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In this paper, we studied the impact of the off-diagonal SNSI parameters in the future long-baseline neutrino oscillation experiments DUNE and P2SO. In our analysis, we found that the sensitivities of these experiments altered in a very non-trivial way due to the presence of these parameters. Depending on the values of these parameters, they can either completely mimic the standard scenario or can wash out their CP sensitivity. For large values of parameters $\eta_{e\mu}$ and $\eta_{e\tau}$, we obtained larger mass ordering and octant sensitivities as compared to the standard three flavour scenario. For the parameter $\eta_{\mu\tau}$, the mass ordering sensitivity and the precision of $\Delta m^2_{31}$ deteriorated compared to the standard scenario. Our results also showed that the sensitivities significantly influenced by the phases of the off-diagonal parameters., Comment: 17 pages, 9 figures, 3 tables

Quantum states exhibiting single-particle entanglement (SPE) can encode and process quantum information more robustly than their multi-particle analogs. Understanding the vulnerability and resilience of SPE to disorder is therefore crucial. This letter investigates phase, coin, and position disorder via discrete-time quantum walks on odd and even cyclic graphs to study their effect on SPE. The reduction in SPE is insignificant for low levels of phase or coin disorder, showing the resilience of SPE to minor perturbations. However, SPE is seen to be more vulnerable to position disorder. We analytically prove that maximally entangled single-particle states (MESPS) at time step $t=1$ are impervious to phase disorder regardless of the choice of the initial state. Further, MESPS at timestep $t=1$ is also wholly immune to coin disorder for phase-symmetric initial states. Position disorder breaks odd-even parity and distorts the physical time cone of the quantum walker, unlike phase or coin disorder. SPE saturates towards a fixed value for position disorder, irrespective of the disorder strength at large timestep $t$. Furthermore, SPE can be enhanced with moderate to significant phase or coin disorder strengths at specific time steps. Interestingly, disorder can revive single-particle entanglement from absolute zero in some instances, too. These results are crucial in understanding single-particle entanglement evolution and dynamics in a lab setting., Comment: 17 pages, 14 figures

Social media has a significant impact on people's lives. Hate speech on social media has emerged as one of society's most serious issues in recent years. Text and pictures are two forms of multimodal data that are distributed within articles. Unimodal analysis has been the primary emphasis of earlier approaches. Additionally, when doing multimodal analysis, researchers neglect to preserve the distinctive qualities associated with each modality. To address these shortcomings, the present article suggests a scalable architecture for multimodal hate content detection called transformer-based multilevel attention (STMA). This architecture consists of three main parts: a combined attention-based deep learning mechanism, a vision attention-mechanism encoder, and a caption attention-mechanism encoder. To identify hate content, each component uses various attention processes and handles multimodal data in a unique way. Several studies employing multiple assessment criteria on three hate speech datasets such as Hateful memes, MultiOff, and MMHS150K, validate the suggested architecture's efficacy. The outcomes demonstrate that on all three datasets, the suggested strategy performs better than the baseline approaches.

Social media, particularly Twitter, has seen a significant increase in incidents like trolling and hate speech. Thus, identifying hate speech is the need of the hour. This paper introduces a computational framework to curb the hate content on the web. Specifically, this study presents an exhaustive study of pre-processing approaches by studying the impact of changing the sequence of text pre-processing operations for the identification of hate content. The best-performing pre-processing sequence, when implemented with popular classification approaches like Support Vector Machine, Random Forest, Decision Tree, Logistic Regression and K-Neighbor provides a considerable boost in performance. Additionally, the best pre-processing sequence is used in conjunction with different ensemble methods, such as bagging, boosting and stacking to improve the performance further. Three publicly available benchmark datasets (WZ-LS, DT, and FOUNTA), were used to evaluate the proposed approach for hate speech identification. The proposed approach achieves a maximum accuracy of 95.14% highlighting the effectiveness of the unique pre-processing approach along with an ensemble classifier.

The trustworthiness of multimedia is being increasingly evaluated by advanced Image Manipulation Localization (IML) techniques, resulting in the emergence of the IML field. An effective manipulation model necessitates the extraction of non-semantic differential features between manipulated and legitimate sections to utilize artifacts. This requires direct comparisons between the two regions.. Current models employ either feature approaches based on handcrafted features, convolutional neural networks (CNNs), or a hybrid approach that combines both. Handcrafted feature approaches presuppose tampering in advance, hence restricting their effectiveness in handling various tampering procedures, but CNNs capture semantic information, which is insufficient for addressing manipulation artifacts. In order to address these constraints, we have developed a dual-branch model that integrates manually designed feature noise with conventional CNN features. This model employs a dual-branch strategy, where one branch integrates noise characteristics and the other branch integrates RGB features using the hierarchical ConvNext Module. In addition, the model utilizes edge supervision loss to acquire boundary manipulation information, resulting in accurate localization at the edges. Furthermore, this architecture utilizes a feature augmentation module to optimize and refine the presentation of attributes. The shallowfakes dataset (CASIA, COVERAGE, COLUMBIA, NIST16) and deepfake dataset Faceforensics++ (FF++) underwent thorough testing to demonstrate their outstanding ability to extract features and their superior performance compared to other baseline models. The AUC score achieved an astounding 99%. The model is superior in comparison and easily outperforms the existing state-of-the-art (SoTA) models.

Deepfakes, which employ GAN to produce highly realistic facial modification, are widely regarded as the prevailing method. Traditional CNN have been able to identify bogus media, but they struggle to perform well on different datasets and are vulnerable to adversarial attacks due to their lack of robustness. Vision transformers have demonstrated potential in the realm of image classification problems, but they require enough training data. Motivated by these limitations, this publication introduces Tex-ViT (Texture-Vision Transformer), which enhances CNN features by combining ResNet with a vision transformer. The model combines traditional ResNet features with a texture module that operates in parallel on sections of ResNet before each down-sampling operation. The texture module then serves as an input to the dual branch of the cross-attention vision transformer. It specifically focuses on improving the global texture module, which extracts feature map correlation. Empirical analysis reveals that fake images exhibit smooth textures that do not remain consistent over long distances in manipulations. Experiments were performed on different categories of FF++, such as DF, f2f, FS, and NT, together with other types of GAN datasets in cross-domain scenarios. Furthermore, experiments also conducted on FF++, DFDCPreview, and Celeb-DF dataset underwent several post-processing situations, such as blurring, compression, and noise. The model surpassed the most advanced models in terms of generalization, achieving a 98% accuracy in cross-domain scenarios. This demonstrates its ability to learn the shared distinguishing textural characteristics in the manipulated samples. These experiments provide evidence that the proposed model is capable of being applied to various situations and is resistant to many post-processing procedures.

