the minimum requirements have not met. The lighting and glass specifications of reference buildings are better than ECBC level due market and government policies while HVAC system specially chiller COP and its controls are needed to be improved.

Abstract

Forecasting energy consumption enables users to plan their resource utilization optimally. For this, it is essential to make reliable forecasting of consumption profile in real-time which is very challenging and still emerging. In this study, we focus on forecasting HVAC cooling energy consumption at sub-hourly forecasting horizon which enables us to analyze and control the demand in real-time. With the aid of energy meters and adaptation of BACnet technology in the target building, we collected real building cooling energy consumption data and HVAC systems usage at a higher resolution. In this methodology, weather data from Weather Underground of Gachibowli, Hyderabad location has been used. These recorded weather parameters are being used as explanatory variables. Along with the weather parameters, many other feature-engineered explanatory variables which are discussed in the paper has been used to capture the trend and variation of energy consumption. Required pre-processing and data analysis is performed on the raw data to obtain meaningful information for modeling. We have implemented one-step ahead static forecasting using Gradient Boosting Regression Trees (GBRT), where we forecast for single-time-step ahead in each iteration then update the training data at the end of each iteration with actual data available next time-step in order to assess how well the model forecasts one-step-ahead. Our results show that we could improve forecast accuracy by almost 45% by including feature engineered variables. We achieved a Mean Absolute Percentage Error (MAPE) of 14.7% with GBRT.

Abstract

A well-designed mixed mode building allows natural ventilation when the outside weather conditions are favorable and deploys air-conditioning when natural ventilation is not able to provide sufficient comfort. In this study, a methodology is proposed to enable real time control of operable windows for mixed mode ventilation building. EnergyPlus is used for building energy simulations. A real-time simulation is performed using Building Controls Virtual Test Bed (BCVTB). In this methodology, AERIS Weather Data is used to forecast hourly weather parameters. Seasonal Autoregressive Integrated Moving Average (SARIMA) Models are used to model a day-ahead prediction for the lighting load, electric load and the occupancy profiles. Cosimulation of EnergyPlus is carried out with BCVTB to enable real-time simulation wherein; the EnergyPlus weather file parameters and input building loads are updated every hour. Based on the indoor and outdoor conditions, EnergyPlus calculates the schedule for opening/closing of windows. A simulation based case study for Hyderabad, India is performed to demonstrate the working of the proposed methodology. Reduction of 25% in cooling energy demand was estimated in a mixed mode building as compared to the fully air-conditioned building during the study period.

Abstract

In tropical climates, glass windows are important for a building's energy efficiency. By providing shading on a glass window, direct solar incident radiation can be restricted. This lowers the cooling energy consumption in buildings. The most commonly used method to provide shading is by using fixed shades such as overhangs and fins. Another method is to provide dynamic shades that can be controlled based on incident solar radiation on the façade. Shading is a method to control the solar heat gain coefficient (SHGC) and visible light transmission (VLT) of a vertical fenestration. This study is performed to obtain the equivalent SHGC of vertical fenestration with fixed and dynamic shading. Prescriptive requirements of energy codes, such as ASHRAE 90.1 and ECBCIndia, limit the maximum SHGC value of fenestration. The effective SHGC with fixed shading is defined as the equivalent SHGC. The energy codes define tabular methods for the calculation of the equivalent SHGC that is generally given as a function of overhangs/fins depth, glass orientation, and latitude of the building. This tabular method generally does not cover all orientations, shading depths, and locations. Further, the equivalent SHGC for dynamic shade is either not defined in most of the codes or regarded as the minimum SHGC of the fenestration system. Herein, a simulation-based approach is proposed to calculate the equivalent SHGC for fixed and dynamic shades. A tool has been developed that uses EnergyPlus, the weather file of the building location, window and shading dimensions, and set point for dynamic shades, to perform parametric energy simulations to calculate the equivalent SHGC.

Abstract

The buildings are conventionally operated to maintain homogeneous indoor ambient conditions to maintain comfortable thermal and visual environments. However, maintaining these homogeneous conditions throughout the building leads to unnecessary energy consumption, and does not address the varying thermal and visual comfort needs of the individual occupants. This has led the building science community to pursue personal environmental control (PEC) systems that work in tandem with adaptive centralized ambient comfort systems. These PEC systems create favorable environmental conditions around each occupant, employing specialized equipment, such as a personal thermal conditioning system, task lighting, plug load monitoring and control, window shade control system, and similar systems. Coordinating among personal control systems and with centralized building management systems allows the optimal provision of services such as cooling, lighting, and other such services where they are needed, potentially leading to significant energy efficiency and improved occupant satisfaction. This paper provides an overview of the state of research associated with personalized thermal conditioning and lighting systems. In addition, presents a survey of controls and communication systems that operate these devices. Finally, the paper considers the energy savings potential from a personal thermal comfort and lighting comfort.