Search

The article investigates cosmological applications of $f(Q)$ theories in a non-coincident formalism. We explore a new $f(Q)$ theory dynamics utilizing a non-vanishing affine connection involving a non-constant function $\gamma(t)=-a^{-1}\dot{H}$, resulting in Friedmann equations that are entirely distinct from those of $f(T)$ theory. In addition, we propose a new parameterization of the Hubble function that can consistently depicts the present deceleration parameter value, transition redshift, and the late time de-Sitter limit. We evaluate the predictions of the assumed Hubble function by imposing constraints on the free parameters utilizing Bayesian statistical analysis to estimate the posterior probability by employing the CC, Pantheon+SH0ES, and the BAO samples. Moreover, we conduct the AIC and BIC statistical evaluations to determine the reliability of MCMC analysis. Further, we consider some well-known corrections to the STEGR case such as an exponentital $f(Q)$ correction, logarithmic $f(Q)$ correction, and a power-law $f(Q)$ correction and then we find the constraints on the parameters of these models via energy conditions. Finally, to test the physical plausibility of the assumed $f(Q)$ models we conduct the thermodynamical stability analysis via the sound speed parameter., Comment: Journal of High Energy Astrophysics published version

The nonlocality arising in a multi-party network involving multiple independent sources radically differs from the standard multipartite Bell nonlocality involving a single source. The notion of the full network nonlocality (FNN) (Phys. Rev. Lett.128, 010403 (2022)) characterizes the quantum correlations that cannot be reproduced by a local-nonlocal model featuring one local source and the rest of nonlocal no-signaling sources. However, the demonstration of FNN was limited to bilocal and trilocal star-shaped network scenarios involving three or two dichotomic measurements for edge parties. In this paper, we first demonstrate that a large class of prevailing network inequalities does not exhibit FNN. We then introduce an elegant set of arbitrary-party and unbounded-input network inequalities in star-shaped and linear-chain networks whose optimal quantum violation exhibits FNN, certifying that the nonlocality is genuinely distributed to the entire network. Contrasting to existing demonstrations of FNN that inevitably require fixed-input and four-output elegant joint measurements for the central party, our generalized inequalities are more experimentally friendly, requiring only two-output measurements. Moreover, our derivation of optimal quantum violation is fully analytic and devoid of assuming the dimension of the quantum system, thereby showcasing its potential for device-independent self-testing.

Semi-supervised learning (SSL) has shown its effectiveness in learning effective 3D representation from a small amount of labelled data while utilizing large unlabelled data. Traditional semi-supervised approaches rely on the fundamental concept of predicting pseudo-labels for unlabelled data and incorporating them into the learning process. However, we identify that the existing methods do not fully utilize all the unlabelled samples and consequently limit their potential performance. To address this issue, we propose AllMatch, a novel SSL-based 3D classification framework that effectively utilizes all the unlabelled samples. AllMatch comprises three modules: (1) an adaptive hard augmentation module that applies relatively hard augmentations to the high-confident unlabelled samples with lower loss values, thereby enhancing the contribution of such samples, (2) an inverse learning module that further improves the utilization of unlabelled data by learning what not to learn, and (3) a contrastive learning module that ensures learning from all the samples in both supervised and unsupervised settings. Comprehensive experiments on two popular 3D datasets demonstrate a performance improvement of up to 11.2% with 1% labelled data, surpassing the SOTA by a significant margin. Furthermore, AllMatch exhibits its efficiency in effectively leveraging all the unlabelled data, demonstrated by the fact that only 10% of labelled data reaches nearly the same performance as fully-supervised learning with all labelled data. The code of our work is available at: https://github.com/snehaputul/AllMatch., Comment: Accepted at the European Conference on Computer Vision, ECCV 2024

