Abstract

Residensial Energy electricity

Abstract

Locally recoverable codes (LRCs) are known to reduce the repair locality, i.e., the number of nodes accessed during node repair, in distributed storage systems. Regenerating codes, on the other hand, offer a decreased repair bandwidth, which is the repair traffic incurred during node repairs. Locally regenerating codes (LRGCs), which are constructed via intertwining regenerating codes of smaller block lengths, simultaneously offer a small repair locality and repair bandwidth. In this letter, we construct a family of LRGCs where the constituent codes are bandwidth-efficient, minimum bandwidth cooperative regenerating codes. The newly constructed LRGCs are optimal with respect to both minimum distance and rate.

Abstract

Rural areas are often neglected while deploying newer mobile technologies. Hence, these place are digitally disconnected from the world. To overcome this challenge, 5G network slicing supporting multi-tenancy, also known as neutral host network, is studied to improve rural connectivity. The infrastructure provider (InP) deploys the rural 5G network and mobile network operators (MNOs) lease the slices from InP to serve their end-users. This aims to study the value network configuration (VNC) for the 5G network slicing architecture to understand the possible business model. As a result, three configurations are defined driven by micro-operator, MNO and community end-users respectively. The business models are constructed using SWOT analysis and business canvas models. The revenue streams for the proposed rural network are analyzed.

Abstract

The design of monolithic power management circuits is critical in energy harvesting applications. A Pulse width modulation (PWM) boost converter with feedforward and feedback control mitigates the changes in input-output parameters and avoids the use of compensation circuit. However, the conventional design of a clock generator in feedback path employs power hungry comparators and latches. This paper proposes an improved design of clock generator replacing comparators and latches with a schmitt trigger circuit. As a result, a low power and a low ripple PWM was acheived. The design was implemented in UMC 180 nm CMOS technology generating a stable output of 3.3 V for an input range of 1.2 V - 1.6 V. Due to the application of the proposed technique, a reduced power consumption of 60.19 mW and low ripple voltage of 18.29 mV was achieved, for a maximum load current of 100 mA at an efficiency of 98%. Thus making the design suitable for energy harvesting applications.

Abstract

Model checking is now a standard technology for verifying large and complex systems. While there are a range of tools and techniques to verify various properties of a system under consideration, in this work, we restrict our attention to safety checking procedures using explicit state space generation. The necessary hardware resources required in this approach depends on the model complexity and the resulting state transition graph that gets generated. This cannot be estimated apriori. For reasonably realistic models, the available main memory in even high end servers may not be sufficient. Hence, we have to use distributed safety verification approaches on a cluster of nodes. However, the problem of estimating the minimum number of nodes in the cluster for the verification procedure to complete successfully remains unsolved. In this paper, we propose a dynamically scalable model checker using an actor based architecture. Using the proposed approach, an end user can invoke a model checker hosted on a cloud platform in a push button fashion. Our safety verification procedures automatically expands the cluster by requesting more virtual machines from the cloud provider. Finally, the user gets to pay only for the hardware resources he rented for the duration of the verification procedure. We refer to this as Model Checking as Service. We approach this problem by proposing an asynchronous algorithm for safety checking in actor framework. The actor based approach allows for scaling the resources on a need basis and redistributes the work load transparently through state migration. We tested our approach by developing a distributed version of SpinJA model checker using Akka actor framework. We conducted our experiments on Google Cloud Engine (GCE) platform wherein we scale our resources automatically using the GCE API. On large models such as anderson.8 from BEEM benchmark suite, our approach reduced the model checking cost in dollars by 8.6x while reducing the wall clock time to complete the safety checking procedure 5.5x times.

Abstract

In one aspect, a computerized method of automatically generating video content using a one click artificial-intelligence assistant for generating an animation video includes the step of providing a do-it-yourself (DIY) computer animation generation system. The DIY computer animation generation system includes an animation generation dashboard. The method includes the step of providing a one click AI assistant for generating an animation video in the DIY computer animation generation system. The method includes the step of providing a one click virtual button that is displayed in the animation generation dashboard. The one click AI assistant automatically suggests a set of animation choices to a user on a single button press of the one click virtual button.

