Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconfigurable Intelligent Surface-Aided Full-Duplex mmWave MIMO: Channel Estimation, Passive and Hybrid Beamforming

Millimeter wave (mmWave) full-duplex (FD) is a promising technique for improving capacity by maximizing the utilization of both time and the rich mmWave frequency resources. Still, it has restrictions due to FD self-interference (SI) and mmWave's limited coverage. Therefore, this study dives into FD mmWave MIMO with the assistance of reconfigurable intelligent surfaces (RIS) for capacity improvement. First, we demonstrate the angular-domain reciprocity of FD antenna arrays under the far-field planar wavefront assumption. Accordingly, a strategy for joint downlink-uplink (DL-UL) channel estimation is presented. For estimating the SI channel, the direct channel, and the cascaded channel, the Khatri-Rao product-based compressive sensing (KR-CS), distributed CS (D-CS), and two-stage multiple measurement vector-based D-CS (M-D-CS) frameworks are proposed, respectively. Additionally, we propose a passive beamforming optimization solution based on the angular-domain cascaded channel. With hybrid beamforming architectures, a novel hybrid weighted minimum mean squared error method for SI cancellation (H-WMMSE-SIC) is proposed. Simulations have revealed that joint DL-UL processing significantly improves estimation performance in comparison to separate DL/UL channel estimation. Particularly, when the interference-to-noise ratio is less than 35 dB, our proposed H-WMMSE-SIC offers spectral efficiency performance comparable to fully-digital WMMSE-SIC. Finally, the computational complexity is analyzed for our proposed methods.
An improved lyapunov-function approach to the behavior of diffusion processes in hilbert spaces

We establish a general theorem of Lyapunov's type and derive from it results on the pathwise behavior of diffusion processes in Hilbert spaces. In this way we formulate conditions ensuring that mild solutions of stochastic differential equations are strong. In particular we obtain sufficient conditions for the existence of strong solutions to stochastic reaction-diffusion equations. In important linear cases these conditions are sharp.
Polarization performance analysis of etched diffraction grating demultiplexer using boundary element method

The polarization-dependent characteristics of an etched diffraction grating demultiplexer are analyzed using the boundary element method. Comparisons are made between an echelle grating coated with a metal and a dielectric grating with retro-reflecting facets. The results indicate that an echelle grating coated with a metal can be approximated as a grating with a perfectly conducting surface. A retro-reflecting-facet grating based on a high index material has a lower polarization dependent loss (PDL), while the other performances are similar to an echelle grating coated with a metal. It is also shown that by titling the shaded facets appropriately the overall PDL performance can be improved when the total number of used channels is not too large. The influence of the surface roughness of the shaded facets on the PDL of the device is also investigated for all wavelength channels.
Vortical flow. Part 1. Flow through a constant-diameter pipe

This paper describes an exploration of the behaviour and properties of swirling flow through a constant-diameter pipe. The experiments reveal a complicated transition process as the swirl intensity Ω is increased at fixed pipe Reynolds number Re ≈ 4900. For Ω [les ] 1.09, the vortex was steady, laminar, axisymmetric, and developed slowly with streamwise distance. The upstream velocity profiles were similar to those commonly appearing in the literature in similar apparatus. Spiral vortex breakdown appeared in the test section for 1.09 [les ] Ω [les ] 1.31 and was associated with a localized transition from jet-like to wake-like mean axial velocity profiles. Further increase in Ω caused the breakdown to move upstream of the test section. Downstream, the core of the post-breakdown flow was unsteady and recovered toward jet-like profiles with streamwise distance. At Ω = 2.68, a global transition occurred in which the mean axial velocity profiles suddenly developed an annular axial velocity deficit. At the same time, disturbances began to appear in the outer flow. Further increase in Ω eventually led to an annulus of reversed axial flow and a completely unsteady vortex.
AniMOJity:Detecting Hate Comments in Indic languages and Analysing Bias against Content Creators

Online platforms have dramatically changed how people communicate with one another, resulting in a 467 million increase in the number of Indians actively exchanging and distributing social data. This caused an unexpected rise in harmful, racially, sexually, and religiously biased Internet content humans cannot control. As a result, there is an urgent need to research automated computational strategies for identifying hostile content in academic forums. This paper presents our learning pipeline and novel model, which classifies a multilingual text with a test f1-Score of 88.6% on the Moj Multilingual Abusive Comment Identification dataset for hate speech detection in thirteen Indian regional languages. Our model, Animojity, incorporates transfer learning and SOTA pre- and post-processing techniques. We manually annotate 300 samples to investigate bias and provide insight into the hate towards creators.
PACMAN:PArallel CodeMixed dAta generatioN for POS tagging