Rheumatoid arthritis (RA) has an intricate etiology that includes environmental factors as well as genetics. Organophosphate esters (OPEs) are frequently used as chemical additives in many personal care products and household items. However, there has been limited research on their potential effects on rheumatoid arthritis (RA). The specific associations between OPEs and RA remain largely unexplored. This study investigates any potential associations between adult rheumatoid arthritis risk and exposure to OPEs. We investigated data from the National Health and Nutrition Examination Survey (NHANES) 2011-2018 among participants over 20 years old. In two models, multivariable logistic regression was utilized to investigate the relationship between exposure to OPEs and RA. Furthermore, subgroup analyses stratified by age, gender, and dose exposure response were evaluated. Generalized additive models and smooth curve fits were used to characterize the nonlinear relationship between RA and OPEs. In conclusion, 5490 individuals (RA: 319, Non-RA: 5171) were analyzed. Higher quantiles (Q4) of DPHP and DBUP showed a higher prevalence of RA than the lowest quantiles. Our findings show that adult RA prevalence is higher in those who have been exposed to OPEs (DPHP, DBUP). These correlations seem to be stronger among women, the elderly, those with higher BMIs, and those who have diabetes. The dose-response curve for DPHP and DBUP demonstrated an upward-sloping trend. In contrast, BCEP and BCPP showed a U-shaped relationship and an inverted U-shaped relationship with the probability of RA. BDCPP demonstrates a complex relationship with a peak at lower concentrations followed by a decrease. Our study concludes that exposure to OPEs plays a crucial role in the pathogenesis of RA.

Speech technology has been increasingly deployed in various areas of daily life including sensitive domains such as healthcare and law enforcement. For these technologies to be effective, they must work reliably for all users while preserving individual privacy. Although tradeoffs between privacy and utility, as well as fairness and utility, have been extensively researched, the specific interplay between privacy and fairness in speech processing remains underexplored. This review and position paper offers an overview of emerging privacy-fairness tradeoffs throughout the entire machine learning lifecycle for speech processing. By drawing on well-established frameworks on fairness and privacy, we examine existing biases and sources of privacy harm that coexist during the development of speech processing models. We then highlight how corresponding privacy-enhancing technologies have the potential to inadvertently increase these biases and how bias mitigation strategies may conversely reduce privacy. By raising open questions, we advocate for a comprehensive evaluation of privacy-fairness tradeoffs for speech technology and the development of privacy-enhancing and fairness-aware algorithms in this domain.

We prove a Hankel-variant commutant lifting theorem. This also uncovers the complete structure of the Beurling-type reducing and invariant subspaces of Hankel operators. Kernel spaces of Hankel operators play a key role in the analysis., Comment: 18 pages. Revised

Adversarial attacks, particularly the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) pose significant threats to the robustness of deep learning models in image classification. This paper explores and refines defense mechanisms against these attacks to enhance the resilience of neural networks. We employ a combination of adversarial training and innovative preprocessing techniques, aiming to mitigate the impact of adversarial perturbations. Our methodology involves modifying input data before classification and investigating different model architectures and training strategies. Through rigorous evaluation of benchmark datasets, we demonstrate the effectiveness of our approach in defending against FGSM and PGD attacks. Our results show substantial improvements in model robustness compared to baseline methods, highlighting the potential of our defense strategies in real-world applications. This study contributes to the ongoing efforts to develop secure and reliable machine learning systems, offering practical insights and paving the way for future research in adversarial defense. By bridging theoretical advancements and practical implementation, we aim to enhance the trustworthiness of AI applications in safety-critical domains., Comment: This is the preprint of the paper that has been accepted for oral presentation and publication in the Proceedings of the IEEE Asian Conference on Intelligent Technologies (ACOIT'2014). The conference will be organized in Kolar, Karnataka, INDIA from September 6 to 7, 2024. The paper is 8 pages long, and it contains 9 Figures and 4 Tables. This is NOT the final version of the paper

We explore the prospects of measurements of spectral moments of inclusive charm decays with BESIII. The rich and uniquely clean data set of charm mesons and baryons at BESIII offers a unique laboratory to study the evolution of Heavy Quark Expansion (HQE) parameters across different charm hadron species and to shed light on the interplay between heavy quark dynamics and light quark effects. The HQE in terms of inverse powers of the heavy meson mass is well-established in beauty decays, however due to the lighter charm mass its applicability to charm remains an open question. To date no determination of the HQE parameters, the kinetic energy, chromomagnetic moment and Darwin terms, has been attempted. A particular important role here is given to the Darwin and weak annihilation operators, whose values are important to predict lifetimes of heavy hadrons. Using a fast simulation for the BESIII detector response and predictions for spectral moments, we investigate the sensitivity to HQE parameters with today's and possible future data sets. In addition, we discuss the theory challenges for the HQE in charm and the experimental limitations. We further investigate the sensitivity of determining the CKM matrix element $|V_{cs}|$ with inclusive semileptonic charm decays.

