Abstract

The paper presents a seismic safety assessment of unreinforced masonry (URM) building using two approaches. The first approach uses the ‘Pier Analysis’ method, based on the concept of equivalent lateral stiffness, where in-plane and out-of-plane actions are considered independently. The second approach is developed with the program SAP2000, where the linear response is evaluated using continuum ‘finite element modelling’ (FEM). Both methods are compared to evaluate the safety of wall piers and the differences in the outcomes under combined gravitational and lateral seismic forces. The analysis results showed that few wall elements are unsafe in in-plane and out-of-plane tension. It is also observed that the pier analysis method is conservative compared to FEM, but can be used as a simplified and quick tool in design offices for safety assessment, with reasonable accuracy. To safeguard the URM wall piers under lateral loads, a retrofitting technique is adopted by providing vertical and horizontal belts called splints and bandages, respectively, using welded wire mesh (WWM) reinforcement. The study using the ‘Pier Analysis’ shows that the lateral load capacity of unsafe URM piers can be enhanced up to 3.67 times and made safe using the applied retrofitting technique. Further, the retrofitting design methodology and recommendations for application procedures to on-site URM buildings are discussed in detail.

Abstract

Visual data obtained during driving scenarios usually con-tain large amounts of text that conveys semantic information necessary to analyse the urban environment and is integral to the traffic control plan. Yet, research on autonomous driving or driver assistance systems typically ignores this information. To advance research in this direction, we present RoadText-3K, a large driving video dataset with fully anno-tated text. RoadText-3K is three times bigger than its predecessor and contains data from varied geographical locations, unconstrained driving conditions and multiple languages and scripts. We offer a comprehensive analysis of tracking by detection and detection by tracking methods ex-ploring the limits of state-of-the-art text detection. Finally, we propose a new end-to-end trainable tracking model that yields state-of-the-art results on this challenging dataset. Our experiments demonstrate the complexity and variability of RoadText-3K and establish a new, realistic benchmark for scene text tracking in the wild.

Abstract

Bluetooth technology has enabled short-range wireless communication for billions of devices. Bluetooth Low-Energy (BLE) variant aims at improving power consumption on battery-constrained devices. BLE-enabled devices broadcast information (e.g., as beacons) to nearby devices via advertisements. Unfortunately, such functionality can become a double-edged sword at the hands of attackers. In this paper, we primarily show how an attacker can exploit BLE advertisements to exfiltrate information from BLE-enable devices. In particular, our attack establishes a communication medium between two devices without requiring any prior authentication or pairing. We develop a proof-of-concept attack framework on the Android ecosystem and assess its performance via a thorough set of experiments. Our results indicate that such an exfiltration attack is indeed possible though with a limited data rate. Nevertheless, we also demonstrate potential use cases and enhancements to our attack that can further its severeness. Finally, we discuss possible countermeasures to prevent such an attack.

Abstract

AI is adept at using large quantities of data, sometimes sensitive personal data, and can adversely affect individuals’ privacy. Data privacy concerns significantly impact the course of next-generation AI. Users do not trust anyone withholding their data and need privacy-preserving intelligent systems. In addition, several regulations mandate that organizations handle users’ data in ways that do not affect their privacy and provide them control on their data. Federated Learning emerged as a privacy-preserving technology for data-intensive machine learning by training the models on-site or on-device. However, several concerns related to federated learning emerged due to: (i) dynamic, distributed, heterogeneous, and collaborative nature of client devices, (ii) membership inference and model inversion attacks affecting the overall privacy and security of FL systems, (iii) the need for strict compliance to data privacy and protection laws, (iv) the vulnerabilities at local client devices leading to data leakage, and (iv) diversity and ubiquity of smart devices collecting real-time multimodal data leading to lack of standardization efforts for security and privacy management framework. In this paper, we discuss (a) how federated learning can help us withholding privacy, (b) the need for improving security and privacy in federated learning systems, (c) the privacy regulations and their application to federated learning in various business domains, (d) proposed a federated recommender system and demonstrated the performance that matches the central setting.

Abstract

The exact determination of settlement and the time required to complete that settlement is very important from the safety point of view. There are several methods available to find the rate of consolidation that is highly dependent on the settlement versus time curve. Here, it has been attempted to identify the suitable method for the determination of the rate of consolidation of coal ash. After analyzing the value of the coefficient of consolidation (Cv) estimated from various methods, the rectangular hyperbola method (RH method) is found to be in best linear correlation between the measured Cv and calculated Cv. The coal ash shows an increment in Cv with the increase in effective pressure. The Cv obtained from the RH method is higher than the Cv obtained from Taylor’s and Casagrande method that helps in close approximation with field Cv. The precise determination of Cv helps in better prediction of the time required for the consolidation settlement that will increase the performance of the structure.