Code-mixing or Code-switching is the mixing of languages in the same context, predominantly observed in multilingual societies. The existing code-mixed datasets are small and primarily contain social media text that does not adhere to standard spelling and grammar. Computational models built on such data fail to generalise on unseen code-mixed data. To address the unavailability of quality code-mixed annotated datasets, we explore the combined task of generating annotated code mixed data, and building computational models from this generated data, specifically for code-mixed Part-Of-Speech (POS) tagging. We introduce PACMAN(PArallel CodeMixed dAta generatioN) - a synthetically generated code-mixed POS tagged dataset, with above 50K samples, which is the largest annotated code-mixed dataset. We build POS taggers using classical machine learning and deep learning based techniques on the generated data to report an F1-score of 98% (8% above current State-of-the-art (SOTA)). To determine the efficacy of our data, we compare it against the existing benchmark in code-mixed POS tagging. PACMAN outperforms the benchmark, ratifying that our dataset and, subsequently, our POS tagging models are generalised and capable of handling even natural code-mixed and monolingual data.
English To Indian Sign Language:Rule-Based Translation System Along With Multi-Word Expressions and Synonym Substitution

The hearing challenged communities all over the world face difficulties to communicate with others. Machine translation has been one of the prominent technologies to facilitate communication with the deaf and hard of hearing community worldwide. We have explored and formulated the fundamental rules of Indian Sign Language(ISL) and implemented them as a translation mechanism of English Text to Indian Sign Language glosses. According to the formulated rules and sub-rules, the source text structure is identified and transferred to the target ISL gloss. This target language is such that it can be easily converted to videos using the Indian Sign Language dictionary. This research work also mentions the intermediate phases of the transfer process and innovations in the process such as Multi-Word Expression detection and synonym substitution to handle the limited vocabulary size of Indian Sign Language while producing semantically accurate translations.
CNN-Transformer based Encoder-Decoder Model for Nepali Image Captioning

Many image captioning tasks have been carried out in recent years, the majority of the work being for the English language. A few research works have also been carried out for Hindi and Bengali languages in the domain. Unfortunately, not much research emphasis seems to be given to the Nepali language in this direction. Furthermore, the datasets are also not publicly available in the Nepali language. The aim of this research is to prepare a dataset with Nepali captions and develop a deep learning model based on the Convolutional Neural Network (CNN) and Transformer combined model to automatically generate image captions in the Nepali language. The dataset for this work is prepared by applying different data preprocessing techniques on the Flickr8k dataset. The preprocessed data is then passed to the CNN-Transformer model to generate image captions. ResNet-101 and EfficientNetB0 are the two pre-trained CNN models employed for this work. We have achieved some promising results which can be further improved in the future.
BoNC: Bag of N-Characters Model for Word Level Language Identification

This paper describes the model submitted by NLP_BFCAI team for Kanglish shared task held at ICON 2022. The proposed model used a very simple approach based on the word representation. Simple machine learning classification algorithms, Random Forests, Support Vector Machines, Stochastic Gradient Descent and Multi-Layer Perceptron have been imple- mented. Our submission, RF, securely ranked fifth among all other submissions.
Methods to Optimize Wav2Vec with Language Model for Automatic Speech Recognition in Resource Constrained Environment

Automatic Speech Recognition (ASR) on resource constrained environment is a complex task since most of the State-Of-The-Art models are combination of multilayered convolutional neural network (CNN) and Transformer models which itself requires huge resources such as GPU or TPU for training as well as inference. The accuracy as a performance metric of an ASR system depends upon the efficiency of phonemes to word translation of an Acoustic Model and context correction of the Language model. However, inference as a performance metric is also an important aspect, which mostly depends upon the resources. Also, most of the ASR models uses transformer models at its core and one caveat of transformers is that it usually has a finite amount of sequence length it can handle. Either because it uses position encodings or simply because the cost of attention in transformers is actually O(n²) in sequence length, meaning that using very large sequence length explodes in complexity/memory. So you cannot run the system with finite hardware even a very high-end GPU, because if we inference even a one hour long audio with Wav2Vec the system will crash. In this paper, we used some state-of-the-art methods to optimize the Wav2Vec model for better accuracy of predictions in resource constrained systems. In addition, we have performed tests with other SOTA models such as Citrinet and Quartznet for the comparative analysis.
Graphical Models: Modeling, Optimization, and Hilbert Space Embedding

