Miracle-dz/UniFAR-FTUs huggingface dataset:
Canonical extensions of bounded lattices and natural duality for default bilattices

This thesis presents results concerning canonical extensions of bounded lattices and natural dualities for quasivarieties of default bilattices. Part I is dedicated to canonical extensions, while Part II focuses on natural duality for default bilattices. A canonical extension of a lattice-based algebra consists of a completion of the underlying lattice and extensions of the additional operations to the completion. Canonical extensions find rich application in providing an algebraic method for obtaining relational semantics for non-classical logics. Part I gives a new construction of the canonical extension of a bounded lattice. The construction is done via successive applications of functors and thus provides an elegant exposition of the fact that the canonical extension is functorial. Many existing constructions are described via representation and duality theorems. We demonstrate precisely how our new formulation relates to existing constructions as well as proving new results about complete lattices constructed from graphs. Part I ends with an analysis of the untopologised structures used in two methods of construction of canonical extensions of bounded lattices: the untopologised graphs used in our new construction, and the so-called ‘intermediate structure’. We provide sufficient conditions for the intermediate structure to be a lattice and, for the case of finite lattices, identify when the dual graph is not a minimal representation of the lattice. Part II applies techniques from natural duality theory to obtain dualities for quasivarieties of bilattices used in default logic. Bilattices are doubly-ordered algebraic structures which find application in reasoning about inconsistent and incomplete information. This account is the first attempt to provide dualities or representations when there is little interaction required between the two orders. Our investigations begin by using computer programs to calculate dualities for specific examples, before using purely theoretical techniques to obtain dualities for more general cases. The results obtained are extremely revealing, demonstrating how one of the lattice orders from the original algebra is encoded in the dual structure. We conclude Part II by describing a new class of default bilattices. These provide an alternative way of interpreting contradictory information. We obtain dualities for two newly-described quasivarieties and provide insights into how these dual structures relate to previously described classes of dual structures for bilattices.
Offensive Language Identification Using Hindi-English Code-Mixed Tweets and Code-Mixed Data Augmentation

The Code-mixed text classification is challenging due to the lack of code-mixed labeled datasets and the non-existence of pre-trained models. This paper presents the HASOC-2021 offensive language identification results and main findings on code-mixed (Hindi-English) Subtask2. In this work, we have proposed a new method of code-mixed data augmentation using synonym replacement of Hindi and English words using WordNet, and phonetics conversion of Hinglish (Hindi-English) words. We used a 5.7k pre-annotated HASOC-2021 code-mixed dataset for training and data augmentation. The proposal’s feasibility was tested with a Logistic Regression (LR) used as a baseline, Convolutional Neural Network (CNN), and BERT with and without data augmentation. The research outcomes were promising and yields almost 3% increase of classifier accuracy and F1 scores as compared to baseline. Our official submission showed a 66.56% F1 score and ranked 8th position in the competition.
CHIMA: a Framework for Network Services Deployment and Performance Assurance

Network Function Virtualization has dramatically increased the flexibility in the deployment of network services, however the execution of virtual functions on compute nodes equipped with general purpose hardware can result in worse performance compared to the middleboxes they aim to replace. The use of programmable network hardware to perform part of the processing at line rate can drastically increase the throughput while retaining the flexibility.This work presents a new framework, called CHIMA, which extends the capabilities of other frameworks proposed in the literature for the deployment of heterogeneous Service Function Chains (SFCs). Heterogeneous SFCs comprise a combination of virtual functions meant to be executed in containers running on general purpose hardware and of functions for programmable switches written using the P4 language. CHIMA exploits programmable data planes to perform real time monitoring of the services through In-band Network Telemetry and uses the collected information to guarantee the requested levels of performance by redeploying and rerouting sections that are affected by adverse conditions, allowing applications with critical requirements to be deployed as SFCs.The solution has been tested by emulating various topologies and services on the FOP4 platform with bmv2 switches. The analysis shows that the system is capable of detecting faults in the order of hundreds of milliseconds, and the overhead it causes in the process of redeployment is negligible compared to the startup time of functions. Measurements also reveal that the current bottleneck for the runtime relocation of heterogeneous functions is the redeployment and reconfiguration of P4 programs.
Fake News Detection in Urdu Language using BERT

With the increase in popularity of social media, we can see an increase in the amount of Fake News in circulation, leading to misleading public opinion. Thus a system of Fake News detection is necessary to avoid such consequences. Most of such existing Fake News detection systems work with resource-rich languages like English and Spanish, but very few systems can work with low resource languages like Urdu. The current study focuses on detecting Fake News in the Urdu language using Machine and Deep learning techniques. The ‘UrduFake’ data is used in this research, provided to us as a shared task of FIRE-2021. The experimental outcomes of various models showed that the Transfer learning models performed better than the Machine learning models and achieved a weighted average F1-score of 0.87 and 0.61 on the validation and test dataset.
Sentiment Analysis using Cross Lingual Word Embedding Model

