Spatial computing is a technological advancement that facilitates the seamless integration of devices into the physical environment, resulting in a more natural and intuitive digital world user experience. Spatial computing has the potential to become a significant advancement in the field of computing. From GPS and location-based services to healthcare, spatial computing technologies have influenced and improved our interactions with the digital world. The use of spatial computing in creating interactive digital environments has become increasingly popular and effective. This is explained by its increasing significance among researchers and industrial organisations, which motivated us to conduct this review. This review provides a detailed overview of spatial computing, including its enabling technologies and its impact on various applications. Projects related to spatial computing are also discussed. In this review, we also explored the potential challenges and limitations of spatial computing. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of spatial computing, its enabling technologies, their impact on various applications, emerging challenges, and potential solutions.

Developing questions that are pedagogically sound, relevant, and promote learning is a challenging and time-consuming task for educators. Modern-day large language models (LLMs) generate high-quality content across multiple domains, potentially helping educators to develop high-quality questions. Automated educational question generation (AEQG) is important in scaling online education catering to a diverse student population. Past attempts at AEQG have shown limited abilities to generate questions at higher cognitive levels. In this study, we examine the ability of five state-of-the-art LLMs of different sizes to generate diverse and high-quality questions of different cognitive levels, as defined by Bloom's taxonomy. We use advanced prompting techniques with varying complexity for AEQG. We conducted expert and LLM-based evaluations to assess the linguistic and pedagogical relevance and quality of the questions. Our findings suggest that LLms can generate relevant and high-quality educational questions of different cognitive levels when prompted with adequate information, although there is a significant variance in the performance of the five LLms considered. We also show that automated evaluation is not on par with human evaluation.

Vision Transformers (ViTs) have significantly advanced image classification by applying self-attention on patch embeddings. However, the standard MLP blocks in each Transformer layer may not capture complex nonlinear dependencies optimally. In this paper, we propose ViKANformer, a Vision Transformer where we replace the MLP sub-layers with Kolmogorov-Arnold Network (KAN) expansions, including Vanilla KAN, Efficient-KAN, Fast-KAN, SineKAN, and FourierKAN, while also examining a Flash Attention variant. By leveraging the Kolmogorov-Arnold theorem, which guarantees that multivariate continuous functions can be expressed via sums of univariate continuous functions, we aim to boost representational power. Experimental results on MNIST demonstrate that SineKAN, Fast-KAN, and a well-tuned Vanilla KAN can achieve over 97% accuracy, albeit with increased training overhead. This trade-off highlights that KAN expansions may be beneficial if computational cost is acceptable. We detail the expansions, present training/test accuracy and F1/ROC metrics, and provide pseudocode and hyperparameters for reproducibility. Finally, we compare ViKANformer to a simple MLP and a small CNN baseline on MNIST, illustrating the efficiency of Transformer-based methods even on a small-scale dataset.

This paper presents an unsupervised segment-based method for robust voice activity detection (rVAD). The method consists of two passes of denoising followed by a voice activity detection (VAD) stage. In the first pass, high-energy segments in a speech signal are detected by using a posteriori signal-to-noise ratio (SNR) weighted energy difference and if no pitch is detected within a segment, the segment is considered as a high-energy noise segment and set to zero. In the second pass, the speech signal is denoised by a speech enhancement method, for which several methods are explored. Next, neighbouring frames with pitch are grouped together to form pitch segments, and based on speech statistics, the pitch segments are further extended from both ends in order to include both voiced and unvoiced sounds and likely non-speech parts as well. In the end, a posteriori SNR weighted energy difference is applied to the extended pitch segments of the denoised speech signal for detecting voice activity. We evaluate the VAD performance of the proposed method using two databases, RATS and Aurora-2, which contain a large variety of noise conditions. The rVAD method is further evaluated, in terms of speaker verification performance, on the RedDots 2016 challenge database and its noise-corrupted versions. Experiment results show that rVAD is compared favourably with a number of existing methods. In addition, we present a modified version of rVAD where computationally intensive pitch extraction is replaced by computationally efficient spectral flatness calculation. The modified version significantly reduces the computational complexity at the cost of moderately inferior VAD performance, which is an advantage when processing a large amount of data and running on low resource devices. The source code of rVAD is made publicly available.