Over the past two decades graphical models have been widely used as powerful tools for compactly representing distributions. On the other hand, kernel methods have been used extensively to come up with rich representations. This thesis aims to combine graphical models with kernels to produce compact models with rich representational abilities. Graphical models are a powerful underlying formalism in machine learning. Their graph theoretic properties provide both an intuitive modular interface to model the interacting factors, and a data structure facilitating efficient learning and inference. The probabilistic nature ensures the global consistency of the whole framework, and allows convenient interface of models to data. Kernel methods, on the other hand, provide an effective means of representing rich classes of features for general objects, and at the same time allow efficient search for the optimal model. Recently, kernels have been used to characterize distributions by embedding them into high dimensional feature space. Interestingly, graphical models again decompose this characterization and lead to novel and direct ways of comparing distributions based on samples. Among the many uses of graphical models and kernels, this thesis is devoted to the following four areas: Conditional random fields for multi-agent reinforcement learning Conditional random fields (CRFs) are graphical models for modeling the probability of labels given the observations. They have traditionally been trained with using a set of observation and label pairs. Underlying all CRFs is the assumption that, conditioned on the training data, the label sequences of different training examples are independent and identically distributed (iid). We extended the use of CRFs to a class of temporal learning algorithms, namely policy gradient reinforcement learning (RL). Now the labels are no longer iid. They are actions that update the environment and affect the next observation. From an RL point of view, CRFs provide a natural way to model joint actions in a decentralized Markov decision process. They define how agents can communicate with each other to choose the optimal joint action. We tested our framework on a synthetic network alignment problem, a distributed sensor network, and a road traffic control system. Using tree sampling by Hamze & de Freitas (2004) for inference, the RL methods employing CRFs clearly outperform those which do not vii
A simplified analysis of spherical and cylindrical blast waves

Investigations into the behaviour of the gas flowbehind spherical or cylindrical blasts have shown that secondary shocks arise within the original detonation gases. The secondary shock, at first weak, is carried outward with the expanding gases. Subsequently it strengthens and bends back toward the origin, arriving there with high intensity. By using some recently developed techniques in shock dynamics and extending them where necessary, a theory is developed by which the motion of the main shock wave, as well as the formation and subsequent motion of the secondary shock, are given by explicit formulae. In addition, a method for determining, also by explicit formulae, the location of the contact surface between the detonation gases and the outside atmosphere is given. The results of a specific problem, which has been solved by numerically integrating the total equations of motion, and has also been checked experimentally, are compared with the results of the present theory.
CoLI-Kanglish: Word-Level Language Identification in Code-Mixed Kannada-English Texts Shared Task using the Distilka model

Due to the intercultural demographic of online users, code-mixed language is often used by them to express themselves on social media. Language support to such users is based on the ability of a system to identify the constituent languages of the code-mixed language. Therefore, the process of language identification that helps in determining the language of individual textual entities from a code-mixed corpus is a current and relevant classification problem. Code-mixed texts are difficult to interpret and analyze from an algorithmic perspective. However, highly complex transformer- based techniques can be used to analyze and identify distinct languages of words in code-mixed texts. Kannada is one of the Dravidian languages which is spoken and written in Karnataka, India. This study aims to identify the language of individual words of texts from a corpus of code-mixed Kannada-English texts using transformer-based techniques. The proposed Distilka model was developed by fine-tuning the DistilBERT model using the code-mixed corpus. This model performed best on the official test dataset with a macro-averaged F1-score of 0.62 and weighted precision score of 0.86. The proposed solution ranked first in the shared task.
PAR: Persona Aware Response in Conversational Systems

To make the Human Computer Interaction more user friendly and persona aligned, detection of user persona is of utmost significance. Towards achieving this objective, we describe a novel approach to select the persona of a user from pre-determine list of personas and utilize it to generate personalized responses. This is achieved in two steps. Firstly, closest matching persona is detected from a set of pre-determined persona for the user. The second step involves the use of a fine-tuned natural language generation (NLG) model to generate persona compliant responses. Through experiments, we demonstrate that the proposed architecture generates better responses than current approaches by using a detected persona. Experimental evaluation on the PersonaChat dataset has demonstrated notable performance in terms of perplexity and F1-score.
Knowledge Graph-based Thematic Similarity for Indian Legal Judgement Documents using Rhetorical Roles