Federated Learning (FL) represents a significant advancement in distributed machine learning, enabling multiple participants to collaboratively train models without sharing raw data. This decentralized approach enhances privacy by keeping data on local devices. However, FL introduces new privacy challenges, as model updates shared during training can inadvertently leak sensitive information. This chapter delves into the core privacy concerns within FL, including the risks of data reconstruction, model inversion attacks, and membership inference. It explores various privacy-preserving techniques, such as Differential Privacy (DP) and Secure Multi-Party Computation (SMPC), which are designed to mitigate these risks. The chapter also examines the trade-offs between model accuracy and privacy, emphasizing the importance of balancing these factors in practical implementations. Furthermore, it discusses the role of regulatory frameworks, such as GDPR, in shaping the privacy standards for FL. By providing a comprehensive overview of the current state of privacy in FL, this chapter aims to equip researchers and practitioners with the knowledge necessary to navigate the complexities of secure federated learning environments. The discussion highlights both the potential and limitations of existing privacy-enhancing techniques, offering insights into future research directions and the development of more robust solutions., Comment: This is the accepted version of the book chapter that has been accepted for inclusion in the book titled "Data Privacy: Techniques, Applications, and Standards. Editor: Jaydip Sen, IntechOpen Publishers, London, UK. ISBN: 978-1-83769-675-8. The chapter is 29 pages long

A crucial aspect of wormhole (WH) physics is the inclusion of exotic matter, which requires violating the null energy condition. Here, we explore the potential for WHs to be sustained by quark matter under conditions of extreme density along with the phantom-like generalized cosmic Chaplygin gas (GCCG) in symmetric teleparallel gravity. Theoretical and experimental studies on baryon structures indicate that strange quark matter, composed of u (up), d (down), and s (strange) quarks, represents the most energy-efficient form of baryonic matter. Drawing from these theoretical insights, we use the Massachusetts Institute of Technology (MIT) bag model equation of state to characterize ordinary quark matter. By formulating specific configurations for the bag parameter, we develop several WH models corresponding to different shape functions for the isotropic and anisotropic cases. Our analysis strongly suggests that an isotropic WH is not theoretically possible. Furthermore, we investigate traversable WH solutions utilizing a phantom-like GCCG, examining their feasibility. This equation of state, capable of violating the null energy condition, can elucidate late-time cosmic acceleration through various beneficial parameters. In this framework, we derive WH solutions for both constant and variable redshift functions. We have employed the volume integral quantifier (VIQ) method for both studies to assess the quantity of exotic matter. Furthermore, we have done the equilibrium analysis through the Tolman-Oppenheimer-Volkoff (TOV) equation, which supports the viability of our constructed WH model., Comment: Physics of the Dark Universe published version

In our investigation, we pioneer the development of geometrically deformed strange stars within the framework of $f(\mathcal{T})$ gravity theory through gravitational decoupling via the complete geometric deformation (CGD) technique. The significant finding is the precise solution for deformed strange star (SS) models achieved through the vanishing complexity factor scenario. Further, we introduce the concept of space-time deformation caused by dark matter (DM) content in DM haloes, leading to perturbations in the metric potentials $g_{tt}$ and $g_{rr}$ components. Mathematically, this DM-induced deformation is achieved through the CGD method, where the decoupling parameter $\alpha$ governs the extent of DM influence. To validate our findings, we compare our model predictions with observational constraints, including GW190814 (with a mass range of $2.5-2.67 M_{\odot}$) and neutron stars (NSTRs) such as EXO 1785-248 [mass=$1.3_{-0.2}^{+0.2}~M_{\odot}$], 4U 1608-52 [mass=$1.74_{-0.14}^{+0.14}~M_{\odot}$], and PSR J0952-0607 [mass=$2.35_{-0.17}^{+0.17}~M_{\odot}$]. Our investigation delves into the stability of the model by considering causality conditions, Herrera's Cracking Method, the adiabatic index, and the Harrison-Zeldovich-Novikov criterion. We demonstrate that the developed model mimics a wide range of recently observed pulsars. To emphasize its compatibility, we highlight the predicted mass and radius in tabular form by varying both the parameters $\alpha$ and $\zeta_1$. Notably, our findings are consistent with the observation of gravitational waves from the first binary merger event. Furthermore, we compare our results with those obtained for a slow-rotating configuration. In addition to this, we discuss the moment of inertia using the Bejger-Haensel approach in this formulation., Comment: 22 pages, 10 figures. Comments are welcome

