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Checker bugs in Deep Learning (DL) libraries are critical yet not well-explored. These bugs are often concealed in the input validation and error-checking code of DL libraries and can lead to silent failures, incorrect results, or unexpected program behavior in DL applications. Despite their potential to significantly impact the reliability and performance of DL-enabled systems built with these libraries, checker bugs have received limited attention. We present the first comprehensive study of DL checker bugs in two widely-used DL libraries, i.e., TensorFlow and PyTorch. Initially, we automatically collected a dataset of 2,418 commits from TensorFlow and PyTorch repositories on GitHub from Sept. 2016 to Dec. 2023 using specific keywords related to checker bugs. Through manual inspection, we identified 527 DL checker bugs. Subsequently, we analyzed these bugs from three perspectives, i.e., root causes, symptoms, and fixing patterns. Using the knowledge gained via root cause analysis of checker bugs, we further propose TensorGuard, a proof-of-concept RAG-based LLM-based tool to detect and fix checker bugs in DL libraries via prompt engineering a series of ChatGPT prompts. We evaluated TensorGuard's performance on a test dataset that includes 92 buggy and 135 clean checker-related changes in TensorFlow and PyTorch from January 2024 to July 2024. Our results demonstrate that TensorGuard has high average recall (94.51\%) using Chain of Thought prompting, a balanced performance between precision and recall using Zero-Shot prompting and Few-Shot prompting strategies. In terms of patch generation, TensorGuard achieves an accuracy of 11.1\%, which outperforms the state-of-the-art bug repair baseline by 2\%. We have also applied TensorGuard on the latest six months' checker-related changes (493 changes) of the JAX library from Google, which resulted in the detection of 64 new checker bugs.

Graph Neural Networks (GNNs) have revolutionized the domain of graph representation learning by utilizing neighborhood aggregation schemes in many popular architectures, such as message passing graph neural networks (MPGNNs). This scheme involves iteratively calculating a node's representation vector by aggregating and transforming the representation vectors of its adjacent nodes. Despite their effectiveness, MPGNNs face significant issues, such as oversquashing, oversmoothing, and underreaching, which hamper their effectiveness. Additionally, the reliance of MPGNNs on the homophily assumption, where edges typically connect nodes with similar labels and features, limits their performance in heterophilic contexts, where connected nodes often have significant differences. This necessitates the development of models that can operate effectively in both homophilic and heterophilic settings. In this paper, we propose a novel approach, ClassContrast, grounded in Energy Landscape Theory from Chemical Physics, to overcome these limitations. ClassContrast combines spatial and contextual information, leveraging a physics-inspired energy landscape to model node embeddings that are both discriminative and robust across homophilic and heterophilic settings. Our approach introduces contrast-based homophily matrices to enhance the understanding of class interactions and tendencies. Through extensive experiments, we demonstrate that ClassContrast outperforms traditional GNNs in node classification and link prediction tasks, proving its effectiveness and versatility in diverse real-world scenarios., Comment: 16 pages, 5 figures

Physics-informed neural networks (PINNs) have emerged as a versatile and widely applicable concept across various science and engineering domains over the past decade. This article offers a comprehensive overview of the fundamentals of PINNs, tracing their evolution, modifications, and various variants. It explores the impact of different parameters on PINNs and the optimization algorithms involved. The review also delves into the theoretical advancements related to the convergence, consistency, and stability of numerical solutions using PINNs, while highlighting the current state of the art. Given their ability to address equations involving complex physics, the article discusses various applications of PINNs, with a particular focus on their utility in computational fluid dynamics problems. Additionally, it identifies current gaps in the research and outlines future directions for the continued development of PINNs.

This paper addresses the challenges of embedding common droop control characteristics in ac-dc power system steady-state simulation and optimization problems. We propose a smooth approximation methodology to construct differentiable functions that encode the attributes of piecewise linear droop control with saturation. We transform the nonsmooth droop curves into smooth nonlinear equality constraints, solvable with Newton methods and interior point solvers. These constraints are then added to power flow, optimal power flow, and security-constrained optimal power flow problems in ac-dc power systems. The results demonstrate significant improvements in accuracy in terms of power sharing response, voltage regulation, and system efficiency, while outperforming existing mixed-integer formulations in computational efficiency., Comment: 6 pages, 8 figures, 2024 IEEE PES Australasian Universities Power Engineering Conference