Sentiment analysis deals with analysing the given text and classifying whether the text is a positive, negative or neutral one. The sentiment analysis forms the base for applications where the public views could be known. This paper shows how a multi label classification of the given text could be implemented by considering the sentiment associated with the text. The models that are applied for monolingual sentiment analysis may not provide good results when it is extended for code mixed data. When a Cross Lingual model was applied to the training data-set provided by Dravidian Code-mix FIRE 2021 for the Task A which uses Tamil - English code mixed data, it was able to classify the test data-set with an average F1 score of 0.514.
Multiplicity of compact group representations and applications to Kronecker coefficients

These notes are an expanded version of a talk given by the second author. Our main interest is focused on the challenging problem of computing Kronecker coefficients. We decided, at the beginning, to take a very general approach to the problem of studying multiplicity functions, and we survey the various aspects of the theory that comes into play, giving a detailed bibliography to orient the reader. Nonetheless the main general theorems involving multiplicities functions (convexity, quasi-polynomial behavior, Jeffrey-Kirwan residues) are stated without proofs. Then, we present in detail our approach to the computational problem, giving explicit formulae, and outlining an algorithm that calculate many interesting examples, some of which appear in the literature also in connection with Hilbert series.
Investigating the Vulnerability of Programmable Data Planes to Static Analysis-Guided Attacks

Programmable network data planes are paving the way for networking innovations, with the ability to perform complex, stateful tasks defined in high-level languages such as P4. The enhanced capabilities of programmable data plane devices has made verification of their runtime behaviour, using established methods such as probe packets, impossible to scale beyond probabilistic detection. This has created a potential opportunity for an attacker, with access to a compromised device, to subtly alter its forwarding program to mishandle only a small subset of packets, evading probabilistic detection. In practice, such subtle binary instrumentation attacks require extensive knowledge of the forwarding program, yet it is unclear whether a static analysis of compiled P4 programs to obtain this knowledge can be fast and accurate enough for an on-device attack scenario. In this work, we investigate this possibility by implementing a static analysis of P4 programs compiled to BPF bytecode. This analysis gathers sufficient information for the attacker to identify appropriate (reliably correct) edits to the program. We found that, due to predictable compiler behaviours, our analysis remains accurate even when several program behaviours are abstracted away. Our evaluation of the analysis requirements shows that, from a defensive perspective, there is scope for selectively manipulating those instructions in P4-BPF programs that are critical to attack-focused analysis in order to increase its difficulty, without increasing the number of program instructions.
Adaptively-refined overlapping grids for the numerical solution of systems of hyperbolic conservation laws

Adaptive mesh refinement (AMR) in conjunction with higher-order upwind finite-difference methods have been used effectively on a variety of problems in two and three dimensions. In this paper we introduce an approach for resolving problems that involve complex geometries in which resolution of boundary geometry is important. The complex geometry is represented by using the method of overlapping grids, while local resolution is obtained by refining each component grid with the AMR algorithm, appropriately generalized for this situation. The CMPGRD algorithm introduced by Chesshire and Henshaw is used to automatically generate the overlapping grid structure for the underlying mesh.
Compositional Verification of Interacting Systems Using Event Monads

Large software systems are usually divided into multiple components that interact with each other. How to verify interacting components in a modular way is one of the major problems in formal verification. In many cases, interaction between components can be modeled asynchronously, where events are sent without requiring a response in order to continue with execution of the component. In this paper, we propose a lightweight, event-based framework for verification of components with asynchronous interaction. We define event monads and event systems, and a Hoare logic-style calculus for reasoning about them. The framework is implemented in Isabelle and applied to several case studies, including models for distributed computing, cache-coherence protocols, and verification of partition scheduling in a real-time operating system
Principal Types as Lambda Nets

We show that there are connections between principal type schemata , cut-free λ -nets , and normal forms of the λ -calculus, and hence there are correspondences between the normalisation algorithms of the above structures, i.e. unification of principal types, cut-elimination of λ -nets, and normalisation of λ -terms. Once the above correspondences have been established, properties of the typing system, such as typability , subject reduction , and inhabitation , can be derived from properties of λ -nets, and vice-versa. We illustrate the above pattern on a specific type assignment system, we study principal types for this system, and we show that they correspond to λ -nets with a non-standard notion of cut-elimination. Properties of the type system are then derived from results on λ -nets.
Hate and Offensive Content Identification from Dravidian Social Media Posts: A Deep Learning Approach