The Naira is Nigeria's official currency in daily transactions. This study presents the deployment and evaluation of Deep Learning (DL) models to classify Currency Notes (Naira) by denomination. Using a diverse dataset of 1,808 images of Naira notes captured under different conditions, trained the models employing different architectures and got the highest accuracy with MobileNetV2, the model achieved a high accuracy rate of in training of 90.75% and validation accuracy of 87.04% in classification tasks and demonstrated substantial performance across various scenarios. This model holds significant potential for practical applications, including automated cash handling systems, sorting systems, and assistive technology for the visually impaired. The results demonstrate how the model could boost the Nigerian economy's security and efficiency of financial transactions.

For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) has started playing a significant role. By evaluating complex data from imaging, genetics, and behavioral assessments, these technologies have the potential to significantly improve clinical outcomes. However, they also present unique challenges related to data integration and ethical issues. This survey reviews the development of ML and DL methods for the early diagnosis and treatment of mental health issues. It examines a range of applications, with a particular emphasis on behavioral assessments, genetic and biomarker analysis, and medical imaging for diagnosing diseases like depression, bipolar disorder, and schizophrenia. Predictive modeling for illness progression is further discussed, focusing on the role of risk prediction models and longitudinal studies. Key findings highlight how ML and DL can improve diagnostic accuracy and treatment outcomes while addressing methodological inconsistencies, data integration challenges, and ethical concerns. The study emphasizes the importance of building real-time monitoring systems for individualized treatment, enhancing data fusion techniques, and fostering interdisciplinary collaboration. Future research should focus on overcoming these obstacles to ensure the valuable and ethical application of ML and DL in mental health services.

The cosmological dynamics are rigorously investigated through the systematic application of autonomous system analysis to the gravitational field equations in non-metricity gravity. The systematic procedure to analyze the late-time cosmic acceleration in higher-order non-metricity gravity is demonstrated by exploring non-hyperbolic critical points with the center manifold theory. The stability properties of these critical points are also evaluated based on the analysis of eigenvalues and phase portraits. It is explicitly shown that the stable node can be realized. The critical points of each model are individually analyzed, and their corresponding cosmological implications are derived. The stability properties of these critical points are evaluated based on the analysis of eigenvalues and phase portraits, revealing that each model includes at least one stable node. Furthermore, the evolution plots of the cosmological parameters confirm the models capacity to exhibit accelerated expansion.

With challenges and limitations associated with security in the fintech industry, the rise to the need for data protection increases. However, the current existing passwordless and password-based peer to peer transactions in online banking systems are vulnerable to advanced forms of digital attacks. The influx of modern data protection methods keeps better records of the transactions, but it still does not address the issue of authentication and account takeovers during transactions. To the address the mentioned issue, this paper proposes a novel and robust peer to peer transaction system which employs best cloud security practices, proper use of cryptography and trusted computing to mitigate common vulnerabilities. We will be implementing FIDO2 compatible Smart Card to securely authenticate the user using physical smart cards and store the records in the cloud which enables access control by allowing access only when an access is requested. The standard incorporates multiple layers of security on cloud computing models to ensure secrecy of the said data. Services of the standard adhere to regulations provides by the government and assures privacy to the information of the payee or the end-user. The whole system has been implemented in the Internet of Things scenario.

Let $\Gamma$ be an undirected and simple graph. A set $S$ of vertices in $\Gamma$ is called a {cyclic vertex cutset} of $\Gamma$ if $\Gamma - S$ is disconnected and has at least two components each containing a cycle. If $\Gamma$ has a cyclic vertex cutset, then it is said to be {cyclically separable}. For any finite group $G$, the order supergraph $\mathcal{S}(G)$ is the simple and undirected graph whose vertices are elements of $G$, and two vertices are adjacent if as elements of $G$ the order of one divides the order of the other. In this paper, we characterize the finite nilpotent groups and various non-nilpotent groups, such as the dihedral groups, the dicyclic groups, the EPPO groups, the symmetric groups, and the alternating groups, whose order supergraphs are cyclically separable.