Prototyping complex computer-aided design (CAD) models in modern softwares can be very time-consuming. This is due to the lack of intelligent systems that can quickly generate simpler intermediate parts. We propose Text2CAD, the first AI framework for generating text-to-parametric CAD models using designer-friendly instructions for all skill levels. Furthermore, we introduce a data annotation pipeline for generating text prompts based on natural language instructions for the DeepCAD dataset using Mistral and LLaVA-NeXT. The dataset contains $\sim170$K models and $\sim660$K text annotations, from abstract CAD descriptions (e.g., generate two concentric cylinders) to detailed specifications (e.g., draw two circles with center $(x,y)$ and radius $r_{1}$, $r_{2}$, and extrude along the normal by $d$...). Within the Text2CAD framework, we propose an end-to-end transformer-based auto-regressive network to generate parametric CAD models from input texts. We evaluate the performance of our model through a mixture of metrics, including visual quality, parametric precision, and geometrical accuracy. Our proposed framework shows great potential in AI-aided design applications. Our source code and annotations will be publicly available., Comment: Accepted in NeurIPS 2024 (Spotlight)

Solar photovoltaic (PV) modules are prone to damage during manufacturing, installation and operation which reduces their power conversion efficiency. This diminishes their positive environmental impact over the lifecycle. Continuous monitoring of PV modules during operation via unmanned aerial vehicles is essential to ensure that defective panels are promptly replaced or repaired to maintain high power conversion efficiencies. Computer vision provides an automatic, non-destructive and cost-effective tool for monitoring defects in large-scale PV plants. We review the current landscape of deep learning-based computer vision techniques used for detecting defects in solar modules. We compare and evaluate the existing approaches at different levels, namely the type of images used, data collection and processing method, deep learning architectures employed, and model interpretability. Most approaches use convolutional neural networks together with data augmentation or generative adversarial network-based techniques. We evaluate the deep learning approaches by performing interpretability analysis on classification tasks. This analysis reveals that the model focuses on the darker regions of the image to perform the classification. We find clear gaps in the existing approaches while also laying out the groundwork for mitigating these challenges when building new models. We conclude with the relevant research gaps that need to be addressed and approaches for progress in this field: integrating geometric deep learning with existing approaches for building more robust and reliable models, leveraging physics-based neural networks that combine domain expertise of physical laws to build more domain-aware deep learning models, and incorporating interpretability as a factor for building models that can be trusted. The review points towards a clear roadmap for making this technology commercially relevant., Comment: 31 pages, 14 figures

Federated learning (FL) is a popular approach that enables organizations to train machine learning models without compromising data privacy and security. As the field of FL continues to grow, it is crucial to have a thorough understanding of the topic, current trends and future research directions for information systems (IS) researchers. Consequently, this paper conducts a comprehensive computational literature review on FL and presents the research landscape. By utilizing advanced data analytics and leveraging the topic modeling approach, we identified and analyzed the most prominent 15 topics and areas that have influenced the research on FL. We also proposed guiding research questions to stimulate further research directions for IS scholars. Our work is valuable for scholars, practitioners, and policymakers since it offers a comprehensive overview of state-of-the-art research on FL., Comment: 18 pages, 8 figures and 4 tables

Blockchain technology is an emerging digital innovation that has gained immense popularity in enhancing individual security and privacy within Information Systems (IS). This surge in interest is reflected in the exponential increase in research articles published on blockchain technology, highlighting its growing significance in the digital landscape. However, the rapid proliferation of published research presents significant challenges for manual analysis and synthesis due to the vast volume of information. The complexity and breadth of topics, combined with the inherent limitations of human data processing capabilities, make it difficult to comprehensively analyze and draw meaningful insights from the literature. To this end, we adopted the Computational Literature Review (CLR) to analyze pertinent literature impact and topic modelling using the Latent Dirichlet Allocation (LDA) technique. We identified 10 topics related to security and privacy and provided a detailed description of each topic. From the critical analysis, we have observed several limitations, and several future directions are provided as an outcome of this review., Comment: 10 pages, 8 figures and 1 table

In this article, we consider p and hp least-squares spectral element methods for one-dimensional elliptic boundary layer problems. We derive stability estimates and design a numerical scheme based on minimizing the residuals in the sense of least squares in appropriate Sobolev norms. We prove parameter robust uniform error estimates i.e. error in the approximation is independent of the boundary layer parameter. For the p-version we prove a robust uniform convergence rate of O(sqrt(log W)/W) in the H2-norm, where W denotes the polynomial order used in approximation and for the hp-version the convergence rate is shown to be O(e^(-W/logW)). Numerical results are presented for a number of model elliptic boundary layer problems confirming the theoretical estimates and uniform convergence results for the p and hp versions.