The ever-growing popularity of large language models (LLMs) has resulted in their increasing adoption for hardware design and verification. Prior research has attempted to assess the capability of LLMs to automate digital hardware design by producing superior-quality Register Transfer Logic (RTL) descriptions, particularly in Verilog. However, these tests have revealed that Verilog code production using LLMs at current state-of-the-art lack sufficient functional correctness to be practically viable, compared to automatic generation of programs in general-purpose programming languages such as C, C++, Python, etc. With this as the key insight, in this paper we assess the performance of a two-stage software pipeline for automated Verilog RTL generation: LLM based automatic generation of annotated C++ code suitable for high-level synthesis (HLS), followed by HLS to generate Verilog RTL. We have benchmarked the performance of our proposed scheme using the open-source VerilogEval dataset, for four different industry-scale LLMs, and the Vitis HLS tool. Our experimental results demonstrate that our two-step technique substantially outperforms previous proposed techniques of direct Verilog RTL generation by LLMs in terms of average functional correctness rates, reaching score of 0.86 in pass@1 metric.

Alterations in Heart Rate (HR) and Heart Rate Variability (HRV) can reflect autonomic dysfunction associated with neurodegeneration. We investigate the influence of Mild Cognitive Impairment (MCI) on HR and its variability measures in the Indian population by designing a complete signal processing pipeline to detect the R-wave peaks and compute HR and HRV features from ECG recordings of 10 seconds, for point-of-care applications. The study cohort involves 297 urban participants, among which 48.48% are male and 51.51% are female. From the Addenbrooke's Cognitive Examination-III (ACE-III), MCI is detected in 19.19% of participants and the rest, 80.8% of them are cognitively healthy. Statistical features like central tendency (mean and root mean square (RMS) of the Normal-to-Normal (NN) intervals) and dispersion (standard deviation (SD) of all NN intervals (SDNN) and root mean square of successive differences of NN intervals (RMSSD)) of beat-to-beat intervals are computed. The Wilcoxon rank sum test reveals that mean of NN intervals (p = 0.0021), the RMS of NN intervals (p = 0.0014), the SDNN (p = 0.0192) and the RMSSD (p = 0.0206) values differ significantly between MCI and non-MCI classes, for a level of significance, 0.05. Machine learning classifiers like, Support Vector Machine (SVM), Discriminant Analysis (DA) and Naive Bayes (NB) driven by mean NN intervals, RMS, SDNN and RMSSD, show a high accuracy of 80.80% on each individual feature input. Individuals with MCI are observed to have comparatively higher HR than healthy subjects. HR and its variability can be considered as potential biomarkers for detecting MCI., Comment: Nil

In this study, we developed the geometrically deformed compact objects in the $f(Q, T)$ gravity theory under an electric field through gravitational decoupling via. minimal geometric deformation (MGD) technique for the first time. The decoupled field equations are solved via two different mimic approaches $\theta_0^0 = \rho$ and $\theta_1^1 = p_r$ through the Karmarkar condition. We conduct physical viability tests on our models and examine how decoupling parameters affect the physical qualities of objects. The obtained models are compared with the observational constraints for neutron stars PSR J1810+174, PSR J1959+2048, and PSR J2215+5135, including GW190814. Particularly, by modifying parameters $\alpha$ and $n$, we accomplish the occurrence of a "\textit{mass gap}" component. The resulting models exhibit stable, well-behaved mass profiles, regular behaviour, and no gravitational collapse, as verified by the Buchdahl--Andr\'{e}asson's limit. Furthermore, we provide a thorough physical analysis that is based on two parameters: $n$ ($f(Q,T)$--coupling parameter) and $\alpha$ (decoupling parameter). This work extends our current understanding of compact star configurations and sheds light on the behaviour of compact objects in the $f(Q,T)$ gravity., Comment: Fortschritte der Physik published version