A dominating set of a graph $G$ is a subset $S$ of its vertices such that each vertex of $G$ not in $S$ has a neighbor in $S$. A face-hitting set of a plane graph $G$ is a set $T$ of vertices in $G$ such that every face of $G$ contains at least one vertex of $T$. We show that the vertex-set of every plane (multi-)graph without isolated vertices, self-loops or $2$-faces can be partitioned into two disjoint sets so that both the sets are dominating and face-hitting. We also show that all the three assumptions above are necessary for the conclusion. As a corollary, we show that every $n$-vertex simple plane triangulation has a dominating set of size at most $(1 - \alpha)n/2$, where $\alpha n$ is the maximum size of an independent set in the triangulation. Matheson and Tarjan [European J. Combin., 1996] conjectured that every plane triangulation with a sufficiently large number of vertices $n$ has a dominating set of size at most $n / 4$. Currently, the best known general bound for this is by Christiansen, Rotenberg and Rutschmann [SODA, 2024] who showed that every plane triangulation on $n > 10$ vertices has a dominating set of size at most $2n/7$. Our corollary improves their bound for $n$-vertex plane triangulations which contain a maximal independent set of size either less than $2n/7$ or more than $3n/7$.

Individuals with visual impairments often face a multitude of challenging obstacles in their daily lives. Vision impairment can severely impair a person's ability to work, navigate, and retain independence. This can result in educational limits, a higher risk of accidents, and a plethora of other issues. To address these challenges, we present MagicEye, a state-of-the-art intelligent wearable device designed to assist visually impaired individuals. MagicEye employs a custom-trained CNN-based object detection model, capable of recognizing a wide range of indoor and outdoor objects frequently encountered in daily life. With a total of 35 classes, the neural network employed by MagicEye has been specifically designed to achieve high levels of efficiency and precision in object detection. The device is also equipped with facial recognition and currency identification modules, providing invaluable assistance to the visually impaired. In addition, MagicEye features a GPS sensor for navigation, allowing users to move about with ease, as well as a proximity sensor for detecting nearby objects without physical contact. In summary, MagicEye is an innovative and highly advanced wearable device that has been designed to address the many challenges faced by individuals with visual impairments. It is equipped with state-of-the-art object detection and navigation capabilities that are tailored to the needs of the visually impaired, making it one of the most promising solutions to assist those who are struggling with visual impairments.

Gd2GaSbO7 and Gd2InSbO7 pyrochlore compounds exhibit quantum fluctuations in a spin ice-like state. These compounds have not been adequately studied based on the concept of magnetic frustration. Here, we have synthesised and characterised Gd2GaSbO7 and Gd2InSbO7 to investigate their ground-state magnetic properties. We confirmed the cubic pyrochlore structure via X-ray powder diffraction. Magnetic and heat capacity measurements show an absence of magnetic order down to 400 mK. Interestingly, the low-temperature magnetic heat capacity of Gd2GaSbO7 and Gd2InSbO7 exhibits a broad maximum around T_{\text{max}} = 0.9 K and T_{\text{max}} = 1 K, respectively. The low-temperature Cmag data fit with the power-law C_{\text{mag}} \approx T^{\frac{d}{n}} with dependencies of T^{1.71} for Gd2GaSbO7 and T^{1.49} for Gd2InSbO7, suggesting spin fluctuations caused by the disorder of magnetic moments at low temperatures. Both compounds retain residual entropy akin to water ice, resembling the spin ice state. Additionally, we analysed the ratio between nearest-neighbour exchange and dipolar interaction for all reported Gd-based pyrochlores, finding that Gd2InSbO7 has the lowest and weakest exchange interaction among them. This indicates that chemical pressure affects exchange interaction due to the different pathways of mediators between magnetic ions. This study reveals robust quantum fluctuations in the ground state in GGSO and GISO, similar to quantum spin ice compounds such as Pr2Zr2O7, Pr2Sn2O7, and Yb2GaSbO7. Other Gd-pyrochlore compounds exhibit long-range magnetic order, which is in stark contrast to this.