Identifying hate and offensive content in social media posts is one of the most challenging tasks for Natural Language Processing. The usage of non-standard acronyms, misspellings, poor grammar, and multilingualism in social media posts makes detecting hate and offensive language much more difficult. This work proposes a deep neural network-based model for the identification of offensive social media posts from Tamil script-mixed, Tamil code-mixed, and Malayalam code-mixed messages. The combination of one to six-gram character-level Term-Frequency Inverse Document Frequency (TF-IDF) features with a four-layered deep neural network model performed better than the other combinations of character-level n-gram TF-IDF features. For Tamil script-mixed, Tamil code-mixed, and Malayalam code-mixed social media postings, the suggested model attained weighted 𝐹 1 -scores of 0.84, 0.65, and 0.71, respectively. The code for the proposed models is available at https://github.com/Abhinavkmr/ Hate-Speech-Identification-Dravidian-Language.git
Taming an Authoritative Armv8 ISA Specification: L3 Validation and CakeML Compiler Verification

Machine-readable specifications for the Armv8 instruction set architecture have become publicly available as part of Arm’s release processes, providing an official and unambiguous source of truth for the semantics of Arm instructions. To date, compiler and machine code verification efforts have made use of unofficial theorem-proving-friendly specifications of Armv8, e.g. CakeML uses an L3-based specification. The validity of these verification efforts hinges upon their unofficial ISA specifications being valid with respect to the official Arm specification. Leveraging the Sail language ecosystem, we bridge this validation gap by formally verifying that an L3-based specification simulates the official Arm specification using the HOL4 interactive theorem prover. We exercise this simulation by proving a novel compiler correctness result for CakeML with respect to Arm’s official specification of the Armv8.6 A-class instruction set. verification; → proving Supplementary Material Specifications and proofs in the HOL4 interactive theorem prover.
Fine-tune BERT to Classify Hate Speech in Hindi English Code-Mixed Text

With the exponential growth in internet technologies and social media usage, communicating and gathering information across countries is increasing at faster pace. These platforms provide opportunities to share opinions and suggestions about any socio-political events. Apart from seeking advantage, a person or community often misuses these platforms to post hate speech content. Content targeted towards casteism, racism, sexism, and insulting is called hate speech. The majority of the user are multilingual speakers knowing two or more languages. Out of them, English is the most known language. Knowing native language like Hindi and well-known languages like English, most social media users switch between Hindi and English while writing content on the social media platform. The phenomenon of mixing two languages like Hindi and English is called as Hindi English Code-mixed language. We have participated in HASOC subtask2 to classify Hi-En code mixed conversation in two classes as Hate Offensive (HOF) and Non-hate (NONE). We have experimented with various methods as Multilingual Bert (MBert), finetuned Pretrained Bert-base-uncased, an ensemble of Bert-base-uncased, XLNet and transfer learning-based approaches. We have analyzed that finetuned Bert-base-uncased has outperformed all other models. In subtask 2 of HASOC 2021, overall, 16 teams have participated, our team named “Rider” has achieved position 4 in Macro F1 score and Macro precision .
Pitfalls and opportunities for applying PEER factors in single-cell eQTL analyses

Using latent variables in gene expression data can help correct spurious correlations due to unobserved confounders and increase statistical power for expression Quantitative Trait Loci (eQTL) detection. Probabilistic Estimation of Expression Residuals (PEER) is a widely used statistical method that has been developed to remove unwanted variation and improve eQTL discovery power in bulk RNA-seq analysis. However, its performance has not been largely evaluated in single-cell eQTL data analysis, where it is becoming a commonly used technique. Potential challenges arise due to the structure of single-cell data, including sparsity, skewness, and mean-variance relationship. Here, we show by a series of analyses that this method requires additional quality control and data transformation steps on the pseudo-bulk matrix to obtain valid PEER factors. By using a population-scale single-cell cohort (OneK1K, N = 982), we found that generating PEER factors without further QC or transformation on the pseudo-bulk matrix could result in inferred factors that are highly correlated (Pearson’s correlation r = 0.626∼0.997). Similar spurious correlations were also found in PEER factors inferred from an independent dataset (induced pluripotent stem cells, N = 31). Optimization of the strategy for generating PEER factors and incorporating the improved PEER factors in the eQTL association model can identify 9.0∼23.1% more eQTLs or 1.7%∼13.3% more eGenes. Sensitivity analysis showed that the pattern of change between the number of eGenes detected and PEER factors fitted varied significantly for different cell types. In addition, using highly variable genes (e.g., top 2000) to generate PEER factors could achieve similar eGenes discovery power as using all genes but save considerable computational resources (∼6.2-fold faster). We provide diagnostic guidelines to improve the robustness and avoid potential pitfalls when generating PEER factors for single-cell eQTL association analyses.
Distance estimation in the goldfish (Carassius auratus)