The natural language processing and multimedia field has seen a notable surge in interest in multimodal sentiment recognition. Hence, this study aims to employ Target-Dependent Multimodal Sentiment Analysis (TDMSA) to identify the level of sentiment associated with every target (aspect) stated within a multimodal post consisting of a visual-caption pair. Despite the recent advancements in multimodal sentiment recognition, there has been a lack of explicit incorporation of emotional clues from the visual modality, specifically those pertaining to facial expressions. The challenge at hand is to proficiently obtain visual and emotional clues and subsequently synchronise them with the textual content. In light of this fact, this study presents a novel approach called the Visual-to-Emotional-Caption Translation Network (VECTN) technique. The primary objective of this strategy is to effectively acquire visual sentiment clues by analysing facial expressions. Additionally, it effectively aligns and blends the obtained emotional clues with the target attribute of the caption mode. The experimental findings demonstrate that our methodology is capable of producing ground-breaking outcomes when applied to two publicly accessible multimodal Twitter datasets, namely, Twitter-2015 and Twitter-2017. The experimental results show that the suggested model achieves an accuracy of 81.23% and a macro-F1 of 80.61% on the Twitter-15 dataset, while 77.42% and 75.19% on the Twitter-17 dataset, respectively. The observed improvement in performance reveals that our model is better than others when it comes to collecting target-level sentiment in multimodal data using the expressions of the face.

Various linguistic and non-linguistic clues, such as excessive emphasis on a word, a shift in the tone of voice, or an awkward expression, frequently convey sarcasm. The computer vision problem of sarcasm recognition in conversation aims to identify hidden sarcastic, criticizing, and metaphorical information embedded in everyday dialogue. Prior, sarcasm recognition has focused mainly on text. Still, it is critical to consider all textual information, audio stream, facial expression, and body position for reliable sarcasm identification. Hence, we propose a novel approach that combines a lightweight depth attention module with a self-regulated ConvNet to concentrate on the most crucial features of visual data and an attentional tokenizer based strategy to extract the most critical context-specific information from the textual data. The following is a list of the key contributions that our experimentation has made in response to performing the task of Multi-modal Sarcasm Recognition: an attentional tokenizer branch to get beneficial features from the glossary content provided by the subtitles; a visual branch for acquiring the most prominent features from the video frames; an utterance-level feature extraction from acoustic content and a multi-headed attention based feature fusion branch to blend features obtained from multiple modalities. Extensive testing on one of the benchmark video datasets, MUSTaRD, yielded an accuracy of 79.86% for speaker dependent and 76.94% for speaker independent configuration demonstrating that our approach is superior to the existing methods. We have also conducted a cross-dataset analysis to test the adaptability of VyAnG-Net with unseen samples of another dataset MUStARD++.

Sarcasm is a type of irony, characterized by an inherent mismatch between the literal interpretation and the intended connotation. Though sarcasm detection in text has been extensively studied, there are situations in which textual input alone might be insufficient to perceive sarcasm. The inclusion of additional contextual cues, such as images, is essential to recognize sarcasm in social media data effectively. This study presents a novel framework for multimodal sarcasm detection that can process input triplets. Two components of these triplets comprise the input text and its associated image, as provided in the datasets. Additionally, a supplementary modality is introduced in the form of descriptive image captions. The motivation behind incorporating this visual semantic representation is to more accurately capture the discrepancies between the textual and visual content, which are fundamental to the sarcasm detection task. The primary contributions of this study are: (1) a robust textual feature extraction branch that utilizes a cross-lingual language model; (2) a visual feature extraction branch that incorporates a self-regulated residual ConvNet integrated with a lightweight spatially aware attention module; (3) an additional modality in the form of image captions generated using an encoder-decoder architecture capable of reading text embedded in images; (4) distinct attention modules to effectively identify the incongruities between the text and two levels of image representations; (5) multi-level cross-domain semantic incongruity representation achieved through feature fusion. Compared with cutting-edge baselines, the proposed model achieves the best accuracy of 92.89% and 64.48%, respectively, on the Twitter multimodal sarcasm and MultiBully datasets.

The emoticons are symbolic representations that generally accompany the textual content to visually enhance or summarize the true intention of a written message. Although widely utilized in the realm of social media, the core semantics of these emoticons have not been extensively explored based on multiple modalities. Incorporating textual and visual information within a single message develops an advanced way of conveying information. Hence, this research aims to analyze the relationship among sentences, visuals, and emoticons. For an orderly exposition, this paper initially provides a detailed examination of the various techniques for extracting multimodal features, emphasizing the pros and cons of each method. Through conducting a comprehensive examination of several multimodal algorithms, with specific emphasis on the fusion approaches, we have proposed a novel contrastive learning based multimodal architecture. The proposed model employs the joint training of dual-branch encoder along with the contrastive learning to accurately map text and images into a common latent space. Our key finding is that by integrating the principle of contrastive learning with that of the other two branches yields superior results. The experimental results demonstrate that our suggested methodology surpasses existing multimodal approaches in terms of accuracy and robustness. The proposed model attained an accuracy of 91% and an MCC-score of 90% while assessing emoticons using the Multimodal-Twitter Emoticon dataset acquired from Twitter. We provide evidence that deep features acquired by contrastive learning are more efficient, suggesting that the proposed fusion technique also possesses strong generalisation capabilities for recognising emoticons across several modes.