Nonlinear optics has become the workhorse for countless applications in classical and quantum optics, from optical bistability to single photon pair generation. However, the intrinsic weakness of optical nonlinearity has meant that large input powers and weak output powers are often a necessity in nonlinear frequency conversion. Here, motivated by recent advances in using non-Hermitian photonics and gain/loss engineering to enable non-reciprocal light transport, we explore how the interplay between non-Hermiticity and optical nonlinearity leads to a fundamentally new regime of nonlinear frequency conversion. We show how non-Hermitian coupling between discrete frequency modes can result in non-reciprocal flow of energy in the frequency dimension, closely resembling the non-Hermitian skin effect (NHSE). Applying our theory to a multimode nonlinear cavity supporting cascaded nonlinear processes, we create an asymmetric infrared (IR) comb that features a ``skin'' frequency mode populated with efficiency exceeding 85\%. Furthermore, we demonstrate how three-wave mixing processes in the non-reciprocal infrared comb we generate enables terahertz (THz) generation exceeding the Manley-Rowe limit. We then show how the non-reciprocal frequency conversion is robust against cavity defects and disorder that cause random fluctuations in the dissipation rate for different modes. Moreover, in certain regimes, the nonlinear, non-Hermitian system supports stable limit cycles that can enable multimode pulsing with picosecond pulse widths and GHz repetition rates. Finally, we explore how the system can be applied to generate simultaneous IR and THz frequency combs, potentially unlocking novel applications in spectroscopy and metrology.

Insider threats usually occur from within the workplace, where the attacker is an entity closely associated with the organization. The sequence of actions the entities take on the resources to which they have access rights allows us to identify the insiders. Insider Threat Detection (ITD) using Machine Learning (ML)-based approaches gained attention in the last few years. However, most techniques employed centralized ML methods to perform such an ITD. Organizations operating from multiple locations cannot contribute to the centralized models as the data is generated from various locations. In particular, the user behavior data, which is the primary source of ITD, cannot be shared among the locations due to privacy concerns. Additionally, the data distributed across various locations result in extreme class imbalance due to the rarity of attacks. Federated Learning (FL), a distributed data modeling paradigm, gained much interest recently. However, FL-enabled ITD is not yet explored, and it still needs research to study the significant issues of its implementation in practical settings. As such, our work investigates an FL-enabled multiclass ITD paradigm that considers non-Independent and Identically Distributed (non-IID) data distribution to detect insider threats from different locations (clients) of an organization. Specifically, we propose a Federated Adversarial Training (FedAT) approach using a generative model to alleviate the extreme data skewness arising from the non-IID data distribution among the clients. Besides, we propose to utilize a Self-normalized Neural Network-based Multi-Layer Perceptron (SNN-MLP) model to improve ITD. We perform comprehensive experiments and compare the results with the benchmarks to manifest the enhanced performance of the proposed FedATdriven ITD scheme., Comment: 10 pages, 7 figures

This study presents an advanced approach to lumbar spine segmentation using deep learning techniques, focusing on addressing key challenges such as class imbalance and data preprocessing. Magnetic resonance imaging (MRI) scans of patients with low back pain are meticulously preprocessed to accurately represent three critical classes: vertebrae, spinal canal, and intervertebral discs (IVDs). By rectifying class inconsistencies in the data preprocessing stage, the fidelity of the training data is ensured. The modified U-Net model incorporates innovative architectural enhancements, including an upsample block with leaky Rectified Linear Units (ReLU) and Glorot uniform initializer, to mitigate common issues such as the dying ReLU problem and improve stability during training. Introducing a custom combined loss function effectively tackles class imbalance, significantly improving segmentation accuracy. Evaluation using a comprehensive suite of metrics showcases the superior performance of this approach, outperforming existing methods and advancing the current techniques in lumbar spine segmentation. These findings hold significant advancements for enhanced lumbar spine MRI and segmentation diagnostic accuracy.