Abstract

The emergence of vehicle connectivity technologies and associated applications have paved the way for increased consumer interest in connected vehicles. These modern day vehicles are now capable of sending/receiving vast amounts of data and offloading computation (which is one possible service) to servers thereby improving safety, comfort, driving experience, etc. In the early stages of connectivity, all the data communication and computation offloading happened between the cloud server and the vehicles. However, this is not feasible in scenarios having strict timing requirements and bandwidth cost constraints. Vehicular Edge Computing (VEC) demonstrated an efficient way to tackle the above problem. In order to optimally utilize the resources of the edge servers for data delivery, an efficient edge resource allocation framework needs to be developed. In a recent work, data/service delivery to connected vehicles assumed a worst-case scenario that all vehicles with routes passing through an edge appear in the edge coverage region simultaneously. However, this worst-case scenario is very pessimistic, which results in overestimation of edge resources. We address this by precisely computing the set of vehicles which simultaneously appear in the coverage region of an edge (which we call textit{vehicle overlaps}). In this work, we first propose an optimization framework for edge resource allocation that minimizes the bandwidth cost of data delivery to connected vehicles while considering the traffic flow and vehicle overlaps. Then, we propose an efficient heuristic to deliver data based on minimizing global edge bandwidth cost gradient under vehicle overlaps. We demonstrate the improvement in resource allocation considering vehicle overlaps. Using real world traffic data, we also demonstrate reduction in data delivery times using the proposed heuristic.

Abstract

In recent years, semi-transparent building-integrated photovoltaics (BIPV) technology has attracted attention for its renewable energy utilization. This research aims to develop a methodology for assessing the energy-saving potential of semi-transparent photovoltaic (STPV) window, which has a complex relationship with daylighting and air conditioning (AC) electricity consumption. A case study has been developed for the composite climate of Hyderabad, India. The study examines four commercially available single and double pane STPV windows and similar non-photovoltaic windows. For evaluation, an automated parametric simulation tool was developed to compute the net electricity consumption (NEC) for a representative model building with different window systems, and several window-to-wall ratios (WWR) and orientations. The result shows benefit in adopting the STPV window system across all directions and higher optimal WWR in STPV window as compared to normal window with the same opto-thermal characteristics.

Abstract

India Meteorological Department (IMD) has started block-level level agromet advisory (AA) service from the year 2015 and is currently operating in a few blocks of each state across India. In a block-level AA service, on every Tuesday and Friday, AA is being prepared for each block based on the block-level Medium Range weather Forecast (MRF). In this paper, we propose a framework to improve the preparation of block- level AA by modeling a weather situation as “Category-based Weather Condition (CWC)” and exploiting both “temporal reuse” and “spatial reuse” of AA based on the similarity among CWCs. The weather data analysis for 12 blocks of Telangana by considering the phenophase-specific CWCs of Rice crop showed that there is a scope to improve the efficiency of block-level AA bulletin preparation process by exploiting reuse

Abstract

here are certain dynamics which although are non-Markovian but never show information backflow. We show that if two such dynamical maps are considered in a scenario where the order of application of these two dynamical maps are not definite, then the effective channel can manifest information backflow. In particular, we use quantum SWITCH to activate such channels. In contrast, those channels can never be activated even if we use many copy of such channels in series or parallel action. We then investigate the actual cause and dynamics behind the quantum SWITCH experiment and find out that after the action of quantum SWITCH both the CP (Complete Positive)- divisiblity and P (Positive)- divisibility of the channel breaks down, triggering the information backflow. Our investigations shed new light on the fundamental cause of the advantages of quantum SWITCH

Abstract

Directions of arrival (DOA) estimation or localization of sources is an important problem in many applications for which numerous algorithms have been proposed. Most lo- calization methods use block-level processing that combines multiple data snapshots to estimate DOA within a block. The DOAs are assumed to be constant within the block duration. However, these assumptions are often violated due to source motion. In this paper, we propose a signal model that cap- tures the linear variations in DOA within a block. We applied conventional beamforming (CBF) algorithm to this model to estimate linear DOA trajectories. Further, we formulate the proposed signal model as a block sparse model and sub- sequently derive sparse Bayesian learning (SBL) algorithm. Our simulation results show that this linear parametric DOA model and corresponding algorithms capture the DOA tra- jectories for moving sources more accurately than traditional signal models and methods.