Abstract

This paper focuses on optimal beamforming to maximize the mean signal-to-noise ratio (SNR) for a passive reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) downlink system. We consider a realistic setting where both the direct and indirect (through RIS) links to the user equipment (UE) experience correlated Rician fading. Such a general fading model is particularly important to capture the impact of line-of-sight (LoS) and correlated multipath fading that may occur due to the compact placement of a large number of RIS elements. The assumption of passive RIS imposes the unit modulus constraint, which makes the beamforming problem non-convex. To tackle this issue, we apply semidefinite relaxation (SDR) to obtain the optimal phase-shift matrix and propose an iterative algorithm to obtain a statistically optimal solution for the transmit beamforming vector and RIS-phase shift matrix. Further, to measure the performance of the proposed beamforming scheme, we analyze key system performance metrics such as outage probability (OP) and ergodic capacity (EC). Similar to the existing works, the OP and EC evaluations rely on the numerical computation of the proposed iterative algorithm. However, this is not conducive to revealing the functional dependence of system performance on key parameters such as line-of-sight (LoS) components, correlated fading, number of reflecting elements, number of antennas at the base station (BS), and fading factor. To overcome this major limitation, we derive closed-form expressions for the optimal beamforming vector and phase shift matrix for various special cases of the above general fading model. These fixed-point solutions aid in deriving a closed-form solution for OP that further provides a direct evaluation of EC. These mathematical expressions are then used to gain useful insights into the system’s performance. We analytically establish the fact that correlated fading is more beneficial than the independent and identically distributed (i.i.d.) case when the LoS components are blocked. Further, we also analytically demonstrate that the maximum mean SNR improves linearly/quadratically with the number of RIS elements in the absence/presence of LoS component under i.i.d. fading.

Abstract

Developing methods for extracting relevant legal information to aid legal practitioners is an active research area. In this regard, research efforts are being made by leveraging different kinds of information, such as meta-data, citations, keywords, sentences, paragraphs, etc. Similar to any text document, legal documents are composed of paragraphs. In this paper, we have analyzed the resourcefulness of paragraph-level information in capturing similarity among judgments for improving the performance of precedence retrieval. We found that the paragraph-level methods could capture the similarity among the judgments with only a few paragraph interactions and exhibit more discriminating power over the baseline document-level method. Moreover, the comparison results on two benchmark datasets for the precedence retrieval on the Indian supreme court judgments task show that the paragraph-level methods exhibit comparable performance with the state-of-the-art methods.

Abstract

The advances in Artificial Intelligence are creating new opportunities to improve people’s lives around the world, from business to healthcare, from lifestyle to education. For example, some systems profile the users using their demographic and behavioral characteristics to make certain domain-specific predictions. Often, such predictions impact the user’s life directly or indirectly (e.g., loan disbursement, determining insurance coverage, shortlisting applications, etc.). As a result, the concerns over such AI-enabled systems are also increasing. To address these concerns, such systems are mandated to be responsible: transparent, fair, and explainable to developers and end-users. In this paper, we present ComplAI, a unique framework to enable, observe, analyze and quantify explainability, robustness, performance, fairness, and model’s behavior in drift scenarios, and to provide a single Trust Factor that evaluates different supervised Machine Learning models not just from their ability to make correct predictions but from overall responsibility perspective. The framework helps users to (a) connect their models and enable explanations, (b) assess and visualize different aspects of the model such as robustness, drift susceptibility, and fairness, and (c) compare different models (from different model families or obtained through different hyperparameter settings) from an overall perspective thereby facilitating actionable recourse for improvement of the models. ComplAI is model agnostic and works with different supervised machine learning scenarios (i.e., Binary Classification, Multi-class Classification, and Regression) and frameworks (viz. scikit-learn, TensorFlow, etc.). It can be seamlessly integrated with any ML life-cycle framework. Thus, this already deployed framework aims to unify critical aspects of Responsible AI systems for regulating the development process of such real systems. The poster paper of ComplAI is also available

Abstract

Embodiments herein provide a system and method for automatically generating a sign language video from an input speech using the machine learning model. The method includes (i) extracting a plurality of spectrograms of an input speech by (a) encoding, using an encoder, a time domain series of the input speech to a frequency domain series, and (b) decoding, using a decoder, a plurality of tokens for time steps of the frequency domain series, (ii) generating a plurality of pose sequences for a current time step of the plurality of spectrograms using a first machine learning model, and (iii) automatically generating, using a discriminator of a second machine learning model, a sign language video for the input speech using the plurality of pose sequences and the plurality of spectrograms when the plurality of pose sequences are matched with corresponding the plurality of spectrograms that are extracted.

Abstract

Warm and dry climate extremes have large impact on water resources, agriculture, ecosystems, and human health. The present study used high-resolution rainfall and temperature data at 0.25-degree resolution from 1951 to 2014 to investigate the occurrence of compound warm and dry extreme events across India. The simultaneous occurrence of temperature exceeding the 75th percentile and a Standardized Precipitation Evapotranspiration Index (SPEI) of less than –1 is considered to represent the compound extreme event of warm and dry. The study reveals a strong negative correlation between temperature and SPEI across most of the country except south-west of India. Analysis reveals that about 3.1% of increase in the spatial areal extent of Warm/Dry compound extreme events allover India. The insights derived from this research could significantly contribute to shaping climate change mitigation and adaptation strategies related to compound extreme events.