The recently developed MAB phases, an extension of the MAX phase, have sparked interest in research among scientists because of their better thermo-mechanical properties. In this paper, we have explored four new MAB phases M2AB2 (M=Mo, Ta and A=Ga, Ge) and studied the elastic, electronic, thermal, and optical properties to predict the possible applications. The stability of the new phases has been confirmed by calculating formation energy (Ef), formation enthalpy (H), phonon dispersion curve (PDC), and elastic constant (Cij). The study reveals that M2AB2 (M=Mo, Ta and A=Ga, Ge) exhibit significantly higher elastic constants, elastic moduli, and Vickers hardness values than their counterpart 211 borides. Higher Vickers hardness values of Ta2AB2 (A=Ga, Ge) than Mo2AB2 (A=Ga, Ge) have been explained based on the values of the bond overlap population. The analysis of the density of states and electronic band structure revealed the metallic nature of the borides under examination. The thermodynamic characteristics of M2AB2 (M=Mo, Ta and A=Ga, Ge) under high temperatures (0 to 1000 K) are investigated using the quasi-harmonic Debye model. Critical thermal properties such as melting temperature (Tm), Gruneisen parameter, minimum thermal conductivity (Kmin), Debye temperature, and others are also computed. Compared with 211 MAX phases, the 212 phases exhibit higher values of Debye temperature and Tm, along with a lower value of Kmin. These findings suggest that the studied compounds exhibit superior thermal properties that are suitable for practical applications. The optical characteristics have been examined, and the reflectance spectrum indicates that the materials have the potential to mitigate solar heating across various energy regions., Comment: 37 pages

Sleep stages play an essential role in the identification of sleep patterns and the diagnosis of sleep disorders. In this study, we present an automated sleep stage classifier termed the Attentive Dilated Convolutional Neural Network (AttDiCNN), which uses deep learning methodologies to address challenges related to data heterogeneity, computational complexity, and reliable automatic sleep staging. We employed a force-directed layout based on the visibility graph to capture the most significant information from the EEG signals, representing the spatial-temporal features. The proposed network consists of three compositors: the Localized Spatial Feature Extraction Network (LSFE), the Spatio-Temporal-Temporal Long Retention Network (S2TLR), and the Global Averaging Attention Network (G2A). The LSFE is tasked with capturing spatial information from sleep data, the S2TLR is designed to extract the most pertinent information in long-term contexts, and the G2A reduces computational overhead by aggregating information from the LSFE and S2TLR. We evaluated the performance of our model on three comprehensive and publicly accessible datasets, achieving state-of-the-art accuracy of 98.56%, 99.66%, and 99.08% for the EDFX, HMC, and NCH datasets, respectively, yet maintaining a low computational complexity with 1.4 M parameters. The results substantiate that our proposed architecture surpasses existing methodologies in several performance metrics, thus proving its potential as an automated tool in clinical settings., Comment: In review to IEEEtrans NNLS; 15-pages main paper and 3-pages supplementary material

We consider the problem of learning a Constrained Markov Decision Process (CMDP) via general parameterization. Our proposed Primal-Dual based Regularized Accelerated Natural Policy Gradient (PDR-ANPG) algorithm uses entropy and quadratic regularizers to reach this goal. For a parameterized policy class with transferred compatibility approximation error, $\epsilon_{\mathrm{bias}}$, PDR-ANPG achieves a last-iterate $\epsilon$ optimality gap and $\epsilon$ constraint violation (up to some additive factor of $\epsilon_{\mathrm{bias}}$) with a sample complexity of $\tilde{\mathcal{O}}(\epsilon^{-2}\min\{\epsilon^{-2},\epsilon_{\mathrm{bias}}^{-\frac{1}{3}}\})$. If the class is incomplete ($\epsilon_{\mathrm{bias}}>0$), then the sample complexity reduces to $\tilde{\mathcal{O}}(\epsilon^{-2})$ for $\epsilon<(\epsilon_{\mathrm{bias}})^{\frac{1}{6}}$. Moreover, for complete policies with $\epsilon_{\mathrm{bias}}=0$, our algorithm achieves a last-iterate $\epsilon$ optimality gap and $\epsilon$ constraint violation with $\tilde{\mathcal{O}}(\epsilon^{-4})$ sample complexity. It is a significant improvement of the state-of-the-art last-iterate guarantees of general parameterized CMDPs.

A systematic investigation of the electrical characteristics of \b{eta}-Ga2O3 Schottky barrier diodes (SBDs) has been conducted under high-dose 60Co gamma radiation, with total cumulative doses reaching up to 5 Mrad (Si). Initial exposure of the diodes to 1 Mrad resulted in a significant decrease in on-current and an increase in on-resistance compared to the pre-radiation condition, likely due to the generation of radiation-induced deep-level acceptor traps. However, upon exposure to higher gamma radiation doses of 3 and 5 Mrad, partial recovery of the device performance occurred, attributed to a radiation annealing effect. The capacitance-voltage (C-V) characterization revealed that the net carrier concentration in the $\beta$-Ga$_2$O$_3$ drift layer reduced from $\sim$3.19 $\times$ 10$^{16}$ cm$^{-3}$ to $\sim$3.05 $\times$ 10$^{16}$ cm$^{-3}$ after 5 Mrad (Si) irradiation. Temperature-dependent I-V characteristics showed that irradiation leads to a reduction in both forward and reverse current across all investigated temperatures ranging from 25 to 250$^\circ$C, accompanied by slight increases in on-resistance, ideality factors, and Schottky barrier heights. The reverse breakdown characteristics of the $\beta$-Ga$_2$O$_3$ SBDs showed a slight increase of the breakdown voltage after radiation. Overall, $\beta$-Ga$_2$O$_3$ Schottky diode exhibits high resilience to gamma irradiation, with performance degradation mitigated by radiation-induced self-recovery, highlighting its potential for radiation-hardened electronic applications in extreme environments.