We propose a quantum algorithm to tackle the quadratic nonlinearity in the Lattice Boltzmann (LB) collision operator. The key idea is to build the quantum gates based on the particle distribution functions (PDF) within the coherence time for qubits. Thus, both the operator and a state vector are linear functions of PDFs, and upon quantum state evolution, the resulting PDFs will have quadraticity. To this end, we decompose the collision operator for a $DmQn$ lattice model into a product of $2(n+1)$ operators, where $n$ is the number of lattice velocity directions. After decomposition, the $(n+1)$ operators with constant entries remain unchanged throughout the simulation, whereas the remaining $(n+1)$ will be built based on the statevector of the previous time step. Also, we show that such a decomposition is not unique. Compared to the second-order Carleman-linearized LB, the present approach reduces the circuit width by half and circuit depth by exponential order. The proposed algorithm has been verified through the one-dimensional flow discontinuity and two-dimensional Kolmogrov-like flow test cases., Comment: 5 pages, 3 figures

A single-photon entangled state (or single-particle entangled state (SPES) in general) can offer a more secure way of encoding and processing quantum information than their multi-photon (or multi-particle) counterparts. The SPES generated via a 2D alternate quantum-walk setup from initially separable states can be either 3-way or 2-way entangled. This letter shows that the generated genuine three-way and nonlocal two-way SPES can be used as cryptographic keys to securely encode two distinct messages simultaneously. We detail the message encryption-decryption steps and show the resilience of the 3-way and 2-way SPES-based cryptographic protocols against eavesdropper attacks like intercept-and-resend and man-in-the-middle. We also detail the experimental realization of these protocols using a single photon, with the three degrees of freedom being OAM, path, and polarization. We have proved that the protocols have unconditional security for quantum communication tasks. The ability to simultaneously encode two distinct messages using the generated SPES showcases the versatility and efficiency of the proposed cryptographic protocol. This capability could significantly improve the throughput of quantum communication systems., Comment: 13 pages (including appendix), two figures and one table

We report exchange bias (EB) in single-phase CoO nanoparticles, where two magnetic phases naturally emerge as the crystallite size decreases from 34.6 to 10.8 nm. The N\'eel temperature (TN) associated with antiferromagnetic ordering decreases monotonically with the reduction in crystallite size, highlighting the significant influence of size effects. The 34.6 nm nanoparticles exhibit magnetization irreversibility between zero field cooled (ZFC) and field-cooled (FC) states below TN. This irreversibility appears well above TN with further reduction in size, resulting in the absence of true paramagnetic regime which indicates the occurrence of an additional magnetic phase. The frequency-dependent ac-susceptibility in 10.8 nm nanoparticles suggests slow dynamics of disordered surface spins above TN, coinciding with the establishment of long-range order in the core. The thermoremanent magnetization (TRM) and isothermoremanent magnetization (IRM) curves suggest a core-shell structure: the core is antiferromagnetic, and the shell consists of disordered surface spins causing ferromagnetic interaction. Hence, the exchange bias in these CoO nanoparticles results from the exchange coupling between an antiferromagnetic core and a disordered shell that exhibits unconventional surface spin characteristics., Comment: 33 Pages, 9 figures

In this work we investigate the effect of curved spacetime on neutrino oscillation. In a curved spacetime, the effect of curvature on fermionic fields is represented by spin connection. The spin connection consists of a non-universal ``contorsion" part which is expressed in terms of vector and axial current density of fermions. The contraction of contorsion part with the tetrad fields, which connects the internal flat space metric and the spacetime metric, is called torsion. In a scenario where neutrino travels through background of fermionic matter at ordinary densities in a curved spacetime, the Hamiltonian of neutrino oscillation gets modified by the torsional coupling constants $\lambda_{21}^{\prime}$ and $\lambda_{31}^{\prime}$. The aim of this work is to study the effect of $\lambda_{21}^{\prime}$ and $\lambda_{31}^{\prime}$ in DUNE and P2SO. In our study we, (i) discuss the effect of torsional coupling constants on the neutrino oscillation probabilities, (ii) estimate the capability of P2SO and DUNE to put bounds on these parameters and (iii) study how the physics sensitivities get modified in presence of torsion., Comment: 21 pages, 7 figures, 3 tables

Entanglement with single-particle states is advantageous in quantum technology because of their ability to encode and process information more securely than their multi-particle analogs. Three-way and nonlocal two-way entangled single-particle states are desirable in this context. Herein, we generate three-way entanglement from an initially separable state involving three degrees of freedom of a quantum particle, which evolves via a 2D alternate quantum walk employing a resource-saving single-qubit coin. We achieve maximum possible values for the three-way entanglement quantified by the $\pi$-tangle between the three degrees of freedom. We also generate optimal two-way nonlocal entanglement, quantified by the negativity between the nonlocal position degrees of freedom of the particle. This prepared architecture using quantum walks can be experimentally realized with a photon., Comment: 13 pages, 10 figures

Given items of different sizes and a fixed bin capacity, the bin-packing problem is to pack these items into a minimum number of bins such that the sum of item sizes in a bin does not exceed the capacity. We define a new variant called \emph{$k$-times bin-packing ($k$BP)}, where the goal is to pack the items such that each item appears exactly $k$ times, in $k$ different bins. We generalize some existing approximation algorithms for bin-packing to solve $k$BP, and analyze their performance ratio. The study of $k$BP is motivated by the problem of \emph{fair electricity distribution}. In many developing countries, the total electricity demand is higher than the supply capacity. We prove that every electricity division problem can be solved by $k$-times bin-packing for some finite $k$. We also show that $k$-times bin-packing can be used to distribute the electricity in a fair and efficient way. Particularly, we implement generalizations of the First-Fit and First-Fit Decreasing bin-packing algorithms to solve $k$BP, and apply the generalizations to real electricity demand data. We show that our generalizations outperform existing heuristic solutions to the same problem., Comment: 38 pages, 7 figures, 3 tables. This article is a full version of our accepted paper at "17th Symposium on Algorithmic Game Theory (SAGT 2024)"

In the contemporary world with degrading natural resources, the urgency of energy efficiency has become imperative due to the conservation and environmental safeguarding. Therefore, it's crucial to look for advanced technology to minimize energy consumption. This research focuses on the optimization of battery-electric city style vehicles through the use of a real-time in-car telemetry system that communicates between components through the robust Controller Area Network (CAN) protocol. By harnessing real-time data from various sensors embedded within vehicles, our driving assistance system provides the driver with visual and haptic actionable feedback that guides the driver on using the optimum driving style to minimize power consumed by the vehicle. To develop the pace feedback mechanism for the driver, real-time data is collected through a Shell Eco Marathon Urban Concept vehicle platform and after pre-processing, it is analyzed using the novel machine learning algorithm TEMSL, that outperforms the existing baseline approaches across various performance metrics. This innovative method after numerous experimentation has proven effective in enhancing energy efficiency, guiding the driver along the track, and reducing human errors. The driving-assistance system offers a range of utilities, from cost savings and extended vehicle lifespan to significant contributions to environmental conservation and sustainable driving practices.

