Abstract

A significant cause of air pollution in urban areas worldwide is the high volume of road traffic. Long-term exposure to severe pollution can cause serious health issues. One approach towards tackling this problem is to design a pollution-aware traffic routing policy that balances multiple objectives of i) avoiding extreme pollution in any area ii) enabling short transit times, and iii) making effective use of the road capacities. We propose a novel sampling-based approach for this problem. We provide the first construction of a Markov Chain that can sample integer max flow solutions of a planar graph, with theoretical guarantees that the probabilities depend on the aggregate transit length. We designed a traffic policy using diverse samples and simulated traffic on real-world road maps using the SUMO traffic simulator. We observe a considerable decrease in areas with severe pollution when experimented with maps of large cities across the world compared to other approaches.

Abstract

Cognitive Internet of Things (CIoT) supports the organizations to learn from the information (data) arriving from various connected devices, sensors, machines and other sources, and at the same time it inspires intelligence into different business operations, products, customer experiences, and people. Data poising attacks are very serious concerns because they may play a significant factor for businesses and organizations for both financial terms and damaging their reputations, when the Big data analytics on the analyzed data is itself corrupted. To mitigate this issue, in this paper, we suggest a blockchain-based Artificial Intelligence(AI)/Machine Learning(ML)-enabled Big data analytics mechanism for CIoT environment. The comprehensive experimental results have been provided under two circumstances: (1) performance of the ML model under data poisoning attacks and (2) performance of the ML model without data poisoning attacks. In the first case, we show how the data poison attacks can effect the ML model when the data is on some cloud storage (i.e. not in the blockchains), whereas in the second case we show the effect when the data is in the blockchains (i.e., without data poisoning attacks). The experimental results demonstrate that we have significant gains in performance in terms of accuracy, recall, precision and F1 score when there are no data poisoning attacks on the data. Moreover, a detailed blockchain simulation has carried out to demonstrate the practical aspects of the proposed security framework.

Abstract

The authenticated key agreement is one of the major security services that can be used to secure an Internet of Things (IoT) environment, where the devices collect the data and the data is then aggregated at the cloud server, and then a user needs to access the data stored at the server(s) securely. For this purpose, after a mutual authentication performed between a user and the accessed server, a session key needs to be established among them for secure communication. In this article, we design an efficient lattice-based authenticated key exchange protocol using ring-based version of learning with errors assumption for the IoT-enabled smart devices. The proposed protocol is basically a key exchange that uses the reconciliation mechanism. The detailed security analysis under the standard model has been performed along with the informal security analysis to show that the proposed protocol is robust against different attacks. We then simulate the proposed protocol under the NS-3 simulator to measure the network performance parameters like network throughput and latency. A comparative analysis shows that the proposed protocol has superior security, less computational cost, and comparable communication cost when compered these parameters with the other competing schemes.

Abstract

Google Earth (GE) has been very popular since 2005 amongst remote-sensing science enthusiasts as well as administrators for its ease of use and the large archive of multiresolution temporal data covering the entire globe. With time, Google Earth has evolved to publish layers of imagery with spatial resolution estimated to be better than 30 centimeters(since GE does not publish any information related to accuracy of the imagery). Many scholars have studied the positional accuracy of Google Earth imagery from 2008 till 2018. Improvements in the positional accuracy(absolute as well as relative) were reported by many.In this study,it was attempted to understand if the CRS of Google Earth(GE) is contributing to positional inaccuracies when its imagery is assessed using reliable satellite reference data or GPS surveyed data. Relative assessment of GE location coordinates of ground check points was done using Sentinel-2B satellite imagery. Test imagery over regions of Africa, Canada, India and Australia were used and RMSE values were reported. The radial RMSE for the image check points was computed as 12.5 metres; min-max values in Easting direction were [-23.48 m,8.26 m] while min-max values in Northing direction were [-5.77m, 17.26m]. The paper focuses on planimetric mislocation/accuracy between Google Earth and Sentinel-2B and explores direct or indirect relationship between increasing UTM zone numbers and mislocation values. The paper attempts to emphasize that there is a need for the end-user/consumer to understand that, though Google Earth is undeniably the best go-to-resource with open access and high usability rating , it has its own quality issues and should be used with caution in applications where quality and reliability are at stake. GE is indeed an excellent source of ancillary information but it is expected that the end-user be aware of inherent accuracy issues of Google Earth imagery when consuming in applications ranging from as small as a geo-spatial survey by a school community to larger projects requiring high accuracy and precision like mapping, autonomous navigation, control point surveying or satellite calibration. Index Terms—Google Earth, Sentinel -2B, UTM Zone, Relative Planimetric Accuracy , Reliability , High Precision Applications

Abstract

In this paper, we present online algorithm called {it Delaytron} for learning multi class classifiers using delayed bandit feedbacks. The sequence of feedback delays ${d_t}_{t=1}^T$ is unknown to the algorithm. At the $t$-th round, the algorithm observes an example $mathbf{x}_t$ and predicts a label $tilde{y}_t$ and receives the bandit feedback $mathbb{I}[tilde{y}_t=y_t]$ only $d_t$ rounds later. When $t+d_t>T$, we consider that the feedback for the $t$-th round is missing. We show that the proposed algorithm achieves regret of $mathcal{O}left(sqrt{frac{2 K}{gamma}left[frac{T}{2}+left(2+frac{L^2}{R^2Vert WVert_F^2}right)sum_{t=1}^Td_tright]}right)$ when the loss for each missing sample is upper bounded by $L$. In the case when the loss for missing samples is not upper bounded, the regret achieved by Delaytron is $mathcal{O}left(sqrt{frac{2 K}{gamma}left[frac{T}{2}+2sum_{t=1}^Td_t+vert mathcal{M}vert Tright]}right)$ where $mathcal{M}$ is the set of missing samples in $T$ rounds. These bounds were achieved with a constant step size which requires the knowledge of $T$ and $sum_{t=1}^Td_t$. For the case when $T$ and $sum_{t=1}^Td_t$ are unknown, we use a doubling trick for online learning and proposed Adaptive Delaytron. We show that Adaptive Delaytron achieves a regret bound of $mathcal{O}left(sqrt{T+sum_{t=1}^Td_t}right)$. We experimentally show that the proposed approach can learn efficient classifiers even with delayed bandit feedbacks and the accuracy does not degrade much due to delays in feedbacks.