Abstract

The scalability of remote Internet of Things (IoT) deployments into verticals of smart cities relies primarily on the communication protocols and data platforms employed. Accordingly, the adoption of LoRaWAN, Sigfox, NB-IoT, etc. has risen in recent years. These protocols have been created to cater to the low power and long range requirements of IoT. In this paper, we evaluate one such protocol, Wi-SUN (Wireless Smart Ubiquitous Network), that additionally offers excellent redundancy owing to its use of a mesh topology. Wi-SUN is based on the IEEE 802.15.4g standard and uses license-free sub-GHz bands. However, IoT networks using any communication protocol require a knowledge of signal propagation and attenuation in real-world scenarios. Thus, we evaluated the path loss of WiSUN transmissions by analysing the RSSI values inside a typical city building and an urban outdoor habitat

Abstract

In the twenty-first century, water quality monitoring is a growing challenge in inland water bodies. Therefore, monitoring the current state of the water bodies is highly important. The remote sensing data is important for effective monitoring that covers a large area. In recent decades, chlorophyll-a (as a proxy) has been a significant indicator of nutrient contamination and also indicates the qualitative status of the water bodies. Besides, the waterbody's temperature also plays an important role in the biological and chemical processes. Hence, the present study aims to detect the chlorophyll-a spread area and temperature of the Bhadra Reservoir, Tungabhadra River system, India. To explore the quality of the reservoir, Sentinel 2A and Landsat 8 satellite images were used for the years 2017 and 2018. The study results noticed that chlorophyll-a spread percent was higher in summer months compared with the water spread area of the reservoir, i.e., 74.5% in 2017 and 61.4% in 2018 from the total water spread area. The study also revealed that as the temperature rises, the chlorophyll-a spread also increases in the reservoir. It was also observed that the October month has a low chlorophyll-a spread, i.e., 32.04% in 2017 and 24.35% in 2018, because the one side reservoir is covered by forest area. Thus, there is a need to explore further to understand the impact of forest area on the reservoir and the temporal changes of the water quality parameters that decide the water bodies quality.

Abstract

Social media was one of the most popular forms of communication among young people of a certain class demographic during the pandemic. Consequently, crucial debates and discussions about the pandemic crisis itself have also developed on social media platforms, making them a great primary source to study the experiences of the LGBTQ+ community during the pandemic. We conducted research using LDA topic modelling on a sub-reddit from the Reddit platform to understand the nature of this discourse. LDA Topic modelling is useful in identifying patterns and themes in large volumes of unstructured data and is an appropriate tool for analyzing social media posts. The results from our preliminary research in this area suggest that institutions such as the health care system, justice system, and legislative system perpetuated systematic inequalities against LGBTQ+ communities during the pandemic, thereby adding to the pre-existing stigma against them during a global crisis. Data from subreddits such as ‘lgbt’ have also suggested a shift in tone in these discussions from the period during the pandemic to the period after the pandemic. We intend to now expand our analysis to other platforms, such as Twitter and Facebook to verify and qualify our approach and understanding of this problem. Researchers, such as Ahmed & Sifat (2021), Pandya & Redcay (2021) and Bhalla & Agarwal (2021), working on qualitative studies have given us enough evidence that proves that inequalities and violence against these communities had intensified during the pandemic. Although research has been done showing that COVID-19 pandemic correlates to an increased amount of discrimination towards the LGBTQ+ community, no research prior to this study has examined content available on Twitter, Facebook and Reddit using LDA topic modelling to understand this correlation. By conducting this research, we will also be able to offer an informed analysis of the effectiveness of computational tools in the study of gender and look at this population from a new perspective.

Abstract

Is the capability to improvise only available to humans? Is it possible to generate musical phrases using Automata Theory? Is it possible to improvise in North Indian classical music using constructs like Context free grammars? In this paper we propose a method to generate improvised sequences for North Indian classical music using context free grammar. We choose to focus on yamanrāga as a case study. We discuss scope of the context free grammar, primary rules for context free grammar based on yamanrāga, rules for stationary, ascending, descending, circulating and microtonal patterns. We also discuss shortcomings of this approach in the discussion section.

Abstract

Video frame prediction is an interesting computer vision problem of predicting the future frames of a video sequence from a given set of context frames. Video prediction models have found wide-scale perspective applications in autonomous navigation, representation learning, and healthcare. However, predicting future frames is challenging due to the high dimensional and stochastic nature of video data. This work proposes a novel cycle consistency loss to disentangle video representation into a low dimensional time-dependent pose and time-independent content latent factors in two different VAE based video prediction models. The key motivation behind cycle consistency loss is that future frame predictions are more plausible and realistic if they reconstruct the previous frames. The proposed cycle consistency loss is also generic because it can be applied to other VAE-based stochastic video prediction architectures with slight architectural modifications. We validate our disentanglement hypothesis and the quality of long-range predictions on standard synthetic and challenging real-world datasets such as Stochastic Moving MNIST and BAIR.