Wind energy has emerged as a viable alternative to fossil fuels, with vertical axis wind turbines (VAWTs) gaining popularity due to their efficiency and adaptability. Combining the Actuator Line Method (ALM) with Large Eddy Simulation (LES) enables accurate performance evaluations, facilitating the design and optimization of wind turbines. The present study invokes ALM-based methodology to perform calculations for the VAWTs. The results of the LES simulations of the VAWTs have been extensively validated against the available experimental and numerical data. The study further explores a VAWT cluster of three turbines by investigating the influence of turbine spacing (in both in-line and staggered configuration) on cluster performance. The study shows that the configuration with a streamwise separation (Xsep) of 0.34D and a transverse separation (Ysep) of 2.5D exhibits superior performance to other combinations owing to increased kinetic energy in the wake for the downstream turbines. Further, we have presented the effect of varying the rotation direction (in combinations of Clockwise and Counter-Clockwise rotation) for the individual turbines in the 3-turbine cluster for the two configurations: in-line (Xsep = 0D, Ysep = 2.5D) and staggered (Xsep = 0.34D, Ysep = 2.5D). Staggered counter-rotating turbine cases show reduced performance compared to co-rotating cases, specifically, the clockwise co-rotating (C-C-C) configuration. In the in-line configuration, counter-rotating setups outperform co-rotating ones. Counter-rotation analysis reveals that reducing streamwise separation allows turbines to align in line without sacrificing performance, thereby increasing the power density of the turbine cluster

Data security and sharing remains nuisance among many applications like business data, medical data, banking data etc. In this research, block chain technology is built with encryption algorithm for high level data security in cloud storage. Medical data security seems critical aspect due to sensitivity of patient information. Unauthorized access of medical data creates major issue to patients. This article proposed block chain with hybrid encryption technique for securing medical data stored in block chain model at cloud storage. New Two fish encryption model is implemented based on RSA Multiple Precision Arithmetic. MPA works by using library concept. The objective of using this methodology is to enhance security performance with less execution time. Patient data is processed by encryption algorithm and stored at blockchain infrastructure using encrypted key. Access permission allows user to read or write the medical data attached in block chain framework. The performance of traditional cryptographic techniques is very less in providing security infrastructure., Comment: https://anapub.co.ke/journals/jmc/jmc_abstract/2023/jmc_volume_03_issue_03/jmc_volume3_issue3_4.html

Large Language Models (LLMs) have revolutionized programming and software engineering. AI programming assistants such as GitHub Copilot X enable conversational programming, narrowing the gap between human intent and code generation. However, prior literature has identified a key challenge--there is a gap between user's mental model of the system's understanding after a sequence of natural language utterances, and the AI system's actual understanding. To address this, we introduce Programming with Representations (PwR), an approach that uses representations to convey the system's understanding back to the user in natural language. We conducted an in-lab task-centered study with 14 users of varying programming proficiency and found that representations significantly improve understandability, and instilled a sense of agency among our participants. Expert programmers use them for verification, while intermediate programmers benefit from confirmation. Natural language-based development with LLMs, coupled with representations, promises to transform software development, making it more accessible and efficient., Comment: 23 pages, 3 figures, 2 tables, under submission for ACM CHI 2024

In this paper we have studied the phenomenon of non-standard interaction mediated by a scalar field (SNSI) in the context of P2SO experiment and compared its sensitivity with DUNE. In particular, we have studied the capability of these two experiments to put bounds on the diagonal SNSI parameters i.e., $\eta_{ee}$, $\eta_{\mu\mu}$ and $\eta_{\tau\tau}$ and studied the impact of these parameters on the determination of neutrino mass ordering, octant of $\theta_{23}$ and CP violation (CPV). In our analysis we find that, the parameter $\Delta m^2_{31}$ has a non-trivial role if one wants estimate the bounds on $\eta_{\mu\mu}$ and $\eta_{\tau\tau}$ assuming SNSI does not exist in nature. Our results show that sensitivity of P2SO and DUNE to constraint $\eta_{\mu\mu}$ and $\eta_{\tau\tau}$ are similar whereas the sensitivity of DUNE is slightly better for $\eta_{ee}$. We find that the mass ordering and CPV sensitivities are mostly affected by $\eta_{ee}$ compared to $\eta_{\mu \mu}$ and $\eta_{\tau \tau}$ if one assumes SNSI exists in nature. On the other hand, octant sensitivity is mostly affected by $\eta_{\mu \mu}$ and $\eta_{\tau \tau}$. These sensitivities can be either higher or lower than the standard three flavour scenario depending on the relative sign of the SNSI parameters. Regarding the precision of atmospheric mixing parameters, we find that the precision of $\theta_{23}$ deteriorates significantly in the presence of $\eta_{\mu\mu}$ and $\eta_{\tau\tau}$., Comment: 24 pages, 15 figures, 4 tables, Includes analytical expression for appearance channel probability

We investigate neutrino magnetic moment, triplet scalar dark matter in a Type-II radiative seesaw scenario. With three vector-like fermion doublets and two scalar triplets, we provide a loop level setup for the electromagnetic vertex of neutrinos. All the scalar multiplet components constitute the total dark matter abundance of the Universe and also their scattering cross section with detector lie below the experimental upper limit. Using the consistent parameter space in dark matter domain, we obtain light neutrino mass in sub-eV scale and also magnetic moment in the desired range. We further derive the constraints on neutrino transition magnetic moments, consistent with XENONnT limit., Comment: 23 pages, 11 figures, version to appear in PRD

We narrate dark matter, neutrino magnetic moment and mass in a Type-III radiative scenario. The Standard Model is enriched with three vector-like fermion triplets and two inert doublets to provide a suitable platform for the above phenomenological aspects. The inert scalars contribute to total relic density of dark matter in the Universe. Neutrino aspects are realized at one-loop with magnetic moment obtained through charged scalars, while neutrino mass gets contribution from charged and neutral scalars. Taking inert scalars up to $2$ TeV and triplet fermion in few hundred TeV range, we obtain a common parameter space, compatible with experimental limits associated with both neutrino and dark matter sectors. Using a specific region for transition magnetic moment (${\cal O} (10^{-11}\mu_B$)), we explain the excess recoil events, reported by the XENON1T collaboration. Finally, we demonstrate that the model is able to provide neutrino magnetic moments in a wide range from $10^{-12}\mu_B$ to $10^{-10}\mu_B$, meeting the bounds of various experiments such as Super-K, TEXONO, Borexino and XENONnT., Comment: 21 pages, 9 figures, version to appear in PRD

According to UNESCO, there are nearly 7,000 languages spoken worldwide, of which around 3,000 languages are in danger of disappearing before the end of the century. With roughly 230 languages having already become extinct between the years 1950-2010, collectively this represents a significant loss of linguistic and cultural diversity. This position paper aims to explore the potential of AI-based language learning approaches that promote early exposure and appreciation of languages to ultimately contribute to the preservation of endangered languages, thereby addressing the urgent need to protect linguistic and cultural diversity., Comment: 3 pages