Abstract

The management and regulation of the waste generated from coal-based power plants have been a key issue throughout the world. There is a need for the voluminous utilization of coal ash in India. The bulk utilization of the industrial waste materials like coal ash can control the exploitation of the natural resources like soil in diferent aspects. Here, it has been attempted to evaluate the potential use of coal ash as a foundation/fll material. In addition, physical, geotechnical, morphological, and chemical characterization of the pond ash has been done. Then, fnite element modeling of the vibratory roller compaction was simulated using PLAXIS3D software for the recommendation of the pond ash as a construction material in place of soil. Also, the reduction in CO2 emission with pond ash was compared with that of the soil. The reduction in CO2 emission was found to be about 15% considering several factors like distance from the source station to the test site, types of coal ash used, types of construction equipment employed, etc. The utilization of pond ash as fll material was found to be economical, and its utilization saves around INR 32 to INR 399 per cubic meter. Keywords Coal ash · Foundation/fll material · Finite element modeling · Vibratory roller compaction · CO2 emission

Abstract

Linear regression is a widely used technique to fit linear models and finds widespread applications across different areas such as machine learning and statistics. In most real-world scenarios, however, linear regression problems are often ill-posed or the underlying model suffers from overfitting, leading to erroneous or trivial solutions. This is often dealt with by adding extra constraints, known as regularization. In this paper, we use the frameworks of block-encoding and quantum singular value transformation (QSVT) to design the first quantum algorithms for quantum least squares with general ℓ2-regularization. These include regularized versions of quantum ordinary least squares, quantum weighted least squares, and quantum generalized least squares. Our quantum algorithms substantially improve upon prior results on quantum ridge regression (polynomial improvement in the condition number and an exponential improvement in accuracy), which is a particular case of our result. To this end, we assume approximate block-encodings of the underlying matrices as input and use robust QSVT algorithms for various linear algebra operations. In particular, we develop a variable-time quantum algorithm for matrix inversion using QSVT, where we use quantum eigenvalue discrimination as a subroutine instead of gapped phase estimation. This ensures that substantially fewer ancilla qubits are required for this procedure than prior results. Owing to the generality of the block-encoding framework, our algorithms are applicable to a variety of input models and can also be seen as improved and generalized versions of prior results on standard (non-regularized) quantum least squares algorithms.

Abstract

Password managers (PMs) are becoming increasingly popular on mobile devices, especially on small-screen devices, mainly due to the convenience of automatically filling credentials into login forms. Modern mobile OSes advocate for system-wide autofill frameworks to support autofilling on browsers as well as other apps. Mobile OSes also empower apps to directly render web content within WebView controls without redirecting users to the main browser. par We present a novel technique, called AutoSpill, to leak users' saved credentials during an autofill operation on a webpage loaded into an app's WebView. AutoSpill conveniently dodges the secure autofill process. The majority of popular Android PMs considered in our experiments were found vulnerable to AutoSpill; even when the app hosting the WebView is not actively participating in the leak. Android intermediates in the autofill process because of its app sandboxing. Hence, the responsibility for any credential leakage is often stranded between PMs and the Android system. We investigate the root causes of AutoSpill and propose countermeasures to fundamentally fix AutoSpill for both the parties. We responsibly disclosed our findings to the affected PMs and Android security team.

Abstract

Electric vehicles (EVs) represent the long-term green substitute for traditional fuel-based vehicles. To encourage EV adoption, the trust of the end-users must be assured. In this work, we focus on a recently emerging privacy threat of profiling and identifying EVs via the analog electrical data exchanged during the EV charging process. The core focus of our work is to investigate the feasibility of such a threat at scale. To this end, we first propose an improved EV profiling approach that outperforms the state-of-the-art EV profiling techniques. Next, we exhaustively evaluate the performance of our improved approach to profile EVs in real-world settings. In our evaluations, we conduct a series of experiments including 25032 charging sessions from 530 real EVs, sub-sampled datasets with different data distributions, etc. Our results show that even with our improved approach, profiling and individually identifying the growing number of EVs appear extremely difficult in practice; at least with the analog charging data utilized throughout the literature. We believe that our findings from this work will further foster the trust of potential users in the EV ecosystem, and consequently, encourage EV adoption.

Abstract

In order to advance academic research, it is important to assess and evaluate the academic influence of researchers and the findings they produce. Citation metrics are universally used methods to evaluate researchers. Amongst the several variations of citation metrics, the h-index proposed by Hirsch has become the leading measure. Recent work shows that h-index is not an effective measure to determine scientific impact - due to changing authorship patterns. This can be mitigated by using h-index of a paper to compute hindex of an author. We show that using fractional allocation of h-index gives better results. In this work, we reapply two indices based on the h-index of a single paper. The indices are referred to as: hp-index and hp-frac-index. We run large-scale experiments in three different fields with about a million publications and 3,000 authors. Our experiments show that hp-frac-index provides a unique ranking when compared to h-index. It also performs better than h-index in providing higher ranks to the awarded researcher.