Nowadays, yoga has gained worldwide attention because of increasing levels of stress in the modern way of life, and there are many ways or resources to learn yoga. The word yoga means a deep connection between the mind and body. Today there is substantial Medical and scientific evidence to show that the very fundamentals of the activity of our brain, our chemistry even our genetic content can be changed by practicing different systems of yoga. Suryanamaskar, also known as salute to the sun, is a yoga practice that combines eight different forms and 12 asanas(4 asana get repeated) devoted to the Hindu Sun God, Surya. Suryanamaskar offers a number of health benefits such as strengthening muscles and helping to control blood sugar levels. Here the Mediapipe Library is used to analyze Surya namaskar situations. Standing is detected in real time with advanced software, as one performs Surya namaskar in front of the camera. The class divider identifies the form as one of the following: Pranamasana, Hasta Padasana, Hasta Uttanasana, Ashwa - Sanchalan asana, Ashtanga Namaskar, Dandasana, or Bhujangasana and Svanasana. Deep learning-based techniques(CNN) are used to develop this model with model accuracy of 98.68 percent and an accuracy score of 0.75 to detect correct yoga (Surya Namaskar ) posture. With this method, the users can practice the desired pose and can check if the pose that the person is doing is correct or not. It will help in doing all the different poses of surya namaskar correctly and increase the efficiency of the yoga practitioner. This paper describes the whole framework which is to be implemented in the model.

Authentication systems have evolved a lot since the 1960s when Fernando Corbato first proposed the password-based authentication. In 2013, the FIDO Alliance proposed using secure hardware for authentication, thus marking a milestone in the passwordless authentication era [1]. Passwordless authentication with a possession-based factor often relied on hardware-backed cryptographic methods. FIDO2 being one an amalgamation of the W3C Web Authentication and FIDO Alliance Client to Authenticator Protocol is an industry standard for secure passwordless authentication with rising adoption for the same [2]. However, the current FIDO2 standards use ECDSA with SHA-256 (ES256), RSA with SHA-256 (RS256) and similar classical cryptographic signature algorithms. This makes it insecure against attacks involving large-scale quantum computers [3]. This study aims at exploring the usability of Module Lattice based Digital Signature Algorithm (ML-DSA), based on Crystals Dilithium as a post quantum cryptographic signature standard for FIDO2. The paper highlights the performance and security in comparison to keys with classical algorithms.

Recent increases in endpoint-based security events and threats compelled enterprise operations to switch to virtual desktop infrastructure and web-based applications. In addition to reducing potential hazards, this has guaranteed a consistent desktop environment for every user. On the other hand, the attack surface is greatly increased because all endpoints are connected to the company network, which could harbor malware and other advanced persistent threats. This results in a considerable loss of system resources on each individual endpoint. Hence our work proposes a standard called Colaboot that enables machines throughout a company to boot from a single operating system in order to address these problems and guarantee a consistent operating system environment that could be easily updated to the most recent security patches across all work stations.

In this study, we conducted a comprehensive review and analysis of the thermoelectric responses exhibited by monolayer pristine graphene in response to temperature variations. Employing the Boltzmann transport theory, we rigorously examined and evaluated various thermoelectric coefficients, with particular emphasis on elucidating their behavior under different scattering mechanisms. We derived the analytical expressions for electrical conductivity, thermopower, and thermal conductivity at low temperatures by Sommerfeld expansion of the Fermi integral. We demonstrated that our numerically obtained values are consistent with the analytical calculations at low temperatures and hence obeying Mott and Wiedeman Franz law. However, the deviation was observed at higher temperatures. Furthermore, we performed theoretical calculations of chemical potential at both low and high temperatures and compared them with our numerically evaluated results at all temperatures. Through extensive calculations and meticulous evaluation, our study contributes to a deeper understanding of the intricate thermoelectric properties inherent in monolayer pristine graphene.