Rational control over the periodic arrangement of particles by means of external stimuli is a technologically important aspect of colloidal science with important physical underpinnings. Here, a robust structural control of particle assemblies in a nematic liquid crystal (NLC) is demonstrated by dissolving trace amounts of light-responsive azo-dendrimer molecules which spontaneously get adsorbed on the particle surface. The azo-dendrimer molecules in the presence of external UV irradiation undergo conformational change (trans-cis); as a result, they transmit the mechanical torque to surrounding LC molecules and alter the near-field director orientation. The director re-orientation at the surface of the particles causes topological defect transformation which involves elastic dipoles, quadrupoles and hexadecapoles. The defect transformation can be emulated in colloidal assemblies towards different purposes such as rotation of chains and restructuring of 2D colloidal crystals. In this study, various topological aspects of light-activated defect transformation and its application in the collective manipulation of colloidal assemblies are presented., Comment: 12 pages, 6 figures

Named Entity Recognition (NER) is the task of identifying and classifying named entities in large-scale texts into predefined classes. NER in French and other relatively limited-resource languages cannot always benefit from approaches proposed for languages like English due to a dearth of large, robust datasets. In this paper, we present our work that aims to mitigate the effects of this dearth of large, labeled datasets. We propose a Transformer-based NER approach for French, using adversarial adaptation to similar domain or general corpora to improve feature extraction and enable better generalization. Our approach allows learning better features using large-scale unlabeled corpora from the same domain or mixed domains to introduce more variations during training and reduce overfitting. Experimental results on three labeled datasets show that our adaptation framework outperforms the corresponding non-adaptive models for various combinations of Transformer models, source datasets, and target corpora. We also show that adversarial adaptation to large-scale unlabeled corpora can help mitigate the performance dip incurred on using Transformer models pre-trained on smaller corpora., Comment: Preprint version of short paper accepted for the ECIR 2023 conference

Maximally entangled single-particle states (MESPS) are opening new possibilities in quantum technology as they have the potential to encode more information and are robust to decoherence compared to their nonlocal two-particle counterparts. We find that a single coin can generate MESPS at recurrent time steps (periodically) via discrete-time quantum walks on both $4$ and $8$ site cyclic graphs. This scheme is resource-saving with possibly the most straightforward experimental realization since the same coin is applied at each time step. We also show that recurrent MESPS can be generated on any arbitrary $k$ site cyclic graph, $k\in\{3,4,5,8\}$ via effective-single (Identity and arbitrary coin) or two coin evolution sequences. Beyond their use in fundamental research, we propose an application of the generated MESPS in quantum cryptography protocols. MESPS as cryptographic keys can strengthen quantum-secure communication., Comment: 18 pages, 9 figures, 2 tables, accepted for publication in Phys. Rev. A (Letters)

As the two phenomena, non-standard interaction (NSI) in neutrino propagation and Lorentz invariance violation (LIV) modify the Hamiltonian of neutrino oscillation in a similar fashion, it is very difficult to distinguish these two effects. The only difference between them lies in the fact that NSI depends on the matter density, whereas LIV is independent of the earth matter effect. Therefore for a fixed baseline experiment, where matter density is constant, the theories describing NSI and LIV are exactly equivalent. However, as the present and future bounds of the NSI and LIV parameters are not equivalent, one can distinguish these two scenarios in the long-baseline neutrino experiments depending on their statistics with respect to the present and future bounds of these parameters. In this paper, we attempt to differentiate between LIV and NSI in the context of DUNE and P2SO, as these two future experiments are believed to be sensitive to the strongest matter effect and will have very large statistics. Taking LIV in the data and NSI in theory, our results show that, indeed it is possible to have good discrimination between LIV and NSI. The best separation between LIV and NSI at $3 \sigma$ C.L. is achieved for the parameter $a_{\mu\mu}$ with P2SO. In this case, the value of LIV parameter for which separation is possible, lies within its future bound, if one considers the value of NSI parameter to be constrained by the present experiments. Between DUNE and P2SO, the latter has better sensitivity for such discrimination., Comment: 11 pages, 5 figures, 5 tables, version accepted for publication in Phys. Rev. D

Named Entity Recognition (NER) involves the identification and classification of named entities in unstructured text into predefined classes. NER in languages with limited resources, like French, is still an open problem due to the lack of large, robust, labelled datasets. In this paper, we propose a transformer-based NER approach for French using adversarial adaptation to similar domain or general corpora for improved feature extraction and better generalization. We evaluate our approach on three labelled datasets and show that our adaptation framework outperforms the corresponding non-adaptive models for various combinations of transformer models, source datasets and target corpora., Comment: Author version of Student Abstract to appear in AAAI 2023 - Student Abstract and Poster Program

Social networks have enabled user-specific advertisements and recommendations on their platforms, which puts a significant focus on Influence Maximisation (IM) for target advertising and related tasks. The aim is to identify nodes in the network which can maximize the spread of information through a diffusion cascade. We propose a community structures-based approach that employs K-Shell algorithm with community structures to generate a score for the connections between seed nodes and communities. Further, our approach employs entropy within communities to ensure the proper spread of information within the communities. We validate our approach on four publicly available networks and show its superiority to four state-of-the-art approaches while still being relatively efficient., Comment: Accepted in the Student Abstract & Poster Presentation Track at AAAI 2023

Recent works on fake news detection have shown the efficacy of using emotions as a feature for improved performance. However, the cross-domain impact of emotion-guided features for fake news detection still remains an open problem. In this work, we propose an emotion-guided, domain-adaptive, multi-task approach for cross-domain fake news detection, proving the efficacy of emotion-guided models in cross-domain settings for various datasets., Comment: Accepted in the Student Abstract & Poster Presentation track at AAAI 2023. arXiv admin note: substantial text overlap with arXiv:2211.13718

Recent works on fake news detection have shown the efficacy of using emotions as a feature or emotions-based features for improved performance. However, the impact of these emotion-guided features for fake news detection in cross-domain settings, where we face the problem of domain shift, is still largely unexplored. In this work, we evaluate the impact of emotion-guided features for cross-domain fake news detection, and further propose an emotion-guided, domain-adaptive approach using adversarial learning. We prove the efficacy of emotion-guided models in cross-domain settings for various combinations of source and target datasets from FakeNewsAMT, Celeb, Politifact and Gossipcop datasets., Comment: Accepted as a Short Paper in the 19th International Conference on Natural Language Processing (ICON) 2022