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Adversarial attacks against deep learning models represent a major threat to the security and reliability of natural language processing (NLP) systems. In this paper, we propose a modification to the BERT-Attack framework, integrating Projected Gradient Descent (PGD) to enhance its effectiveness and robustness. The original BERT-Attack, designed for generating adversarial examples against BERT-based models, suffers from limitations such as a fixed perturbation budget and a lack of consideration for semantic similarity. The proposed approach in this work, PGD-BERT-Attack, addresses these limitations by leveraging PGD to iteratively generate adversarial examples while ensuring both imperceptibility and semantic similarity to the original input. Extensive experiments are conducted to evaluate the performance of PGD-BERT-Attack compared to the original BERT-Attack and other baseline methods. The results demonstrate that PGD-BERT-Attack achieves higher success rates in causing misclassification while maintaining low perceptual changes. Furthermore, PGD-BERT-Attack produces adversarial instances that exhibit greater semantic resemblance to the initial input, enhancing their applicability in real-world scenarios. Overall, the proposed modification offers a more effective and robust approach to adversarial attacks on BERT-based models, thus contributing to the advancement of defense against attacks on NLP systems., Comment: This paper is the pre-reviewed version of our paper that has been accepted for oral presentation and publication in the 4th IEEE ASIANCON. The conference will be organized in Pune, INDIA from August 23 to 25, 2024. The paper consists of 8 pages and it contains 10 tables. It is NOT the final camera-ready version that will be in IEEE Xplore

Time series from different regions of interest (ROI) of default mode network (DMN) from Functional Magnetic Resonance Imaging (fMRI) can reveal significant differences between healthy and unhealthy people. Here, we propose the utility of an existing metric quantifying the lack/presence of structure in a signal called, "deviation from stochasticity" (DS) measure to characterize resting-state fMRI time series. The hypothesis is that differences in the level of structure in the time series can lead to discrimination between the subject groups. In this work, an autoencoder-based model is utilized to learn efficient representations of data by training the network to reconstruct its input data. The proposed methodology is applied on fMRI time series of 50 healthy individuals and 50 subjects with Alzheimer's Disease (AD), obtained from publicly available ADNI database. DS measure for healthy fMRI as expected turns out to be different compared to that of AD. Peak classification accuracy of 95% was obtained using Gradient Boosting classifier, using the DS measure applied on 100 subjects.

Target tracking of surrounding vehicles is essential for collision avoidance in autonomous vehicles. Our approach to target tracking is based on causal numerical differentiation on relative position data to estimate relative velocity and acceleration. Causal numerical differentiation is useful for a wide range of estimation and control problems with application to robotics and autonomous systems. The present paper extends prior work on causal numerical differentiation based on retrospective cost input estimation (RCIE). Since the variance of the input-estimation error and its correlation with the state-estimation error (the sum of the variance and the correlation is denoted as $\widetilde{V}$) used in the Kalman filter update are unknown, the present paper considers an adaptive discrete-time Kalman filter, where $\widetilde{V}_k$ is updated at each time step $k$ to minimize the difference between the sample variance of the innovations and the variance of the innovations given by the Kalman filter. The performance of this approach is shown to reach the performance of numerical differentiation based on RCIE with the best possible fixed value of $\widetilde{V}_k$. The proposed method thus eliminates the need to determine the best possible fixed value for $\widetilde{V}_k$. Finally, RCIE with an adaptive Kalman filter is applied to target tracking of a vehicle using simulated data from CarSim.

An automated knowledge modeling algorithm for Cancer Clinical Practice Guidelines (CPGs) extracts the knowledge contained in the CPG documents and transforms it into a programmatically interactable, easy-to-update structured model with minimal human intervention. The existing automated algorithms have minimal scope and cannot handle the varying complexity of the knowledge content in the CPGs for different cancer types. This work proposes an improved automated knowledge modeling algorithm to create knowledge models from the National Comprehensive Cancer Network (NCCN) CPGs in Oncology for different cancer types. The proposed algorithm has been evaluated with NCCN CPGs for four different cancer types. We also proposed an algorithm to compare the knowledge models for different versions of a guideline to discover the specific changes introduced in the treatment protocol of a new version. We created a question-answering (Q&A) framework with the guideline knowledge models as the augmented knowledge base to study our ability to query the knowledge models. We compiled a set of 32 question-answer pairs derived from two reliable data sources for the treatment of Non-Small Cell Lung Cancer (NSCLC) to evaluate the Q&A framework. The framework was evaluated against the question-answer pairs from one data source, and it can generate the answers with 54.5% accuracy from the treatment algorithm and 81.8% accuracy from the discussion part of the NCCN NSCLC guideline knowledge model.