Automation in the legal domain is promising to be vital to help solve the backlog that currently affects the Indian judiciary. For any system that is developed to aid such a task, it is imperative that it is informed by choices that legal professionals often take in the real world in order to achieve the same task while also ensuring that biases are eliminated. The task of legal case similarity is accomplished in this paper by extracting the thematic similarity of the documents based on their rhetorical roles. The similarity scores between the documents are calculated, keeping in mind the different amount of influence each of these rhetorical roles have in real life practices over determining the similarity between two documents. Knowledge graphs are used to capture this information in order to facilitate the use of this method for applications like information retrieval and recommendation systems.
Multimodal Code-Mixed Tamil Troll Meme Classification using Feature Fusion

Memes became an important way of expressing relevant idea through social media platforms and forums. At the same time, these memes are trolled by a person who tries to get identified from the other internet users like social media users, chat rooms and blogs. The memes contain both textual and visual information. Based on the content of memes, they are trolled in online community. There is no restriction for language usage in online media. The present work focuses on whether memes are trolled or not trolled. The proposed multi modal approach achieved considerably better weighted average F1 score of 0.5437 compared to Unimodal approaches. The other performance metrics like precision, recall, accuracy and macro average have also been studied to observe the proposed system.
Fabrication of the CdSeTe alloyed and CdSeTe/ZnS core-shell quantum dots

The near-infrared (NIR)-emitting quantum dots (Qdots) have great potential for the use in biological imaging and diagnostic applications. In our work, a facile method was developed for the preparation of high quality, water-soluble, and NIR-emitting CdSeTe alloyed Qdots (A-Qdots) with L-cysteine (L-cys) as capping agent. By changing the size and the composition of the A-Qdots, the photoluminescent quantum yields (QYs) can reach as high as 53% and the emission color can be tuned between visible and NIR regions. Based on the fluorescence of the A-Qdots selectively quenched in the presence of Cu2+, the NIR-emitting CdSeTe A-Qdots were applied in ultrasensitive Cu2+ sensing. Furthermore, the prepared CdSeTe A-Qdots have been successfully applied for cell imaging, glucose and cholesterol assay, which demonstrates the great potential of the Qdots for biological applications. In order to improve the biocompatibility of the CdSeTe A-Qdots, new water-soluble CdSeTe/ZnS core-shell Qdots (CS-Qdots) with excellent NIR emission were synthesized in aqueous solution. The prepared CS-Qdots not only possessed high QYs but also exhibited excellent photobstability and favorable biocompatibility. Moreover, the CS-Qdots showed high electrogenerated chemiluminescence (ECL) signal. These characteristics showed their potential applications in cell imaging and biosensing with high sensitivity.
AutoDesc: Facilitating Convenient Perusal of Web Data Items for Blind Users

Web data items such as shopping products, classifieds, and job listings are indispensable components of most e-commerce websites. The information on the data items are typically distributed over two or more webpages, e.g., a ‘Query-Results’ page showing the summaries of the items, and ‘Details’ pages containing full information about the items. While this organization of data mitigates information overload and visual cluttering for sighted users, it however increases the interaction overhead and effort for blind users, as back-and-forth navigation between webpages using screen reader assistive technology is tedious and cumbersome. Existing usability-enhancing solutions are unable to provide adequate support in this regard as they predominantly focus on enabling efficient content access within a single webpage, and as such are not tailored for content distributed across multiple webpages. As an initial step towards addressing this issue, we developed AutoDesc, a browser extension that leverages a custom extraction model to automatically detect and pull out additional item descriptions from the ‘details’ pages, and then proactively inject the extracted information into the ‘Query-Results’ page, thereby reducing the amount of back-and-forth screen reader navigation between the two webpages. In a study with 16 blind users, we observed that within the same time duration, the participants were able to peruse significantly more data items on average with AutoDesc, compared to that with their preferred screen readers as well as with a state-of-the-art solution.
Embodied Agents for Obstetric Simulation Training

Post-partum hemorrhaging is a medical emergency that occurs during childbirth and, in extreme cases, can be life-threatening. It is the number one cause of maternal mortality worldwide. High-quality training of medical staff can contribute to early diagnosis and work towards preventing escalation towards more serious cases. Healthcare education uses manikin-based simulators to train obstetricians for various childbirth scenarios before training on real patients. However, these medical simulators lack certain key features portraying important symptoms and are incapable of communicating with the trainees. The authors present a digital embodiment agent that can improve the current state of the art by providing a specification of the requirements as well as an extensive design and development approach. This digital embodiment allows educators to respond and role-play as the patient in real time and can easily be integrated with existing training procedures. This research was performed in collaboration with medical experts, making a new contribution to medical training by bringing digital humans and the representation of affective interfaces to the field of healthcare.