Social networking sites, blogs, and online articles are instant sources of news for internet users globally. However, in the absence of strict regulations mandating the genuineness of every text on social media, it is probable that some of these texts are fake news or rumours. Their deceptive nature and ability to propagate instantly can have an adverse effect on society. This necessitates the need for more effective detection of fake news and rumours on the web. In this work, we annotate four fake news detection and rumour detection datasets with their emotion class labels using transfer learning. We show the correlation between the legitimacy of a text with its intrinsic emotion for fake news and rumour detection, and prove that even within the same emotion class, fake and real news are often represented differently, which can be used for improved feature extraction. Based on this, we propose a multi-task framework for fake news and rumour detection, predicting both the emotion and legitimacy of the text. We train a variety of deep learning models in single-task and multi-task settings for a more comprehensive comparison. We further analyze the performance of our multi-task approach for fake news detection in cross-domain settings to verify its efficacy for better generalization across datasets, and to verify that emotions act as a domain-independent feature. Experimental results verify that our multi-task models consistently outperform their single-task counterparts in terms of accuracy, precision, recall, and F1 score, both for in-domain and cross-domain settings. We also qualitatively analyze the difference in performance in single-task and multi-task learning models., Comment: Accepted in IEEE Transaction on Computational Social Systems 18 pages 5 figures

Influence Maximization (IM) is the task of determining k optimal influential nodes in a social network to maximize the influence spread using a propagation model. IM is a prominent problem for viral marketing, and helps significantly in social media advertising. However, developing effective algorithms with minimal time complexity for real-world social networks still remains a challenge. While traditional heuristic approaches have been applied for IM, they often result in minimal performance gains over the computationally expensive Greedy-based and Reverse Influence Sampling-based approaches. In this paper, we propose the discretization of the nature-inspired Harris Hawks Optimisation meta-heuristic algorithm using community structures for optimal selection of seed nodes for influence spread. In addition to Harris Hawks intelligence, we employ a neighbour scout strategy algorithm to avoid blindness and enhance the searching ability of the hawks. Further, we use a candidate nodes-based random population initialization approach, and these candidate nodes aid in accelerating the convergence process for the entire populace. We evaluate the efficacy of our proposed DHHO approach on six social networks using the Independent Cascade model for information diffusion. We observe that DHHO is comparable or better than competing meta-heuristic approaches for Influence Maximization across five metrics, and performs noticeably better than competing heuristic approaches., Comment: 24 pages, 7 figures

In recent years, social networking platforms have gained significant popularity among the masses like connecting with people and propagating ones thoughts and opinions. This has opened the door to user-specific advertisements and recommendations on these platforms, bringing along a significant focus on Influence Maximisation (IM) on social networks due to its wide applicability in target advertising, viral marketing, and personalized recommendations. The aim of IM is to identify certain nodes in the network which can help maximize the spread of certain information through a diffusion cascade. While several works have been proposed for IM, most were inefficient in exploiting community structures to their full extent. In this work, we propose a community structures-based approach, which employs a K-Shell algorithm in order to generate a score for the connections between seed nodes and communities for low-budget scenarios. Further, our approach employs entropy within communities to ensure the proper spread of information within the communities. We choose the Independent Cascade (IC) model to simulate information spread and evaluate it on four evaluation metrics. We validate our proposed approach on eight publicly available networks and find that it significantly outperforms the baseline approaches on these metrics, while still being relatively efficient., Comment: 21 pages, 7 figures

In this paper, we study the capability of different long-baseline experiment options at the KM3NeT facility i.e., P2O, Upgraded P2O and P2SO to probe the light sterile neutrino and compare their sensitivities with DUNE. The P2O option will have neutrinos from a 90 KW beam at Protvino to be detected at the ORCA detector, the Upgraded P2O will have neutrinos from the upgraded 450 KW beam to be detected at the ORCA detector and the option P2SO will have neutrinos from a 450 KW beam to be detected at the upgraded Super-ORCA detector. All these options will have a baseline around 2595 km. Our results show that the experiments at the KM3NeT (DUNE) would be more sensitive if the value of $\Delta m^2_{41}$ is around 10 (1) eV$^2$. Our results also show that the role of near detector is very important for the study of sterile neutrinos and addition of near detector improves the sensitivity as compared to only far detector for 3+1 scenario. Among the three options at KM3NeT, the sensitivity of P2O and upgraded P2O is limited and sensitivity of P2SO is either comparable or better than DUNE., Comment: 21 pages, 7 figures, 2 tables, version accepted for publication in Phys. Rev. D

In organic semiconductor based bulk heterojunction solar cells, the presence of acceptor increases the formation of charge transfer (CT) excitons, thereby leading to higher exciton dissociation probabilities. In this work we used steady state EA measurements to probe the change in the nature of excitons as the blend composition of the solar cell active layer material is varied. We investigated blends of poly[(2,5-bis(2-hexyldecyloxy)phenylene)-alt-(5,6-difluoro-4,7-di(thiophen-2-yl)benzo[c]-[1,2,5]thiadiazole)] (PPDT2FBT) and (6,6)-Phenyl C71 butyric acid methyl ester (PCBM). Analysis of the EA spectra showed that in presence of fullerene based acceptor, like PCBM, CT characteristics of the excitons were modified, though, no new CT signature was observed in the blend. Enhancement in the CT characteristic in the blend was reflected in the photoluminescence (PL) measurements of the blends, where, PL quenching of $\sim$ 63\% was observed for 1\% PCBM. The quenching reaches saturation at about 20\% PCBM. However, the maximum efficiency of the devices was obtained for the blend having 50\% PCBM. Comparing experimental results with simulations, the variation of the device efficiency with PCBM percentage was shown to be arising from multiple factors like increase in polarizability and dipole moment of excitons, and the efficiency of the carrier collection from the bulk of the active layer.