Neurophysiological advances have given us exciting insights into the systems responsible for spatial mapping in mammals. However, we are still lacking information on the evolution of these systems and whether the underlying mechanisms identified are universal across phyla, or specific to the species studied. Here we address these questions by exploring whether a species that is evolutionarily distant from mammals can perform a task central to mammalian spatial mapping – distance estimation. We developed a behavioural paradigm allowing us to test whether goldfish (Carassius auratus) can estimate distance and explored the behavioural mechanisms that underpin this ability. Fish were trained to swim a set distance within a narrow tank covered with striped pattern. After changing the background pattern, we found that goldfish use the spatial frequency of their visual environment to estimate distance; doubling the spatial frequency of optic flow pattern resulted in a large overestimation of the swimming distance. These results provide robust evidence that goldfish can accurately estimate distance, and show that they use optic flow to do so. These results provide a compelling basis to utilise goldfish as a model system to interrogate the evolution of the mechanisms that underpin spatial cognition, from brain to behaviour.
Functional morphological innovation corresponds to shifting lines of genetic least resistance

Phenotypic traits are often constrained in their evolution by the genetic and developmental map underlying them. However, the extent to which these constraints themselves evolve has not been well-characterized. Traits that are integrated are generally thought to be constrained in their ability to evolve because their shared developmental and genetic architecture causes any change in one to be mirrored in the other. Nevertheless, it is not yet clear whether correlations can constrain major episodes of phenotypic diversification over longer timescales. We reconstruct patterns in integration within nine primate lineages and model the evolution of the resulting modules in a phylogenetic context. We find that patterns in integration are generally evolvable across lineages, with apes displaying particular lability in the composition of morphological modules. This increased turnover in module composition corresponds to both divergent evolution of skeletal shape and the formation of novel complexes of locomotor behaviors and postures. While integration may play a role in constraining evolutionary innovation, its effects are dynamic, shifting as the structure of integration itself evolves during episodes of exceptional ecomorphological diversification.
An economic and robust TMT labeling approach for high throughput proteomic and metaproteomic analysis

Multiplexed quantitative proteomics using tandem mass tag (TMT) is increasingly used in –omic study of complex samples. While TMT-based proteomics has the advantages of the higher quantitative accuracy, fewer missing values, and reduced instrument analysis time, it is limited by the increased cost due to the use of labeling reagents. In addition, current TMT labeling workflows involve repeated small volume pipetting of reagents in volatile organic solvents, which may increase the sample-to-sample variations and is not readily suitable for high throughput applications. In this study, we demonstrated that the TMT labeling procedures could be streamlined by using pre-aliquoted dry TMT reagents in a 96 well plate or 12-tube strip. As little as 50 μg dry TMT per channel effectively labels 6-12 μg peptides, yielding efficient TMT labeling efficiency (∼99%) in both microbiome and mammalian cell line samples. This streamlined workflow decreases reagent loss and reduces inter-sample variations. We applied this workflow to analyze 97 samples in a study to evaluate whether ice recrystallization inhibitors improve the cultivability and activity of frozen microbiota. The results demonstrated tight sample clustering corresponding to groups and consistent microbiome responses to prebiotic treatments. This study supports the use of TMT reagents that are pre-aliquoted, dried, and stored for streamlined and robust quantitative proteomics and metaproteomics in high throughput applications.
Leveraging Micro-Services for Ultra-Low Latency: An optimization Model for Service Function Chains Placement

The evolution of the Internet tends toward ever requiring lower latency services. Cloud robotics or drone piloting are service use-cases in which the latency of traffic cannot exceed a few milliseconds. Reducing the latency can be achieved through several means, and micro-services deployed over virtual infrastructures appears as a promising way by enabling service chain reductions, micro-function mutualization and parallelism. However, the placement and routing of such components appears as an harder task to achieve as compared to monolithic approaches of the state of the art. Consequently, we propose in this paper a comprehensive optimization model in charge of placing micro-services in a virtualized network infrastructure, under ultra-low latency constraints while preserving resource consumption. By challenging our model with several realistic scenarios in terms of topology and service function chains (SFC), we demonstrate to what extent it improves the overall performance of SFC by especially minimizing the gap between the expected latency and the actual one, as compared to several competitors, thus making it a well-fitted approach for ultra-low latency services.