Traditional approaches for understanding phonological learning have predominantly relied on curated text data. Although insightful, such approaches limit the knowledge captured in textual representations of the spoken language. To overcome this limitation, we investigate the potential of the Featural InfoWaveGAN model to learn iterative long-distance vowel harmony using raw speech data. We focus on Assamese, a language known for its phonologically regressive and word-bound vowel harmony. We demonstrate that the model is adept at grasping the intricacies of Assamese phonotactics, particularly iterative long-distance harmony with regressive directionality. It also produced non-iterative illicit forms resembling speech errors during human language acquisition. Our statistical analysis reveals a preference for a specific [+high,+ATR] vowel as a trigger across novel items, indicative of feature learning. More data and control could improve model proficiency, contrasting the universality of learning., Comment: to be included in the Interspeech Proceedings

In this paper, we introduce an enhanced textual adversarial attack method, known as Saliency Attention and Semantic Similarity driven adversarial Perturbation (SASSP). The proposed scheme is designed to improve the effectiveness of contextual perturbations by integrating saliency, attention, and semantic similarity. Traditional adversarial attack methods often struggle to maintain semantic consistency and coherence while effectively deceiving target models. Our proposed approach addresses these challenges by incorporating a three-pronged strategy for word selection and perturbation. First, we utilize a saliency-based word selection to prioritize words for modification based on their importance to the model's prediction. Second, attention mechanisms are employed to focus perturbations on contextually significant words, enhancing the attack's efficacy. Finally, an advanced semantic similarity-checking method is employed that includes embedding-based similarity and paraphrase detection. By leveraging models like Sentence-BERT for embedding similarity and fine-tuned paraphrase detection models from the Sentence Transformers library, the scheme ensures that the perturbed text remains contextually appropriate and semantically consistent with the original. Empirical evaluations demonstrate that SASSP generates adversarial examples that not only maintain high semantic fidelity but also effectively deceive state-of-the-art natural language processing models. Moreover, in comparison to the original scheme of contextual perturbation CLARE, SASSP has yielded a higher attack success rate and lower word perturbation rate., Comment: The paper is 12 pages long. and it contains 5 tables. It is the pre-reviewed version of the paper that has been accepted for oral presentation and publication in the 5th International Conference on Data Science and Applications which will be organized in Jaipur, India from July 17 to 19, 2024. This is not the final version

Large Language Models (LLMs) have an increasing impact on our lives with use cases such as chatbots, study support, coding support, ideation, writing assistance, and more. Previous studies have revealed linguistic biases in pronouns used to describe professions or adjectives used to describe men vs women. These issues have to some degree been addressed in updated LLM versions, at least to pass existing tests. However, biases may still be present in the models, and repeated use of gender stereotypical language may reinforce the underlying assumptions and are therefore important to examine further. This paper investigates gender biases in LLMs in relation to educational choices through an open-ended, true to user-case experimental design and a quantitative analysis. We investigate the biases in the context of four different cultures, languages, and educational systems (English/US/UK, Danish/DK, Catalan/ES, and Hindi/IN) for ages ranging from 10 to 16 years, corresponding to important educational transition points in the different countries. We find that there are significant and large differences in the ratio of STEM to non-STEM suggested education paths provided by chatGPT when using typical girl vs boy names to prompt lists of suggested things to become. There are generally fewer STEM suggestions in the Danish, Spanish, and Indian context compared to the English. We also find subtle differences in the suggested professions, which we categorise and report.

Despite being more secure and strongly promoted, two-factor (2FA) or multi-factor (MFA) schemes either fail to protect against recent phishing threats such as real-time MITM, controls/relay MITM, malicious browser extension-based phishing attacks, and/or need the users to purchase and carry other hardware for additional account protection. Leveraging the unprecedented popularity of NFC and BLE-enabled smartphones, we explore a new horizon for designing an MFA scheme. This paper introduces an advanced authentication method for user verification that utilizes the user's real-time facial biometric identity, which serves as an inherent factor, together with BLE- NFC-enabled mobile devices, which operate as an ownership factor. We have implemented a prototype authentication system on a BLE-NFC-enabled Android device, and initial threat modeling suggests that it is safe against known phishing attacks. The scheme has been compared with other popular schemes using the Bonneau et al. assessment framework in terms of usability, deployability, and security.