In many developing countries, the total electricity demand is larger than the limited generation capacity of power stations. Many countries adopt the common practice of routine load shedding - disconnecting entire regions from the power supply - to maintain a balance between demand and supply. Load shedding results in inflicting hardship and discomfort on households, which is even worse and hence unfair to those whose need for electricity is higher than that of others during load shedding hours. Recently, Oluwasuji et al. [2020] presented this problem and suggested several heuristic solutions. In this work, we study the electricity distribution problem as a problem of fair division, model it using the related literature on cake-cutting problems, and discuss some insights on which parts of the time intervals are allocated to each household. We consider four cases: identical demand, uniform utilities; identical demand, additive utilities; different demand, uniform utilities; different demand, additive utilities. We provide the solution for the first two cases and discuss the novel concept of q-times bin packing in relation to the remaining cases. We also show how the fourth case is related to the consensus k-division problem. One can study objectives and constraints using utilitarian and egalitarian social welfare metrics, as well as trying to keep the number of cuts as small as possible. A secondary objective can be to minimize the maximum utility-difference between agents., Comment: 16 pages, 1 figure

Local structural symmetries play a key role in the functionalities of ferroelectric materials and are often found different from average symmetry. Here, we study the real space nanoscale structure in Pb-free BaZr$_{x}$Ti$_{1-x}$O$_{3}$ (x $\leq$ 0.10) by pair distribution function measurements, complemented by transmission electron microscopy and x-ray diffraction. Our observations show existence of the rhombohedrally distorted unit cells; however, at intermediate length scales, at least up to 5 nm, there exist nano-scale correlated regions of monoclinic symmetry. This is complemented by the observation of curved frustrated nanodomains. Further, the average structure is found to have coexisting monoclinic and rhombohedral symmetries. Our observation of a two-phase ferroelectric state is in contrast to interferroelectric instabilities of conventional polymorphic phase boundaries reported for doped BaTiO$_{3}$., Comment: 7 pages, 6 figures

Environmentally benign (1-x)Ba(Ti$_{0.8}$Zr$_{0.2}$)O$_3$-x(Ba$_{0.7}$Ca$_{0.3}$)TiO$_3$ (BZT-BCT) ceramics are promising materials due to their remarkable high piezoresponse [Liu and Ren, Phys. Rev. Lett. \textbf{103}, 257602 (2009)]. In this Letter, by focusing on local and average structure in combination with macroscopic electromechanical and dielectric measurements we demonstrate the structure property relationship in the tetragonal BZT-BCT ceramic. During high-temperature cubic to tetragonal phase transformation, polar nanoregions are manifested through the spontaneous volume ferroelectrostriction at temperatures below $\sim$ 477 K. Temperature-dependent local structural investigations across the Zr K edge extended x-ray absorption fine structure spectroscopy reveal an anomalous collaboration between the ZrO$_{6}$ and TiO$_6$ octahedra. These octahedra compromise their individuality during polarization development. The presence of domains of submicron size embedded inside the macroscopic ferroelectric regions below T$_{m}$, as well as their hierarchical arrangement, is observed by piezo-response force microscopy. Effects of the existence of the structural/polar heterogeneities below T$_{m}$ are observed also when polarizibilities of the poled and the unpoled samples are compared; the poled sample is found to be more susceptible to the electric field. In addition, by using electric field dependent x-ray diffraction studies we also show that this ceramic under field exhibits reduction of tetragonal distortion, which is consistent with earlier reports., Comment: 9 pages, 6 figures

In the current epoch of neutrino physics, many experiments are aiming for precision measurements of oscillation parameters. Thus, various new physics scenarios which alter the neutrino oscillation probabilities in matter deserve careful investigation. In this context, we study the effect of a vector leptoquark which induces non-standard neutrino interactions (NSI) that modify the oscillation probabilities of neutrinos in matter. We show that such interactions provide a relatively large value of NSI parameter $\varepsilon_{e \mu}$. Considering this NSI parameter, we successfully explain the recent discrepancy between the observed $\delta_{CP}$ results of T2K and NOvA., Comment: 18 pages, 8 figures, 1 table

Nontrivial shape of colloidal particles create complex elastic distortions and topological defects in liquid crystals and play a key role in governing their electrophoretic propulsion through the medium. Here, we report experimental results on defects and electrophoretic transport of anisometric (snowman-shaped) dielectric particles subjected to an alternating electric field perpendicular to the director in a nematic liquid crystal. We demonstrate that the shape asymmetry gives rise to defect-polymorphism by nucleating point or ring defects at multiple locations on the particle and controls the direction as well as the magnitude of the electrophoretic propulsion. Our findings unveil a novel degree of freedom in translocating microparticles in liquid crystals for applications in microfluidics, controlled transport and assembly., Comment: 12 pages, 4 figures

Unscrupulous content creators on YouTube employ deceptive techniques such as spam and clickbait to reach a broad audience and trick users into clicking on their videos to increase their advertisement revenue. Clickbait detection on YouTube requires an in depth examination and analysis of the intricate relationship between the video content and video descriptors title and thumbnail. However, the current solutions are mostly centred around the study of video descriptors and other metadata such as likes, tags, comments, etc and fail to utilize the video content, both video and audio. Therefore, we introduce a novel model to detect clickbaits on YouTube that consider the relationship between video content and title or thumbnail. The proposed model consists of a stacking classifier framework composed of six base models (K Nearest Neighbours, Support Vector Machine, XGBoost, Naive Bayes, Logistic Regression, and Multilayer Perceptron) and a meta classifier. The developed clickbait detection model achieved a high accuracy of 92.89% for the novel BollyBAIT dataset and 95.38% for Misleading Video Dataset. Additionally, the stated classifier does not use meta features or other statistics dependent on user interaction with the video (the number of likes, followers, or comments) for classification, and thus, can be used to detect potential clickbait videos before they are uploaded, thereby preventing the nuisance of clickbaits altogether and improving the users streaming experience., Comment: 26 pages, 16 figures

In this paper, we study the hierarchy sensitivity of Protvino to ORCA (P2O) experiment in three flavour scenario as well as its sensitivity to non-standard interactions (NSI) in neutrino propagation. Because of the largest possible baseline length of 2595 km, P2O is expected to have strong sensitivity towards neutrino mass hierarchy and NSI parameters. In our study, we show that even though the number of appearance channel events for the minimal configuration of P2O are higher compared to DUNE, still the hierarchy sensitivity of P2O is less than DUNE because of large background events. Our results show that for a background reduction factor of 0.46 and appearance channel background systematic normalization error of $4\%$, the hierarchy sensitivity of P2O becomes equivalent of DUNE for $\delta_{\rm CP} = 195^\circ$. We call this configuration of P2O as optimized P2O. Regarding the study of NSI, we find that, for $\epsilon_{e\mu}$ ($\epsilon_{e\tau}$) sensitivity of DUNE is similar (better) as compared to optimized P2O when both $\epsilon_{e\mu}$ and $\epsilon_{e\tau}$ are included in the analysis. Our results show that in presence of NSI, the change of hierarchy sensitivity with respect to standard three flavor scenario, is higher in P2O as compared to DUNE. Further, hierarchy sensitivity in presence of NSI is lower (higher) than sensitivity in the standard three flavour scenario for $\delta_{\rm CP} = 270^\circ (90^\circ)$. It is important to note that hierarchy sensitivity of optimized P2O does not get significantly better than DUNE for the current favourable values of $\delta_{\rm CP}$ which is $180^\circ < \delta_{\rm CP} < 360^\circ$ as obtained by the global analysis in both standard three flavour and in presence of NSI., Comment: 22 pages, 7 figures, 5 tables, version accepted for publication in JHEP