Wireless Fidelity or Wi-Fi, has completely transfigured wireless networking by offering a smooth connection to the internet and networks, particularly when dealing with enclosed environments. As with the majority of wireless technology, it functions through radio communication. This makes it possible for Wi-Fi to operate effectively close to an Access Point. However, a device's ability to receive Wi-Fi signals can vary greatly. These discrepancies arise because of impediments or motions between the device and the access point. We have creatively used these variances as unique opportunities for applications that can be used to detect movement in confined areas. As this approach makes use of the current wireless infrastructure, no additional hardware is required. These applications could potentially be leveraged to enable sophisticated robots or enhance security systems.

Consider a graph $\Gamma$. A set $S$ of vertices in $\Gamma$ is called a {cyclic vertex cutset} of $\Gamma$ if $\Gamma - S$ is disconnected and has at least two components containing cycles. If $\Gamma$ has a cyclic vertex cutset, then it is said to be {cyclically separable}. The {cyclic vertex connectivity} is the minimum cardinality of a cyclic vertex cutset of $\Gamma$. The power graph $\mathcal{P}(G)$ of a group $G$ is the undirected simple graph with vertex set $G$ and two distinct vertices are adjacent if one of them is a positive power of the other. If $G$ is a cyclic, dihedral, or dicyclic group, we determine the order of $G$ such that $\mathcal{P}(G)$ is cyclically separable. Then we characterize the equality of vertex connectivity and cyclic vertex connectivity of $\mathcal{P}(G)$ in terms of the order of $G$.

We have investigated the element specific magnetic characteristics of single-crystal FeCr2S4 using x-ray absorption spectroscopy (XAS) and x-ray magnetic circular dichroism (XMCD). We have found that the Fe L2,3-edge XAS spectra do not exhibit clear multiplet structures, indicating strong hybridization between the Fe 3d and S 3p orbitals, leading to delocalized rather than localized electronic states. The Fe 3d and Cr 3d spin moments are antiferromagnetically coupled, consistent with the Goodenough-Kanamori rule. The orbital magnetic moments of Fe and Cr are determined to be -0.23 and -0.017 {\mu}B/ion, respectively. The large orbital magnetic moment of Fe is due to the d6 configuration under the relatively weak tetrahedra crystal field at the Fe site, and the delocalized Fe electrons maintain the orbital degree of freedom in spite of their itinerant nature. To understand phenomena such as the gigantic Kerr rotation, it is essential to consider not only the orbital degrees of freedom but also the role of spin-orbit coupling, which induces a finite orbital magnetic moment through t2 and e level hybridization under the tetrahedral crystal field. This finite orbital moment serves as a direct indicator of spin-orbit interaction strength and links element-specific orbital magnetism to the large Kerr rotation. On the other hand, the octahedral crystal-field splitting of the Cr 3d level is large enough to result in the quenching of the orbital moment of the Cr ion in FeCr2S4.

In the growing world of the internet, the number of ways to obtain crucial data such as passwords and login credentials, as well as sensitive personal information has expanded. Page impersonation, often known as phishing, is one method of obtaining such valuable information. Phishing is one of the most straightforward forms of cyberattack for hackers and one of the simplest for victims to fall for. It can also provide hackers with everything they need to get access to their target's personal and corporate accounts. Such websites do not offer a service, but instead, gather personal information from users. In this paper, we achieved state-of-the-art accuracy in detecting malicious URLs using recurrent neural networks. Unlike previous studies, which looked at online content, URLs, and traffic numbers, we merely look at the text in the URL, which makes it quicker and catches zero-day assaults. The network has been optimised to be utilised on tiny devices like Mobiles, and Raspberry Pi without sacrificing the inference time.