Additive manufacturing (AM) techniques hold promise but face significant challenges in process planning and optimization. The large temporal and spatial variations in temperature that can occur in layer-wise AM lead to thermal excursions, resulting in property variations and defects. These variations cannot always be fully mitigated by simple static parameter search. To address this challenge, we propose a general approach based on modeling AM processes on the part-scale in state-space and framing AM process planning as a numerical optimal control problem. We demonstrate this approach on the problem of minimizing thermal variation in a given layer in the electron beam powder bed fusion (EB-PBF) AM process, and are able to compute globally optimal dynamic process plans. These optimized process plans are then evaluated in simulation, achieving an 87% and 86% reduction in cumulative variance compared to random spot melting and a uniform power field respectively, and are further validated in experiment. This one-shot feedforward planning approach expands the capabilities of AM technology by minimizing the need for experimentation and iteration to achieve process optimization. Further, this work opens the possibility for the application of optimal control theory to part-scale optimization and control in AM.

The trends and reactions of the general public towards global events can be analyzed using data from social platforms, including Twitter. The number of tweets has been reported to help detect variations in communication traffic within subsets like countries, age groups and industries. Similarly, publicly accessible data and (in particular) data from social media about agricultural issues provide a great opportunity for obtaining instantaneous snapshots of farmer opinions and a method to track changes in opinion through temporal analysis. In this paper we hypothesize that the presence of keywords like precision agriculture, digital agriculture, Internet of Things (IoT), BigData, remote sensing, GPS, etc., in tweets could serve as an indicator of discussions centered around interest in modern farming practices. We extracted relevant tweets using keywords such as IoT, BigData and Geographical Information System (GIS), and then analyzed their geographical origin and frequency of their mention. We analyzed the Twitter data for the period of 1st -11th January 2018 to understand these trends and the factors affecting them. These factors, such as special events, projects, biogeography, etc., were further analyzed using tweet sources and trending hashtags from the database. The regions with the highest interest in the keywords were United States, Egypt, Brazil, Japan and China. A comparison of frequency of keywords revealed IoT as the most tweeted word (77.6%) in the downloaded data. The most used language was English followed by Spanish, Japanese and French. Periodical tweets on IoT from an account handled by IoT project on Twitter and Seminars on IoT in January in Santa Catarina (Brazil) were found to be the underlying factors for the observed trends., Comment: 7 pages, conference accepted

This paper explores using a deep learning Long Short-Term Memory (LSTM) model for accurate stock price prediction and its implications for portfolio design. Despite the efficient market hypothesis suggesting that predicting stock prices is impossible, recent research has shown the potential of advanced algorithms and predictive models. The study builds upon existing literature on stock price prediction methods, emphasizing the shift toward machine learning and deep learning approaches. Using historical stock prices of 180 stocks across 18 sectors listed on the NSE, India, the LSTM model predicts future prices. These predictions guide buy/sell decisions for each stock and analyze sector profitability. The study's main contributions are threefold: introducing an optimized LSTM model for robust portfolio design, utilizing LSTM predictions for buy/sell transactions, and insights into sector profitability and volatility. Results demonstrate the efficacy of the LSTM model in accurately predicting stock prices and informing investment decisions. By comparing sector profitability and prediction accuracy, the work provides valuable insights into the dynamics of the current financial markets in India., Comment: This is the pre-print of the paper that has been accepted for publication in the Inderscience Journal "International Journal of Business Forecasting and Marketing Intelligence". The paper is 35 pages long, and it contains 37 figures and 20 tables. This is, however, not the final published version

This paper addresses the critical need for online action representation, which is essential for various applications like rehabilitation, surveillance, etc. The task can be defined as representation of actions as soon as they happen in a streaming video without access to video frames in the future. Most of the existing methods use predefined window sizes for video segments, which is a restrictive assumption on the dynamics. The proposed method employs a change detection algorithm to automatically segment action sequences, which form meaningful sub-actions and subsequently fit symbolic generative motion programs to the clipped segments. We determine the start time and end time of segments using change detection followed by a piece-wise linear fit algorithm on joint angle and bone length sequences. Domain-specific symbolic primitives are fit to pose keypoint trajectories of those extracted segments in order to obtain a higher level semantic representation. Since this representation is part-based, it is complementary to the compositional nature of human actions, i.e., a complex activity can be broken down into elementary sub-actions. We show the effectiveness of this representation in the downstream task of class agnostic repetition detection. We propose a repetition counting algorithm based on consecutive similarity matching of primitives, which can do online repetition counting. We also compare the results with a similar but offline repetition counting algorithm. The results of the experiments demonstrate that, despite operating online, the proposed method performs better or on par with the existing method.