We study electrophoretic mobility of metal-dielectric Janus particles with dipolar director profile in two nematic liquid crystals (LCs) having same (positive) conductivity anisotropy and opposite dielectric anisotropy. The applied ac electric field is parallel and perpendicular to the director for the positive and negative dielectric anisotropy LCs, respectively. The velocity of the Janus particles in both LCs is significantly higher than that of the non-Janus particles. We map the electroosmotic flow fields surrounding the particles using microparticle image velocimetry ($\mu$-PIV) and show that the flows on the metal hemisphere is stronger than that on the dielectric hemisphere and the pumping of LC along the direction of motion of the Janus particles is more than that of the non-Janus particles. For a given liquid crystal, particles with asymmetric surface properties is useful for enhancing their electrophoretic mobility and activity., Comment: 6 pages, 6 figures

Social media becomes the central way for people to obtain and utilise news, due to its rapidness and inexpensive value of data distribution. Though, such features of social media platforms also present it a root cause of fake news distribution, causing adverse consequences on both people and culture. Hence, detecting fake news has become a significant research interest for bringing feasible real time solutions to the problem. Most current techniques of fake news disclosure are supervised, that need large cost in terms of time and effort to make a certainly interpreted dataset. The proposed framework concentrates on the text-based detection of fake news items while considering that only limited number of labels are available. Graphs are functioned extensively under several purposes of real-world problems on the strength of their property to structure things easily. Deep neural networks are used to generate great results within tasks that utilizes graph classification. The Graph Convolution Network works as a deep learning paradigm which works on graphs. Our proposed framework deals with limited amount of labelled data; we go for a semi-supervised learning method. We come up with a semi-supervised fake news detection technique based on GCN (Graph Convolutional Networks). The recommended architecture comprises of three basic components: collecting word embeddings from the news articles in datasets utilising GloVe, building similarity graph using Word Movers Distance (WMD) and finally applying Graph Convolution Network (GCN) for binary classification of news articles in semi-supervised paradigm. The implemented technique is validated on three different datasets by varying the volume of labelled data achieving 95.27 % highest accuracy on Real or Fake dataset. Comparison with other contemporary techniques also reinforced the supremacy of the proposed framework., Comment: 25 pages, 7 figures

The nuisance of misinformation and fake news has escalated many folds since the advent of online social networks. Human consciousness and decision-making capabilities are negatively influenced by manipulated, fabricated, biased or unverified news posts. Therefore, there is a high demand for designing veracity analysis systems to detect fake information contents in multiple data modalities. In an attempt to find a sophisticated solution to this critical issue, we proposed an architecture to consider both the textual and visual attributes of the data. After the data pre-processing is done, text and image features are extracted from the training data using separate deep learning models. Feature extraction from text is done using BERT and ALBERT language models that leverage the benefits of bidirectional training of transformers using a deep self-attention mechanism. The Inception-ResNet-v2 deep neural network model is employed for image data to perform the task. The proposed framework focused on two independent multi-modal fusion architectures of BERT and Inception-ResNet-v2 as well as ALBERT and Inception-ResNet-v2. Multi-modal fusion of textual and visual branches is extensively experimented and analysed using concatenation of feature vectors and weighted averaging of probabilities named as Early Fusion and Late Fusion respectively. Three publicly available broadly accepted datasets All Data, Weibo and MediaEval 2016 that incorporates English news articles, Chinese news articles, and Tweets correspondingly are used so that our designed framework's outcomes can be properly tested and compared with previous notable work in the domain., Comment: 31 pages, 12 figures

The verification of multimedia content over social media is one of the challenging and crucial issues in the current scenario and gaining prominence in an age where user-generated content and online social web platforms are the leading sources in shaping and propagating news stories. As these sources allow users to share their opinions without restriction, opportunistic users often post misleading/ unreliable content on social media such as Twitter, Facebook, etc. At present, to lure users towards the news story, the text is often attached with some multimedia content (images/videos/audios). Verifying these contents to maintain the credibility and reliability of social media information is of paramount importance. Motivated by this, we proposed a generalized system that supports the automatic classification of images into credible or misleading. In this paper, we investigated machine learning-based as well as deep learning-based approaches utilized to verify misleading multimedia content, where the available image traces are used to identify the credibility of the content. The experiment is performed on the real-world dataset (Media-eval-2015 dataset) collected from Twitter. It also demonstrates the efficiency of our proposed approach and features using both Machine and Deep Learning Model (Bi-directional LSTM). The experiment result reveals that the Microsoft bings image search engine is quite effective in retrieving titles and performs better than our study's Google image search engine. It also shows that gathering clues from attached multimedia content (image) is more effective than detecting only posted content-based features., Comment: 22 pages, 8 figures

Spreading of misleading information on social web platforms has fuelled huge panic and confusion among the public regarding the Corona disease, the detection of which is of paramount importance. To address this issue, in this paper, we have developed an automated system that can collect and validate the fact from multi web-platform to decide the credibility of the content. To identify the credibility of the posted claim, probable instances/clues(titles) of news information are first gathered from various web platforms. Later, the crucial set of features is retrieved that further feeds into the ensemble-based machine learning model to classify the news as misleading or real. The four sets of features based on the content, linguistics/semantic cues, similarity, and sentiments gathered from web-platforms and voting are applied to validate the news. Finally, the combined voting decides the support given to a specific claim. In addition to the validation part, a unique source platform is designed for collecting data/facts from three web platforms (Twitter, Facebook, Google) based on certain queries/words. This unique platform can also help researchers build datasets and gather useful/efficient clues from various web platforms. It has been observed that our proposed intelligent strategy gives promising results and quite effective in predicting misleading information. The proposed work provides practical implications for the policy makers and health practitioners that could be useful in protecting the world from misleading information proliferation during this pandemic., Comment: 22 pages, 06 figures