Abstract

—Social media platforms have taken political and cultural conversations to an online platform making them more accessible. Ability to anonymously post has allowed more people to participate fearlessly. However, this has also led to an opportunity to spread miss information and manipulative content. Political groups around the globe have used Bot accounts to help spread their preferred narrative online during elections. In the midst of 2019 Indian Lok Sabha Elections speculations were made about the presence of cyber-troops/IT Cells which operate fake accounts and push propaganda. Our finding suggests that a portion of Bot accounts seems to be operated by humans in the background. These accounts have a very distinct usage pattern on Twitter compared to legitimate human users. Our experiments also point out that only 1.3% of total interactions are directed from Humans to Bots, showing Bot accounts inability to gel well in the online social network. Keywords-Social Networks, Bots, Twitter, Elections

Abstract

Given that India is now moving towards automated solutions to curb traffic violations and road accidents, we focus our efforts on characterizing these violations in Indian cities. In this work, we present our characterization of the traffic violations via an Automated e-challan (electronic trafficviolation receipt) issuance system of Ahmedabad and New Delhi. To explore this, we collected an exhaustive dataset of over 6 million e-challans. Characterizing the fine payment behavior, we find that 57% of unique vehicles in Ahmedabad are involved in repeat offenses. The temporal analysis shows a significant difference in e-challans issued during the festivals. Spatially, different violation types are distributed differently with the existence of certain unique hotspots. Finally, we also demonstrate how e-challans can act as a proxy measure to analyze the efficacy of the Motor Vehicles (Amendment) Act 2019. Our work suggests that high penalties may not have a long term impact on decreasing traffic violations. Keywords-big data; traffic violations; e-challan; transport systems; state policy; surveillance;

Abstract

In 2015, 391,000 people were injured due to distracted driv-ing in the US. One of the major reasons behind distracteddriving is the use of cell-phones, accounting for 14% of fatalcrashes. Social media applications have enabled users to stayconnected, however, the use of such applications while driv-ing could have serious repercussions - often leading the userto be distracted from the road and ending up in an accident.In the context of impression management, it has been discov-ered that individuals often take a risk (such as teens smokingcigarettes, indulging in narcotics, and participating in unsafesex) to improve their social standing. Therefore, viewing thephenomena of posting distracted driving posts under the lensof self-presentation, it can be hypothesized that users oftenindulge in risk-taking behavior on social media to improvetheir impression among their peers. In this paper, we firsttry to understand the severity of such social-media-based dis-tractions by analyzing the content posted on a popular socialmedia site where the user is driving and is also simultane-ously creating content. To this end, we build a deep learningclassifier to identify publicly posted content on social mediathat involves the user driving. Furthermore, a framework pro-posed to understand factors behind voluntary risk-taking ac-tivity observes that younger individuals are more willing toperform such activities, and men (as opposed to women) aremore inclined to take risks. Grounding our observations inthis framework, we test these hypotheses on 173 cities acrossthe world. We conduct spatial and temporal analysis on a city-level and understand how distracted driving content postingbehavior changes due to varied demographics. We discoverthat the factors put forth by the framework are significant inestimating the extent of such behavior.

Abstract

High-temperature weather conditions induce geometrical distortions in images which can adversely affect the performance of a computer vision model performing downstream tasks such as semantic segmentation. The performance of such models has been shown to improve by adding a restoration network before a semantic segmentation network. The restoration network removes the geometrical distortions from the images and shows improved segmentation results. However, this approach suffers from a major architec- tural drawback that is the restoration network does not learn directly from the errors of the segmentation network. In other words, the restoration network is not task aware. In this work, we propose a semantic feedback learning approach, which improves the task of semantic segmentation giving a feedback response into the restoration network. This response works as an attend and fix mechanism by focusing on those areas of an image where restoration needs improvement. Also, we proposed loss functions: Iterative Fo- cal Loss (iFL) and Class-Balanced Iterative Focal Loss (CB-iFL), which are specifically designed to improve the performance of the feedback network. These losses focus more on those samples that are continuously miss-classified over successive iterations. Our approach gives a gain of 17.41 mIoU over the standard segmentation model, including the additional gain of 1.9 mIoU with CB-iFL on the Cityscapes dataset.