Abstract

In this paper, we propose spectrum sensing schemes for a secondary user (SU) with multiple antennas to detect a primary user (PU) transmission in a cognitive mmWave network. The channel model considered at mmWave carrier frequencies is the clustered Rician fading channel, which has few multipaths. For the considered scenario, we propose three beamformed energy detection (BFED) schemes where beamforming is done in the dominant direction of arrival (DoA) at the SU and then energy detection (ED) is applied. The three schemes differ in the amount of information assumed about the DoAs at the SU. The performance of these schemes has been compared with the traditional ED and maximal ratio combining (MRC) schemes for multiantenna systems. It is shown through simulations that the proposed BFED approaches provide significant performance gains over the ED and negligible loss as compared to the MRC, which makes an impractical assumption of the channel between the PU and the SU to be exactly known.

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

There have been numerous attempts in explaining the general learning behaviours using model-based and model-free methods. While the model-based control is flexible yet computationally expensive in planning, the model-free control is quick but inflexible. Multiple arbitration schemes have been suggested to achieve the data efficiency and computational efficiency of model-based and model-free control schemes, respectively. In this context, we propose a quantitative ’valueof-information’ based arbitration between both the controllers in order to establish a general computational framework for skill learning. The interacting model-based and model-free reinforcement learning processes are arbitrated using an uncertainty-based value-of-information estimation. We further show that our algorithm performs better than Q-learning as well as Q-learning with experience replay.

Abstract

Several canonical experimental paradigms (serial reaction task, mxn task, etc.) have been proposed to study the typical behavioural phenomenon in sequential key-press tasks. However, not much work has been done on studying motor sequencing in grid-navigation tasks. In this work, using empirical examinations, we systematically show grid-navigation task as an instance of skill learning paradigm. The participants performed Grid-Sailing Task (GST), which required navigating (by executing sequential key-presses) a 5x5 grid from start to goal position while using a particular key-mapping among the 3 cursor movement directions and the 3 keyboard buttons. We employ two different experiments to argue for the learning of cognitive strategies as well as motor sequences. By rejecting the motor adaptation argument and validating the law of practice, we characterize GST as a skill learning task. We further argue for advantages of GST as a general, canonical task over others for use in skill learning studies.

Abstract

Several canonical experimental paradigms (serial reaction task, mxn task, etc.) have been proposed to study the typical behavioural phenomena in a sequential motor key-press task. The repeated execution of visuomotor sequences in such paradigms lead to overall performance improvement such that the inter-response intervals in between certain sub-sequences decreases as compared to that across other sub-sequences. This efficient and hierarchical cluster organisation is called \textit{motor chunking}. We provide empirical evidence for motor chunking in grid-navigation sequencing tasks. The participants performed Grid-Sailing Task (GST) [Fermin et. al., 2010] that required navigating a 10x10 grid from start to goal position while using a particular key-mapping between the 3 cursor movement directions and the 3 keyboard buttons. This study confirms the emergence of subject-specific, unique temporal patterns related to chunking after substantial practice.

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.