This work investigates the thermo-chemical freeze-out condition of the multi-component hot and dense hadron resonance gas (HRG) formed in the ultra-relativistic nucleus-nucleus collisions (URNNC). The van der Waals (VDW) type model used in the present analysis incorporates the repulsive as well as attractive interactions among the hadrons. The baryons (antibaryons) are treated as incompressible objects. Using this theoretical approach the values of the model freeze-out parameters of the system are extracted over a wide range of collision energy by analyzing experimental data on like-mass antibaryon to baryon ratios. The same set of parameters is found to explain the energy dependence of several other particle ratios quite satisfactorily. We find that the horn-like structures seen in the ratios of strange particles to pions as a function of the collision energy cannot be explained by the VDW-HRG model alone without considering the strangeness imbalance effect in the system. We have compared our freeze-out line with those obtained earlier. The correlation between the $\bar{p}/p$ and $K^-/K^+$ ratios is also examined., Comment: 15 pages, 10 Figures

Linear quadratic output systems constitute an important class of dynamical systems with numerous practical applications. When the order of these models is exceptionally high, simulating and analyzing these systems becomes computationally prohibitive. In such instances, model order reduction offers an effective solution by approximating the original high-order system with a reduced-order model while preserving the system's essential characteristics. In frequency-limited model order reduction, the objective is to maintain the frequency response of the original system within a specified frequency range in the reduced-order model. In this paper, a mathematical expression for the frequency-limited $\mathcal{H}_2$ norm is derived, which quantifies the error within the desired frequency interval. Subsequently, the necessary conditions for a local optimum of the frequency-limited $\mathcal{H}_2$ norm of the error are derived. The inherent difficulty in satisfying these conditions within a Petrov-Galerkin projection framework is also discussed. Based on the optimality conditions and Petrov-Galerkin projection, a stationary point iteration algorithm is proposed that enforces three of the four optimality conditions upon convergence. A numerical example is provided to illustrate the algorithm's effectiveness in accurately approximating the original high-order model within the specified frequency interval.

Quantum Energy Teleportation (QET) is a novel method that leverages quantum entanglement to transfer energy between two distant locations without any physical movement of the energy. The first realization of QET on superconducting hardware, utilizing a 2-qubit system, demonstrated an average energy retrieval efficiency of 35.4% (observing only V ) by the receiver, Bob. In this paper, we present a new approach using a 3-qubit system to enhance the energy efficiency of QET. We have incorporated a novel 3-qubit ground state Hamiltonian H to achieve this, which conforms to the constraints of Zero mean energy and anti-commutative properties of the operations on the observable of the senders and receiver. Our experimental results show a significant improvement in terms of energy retrieval. Though the Multiple-Input Single-Output (MISO) model demonstrates a similar result achieving an average efficiency of 32.5% (observing only V ), the Single-Input Multiple-Output (SIMO) model shows a significantly higher result than that of the 2-qubit system considering practical usage, which is 67.2%., Comment: 13 pages, 13 figures, 2 table, 50+ equations

Developers continuously interact in crowd-sourced community-based question-answer (Q&A) sites. Reportedly, 30% of all software professionals visit the most popular Q&A site StackOverflow (SO) every day. Software engineering (SE) research studies are also increasingly using SO data. To find out the trend, implication, impact, and future research potential utilizing SO data, a systematic mapping study needs to be conducted. Following a rigorous reproducible mapping study approach, from 18 reputed SE journals and conferences, we collected 384 SO-based research articles and categorized them into 10 facets (i.e., themes). We found that SO contributes to 85% of SE research compared with popular Q&A sites such as Quora, and Reddit. We found that 18 SE domains directly benefited from SO data whereas Recommender Systems, and API Design and Evolution domains use SO data the most (15% and 16% of all SO-based research studies, respectively). API Design and Evolution, and Machine Learning with/for SE domains have consistent upward publication. Deep Learning Bug Analysis and Code Cloning research areas have the highest potential research impact recently. With the insights, recommendations, and facet-based categorized paper list from this mapping study, SE researchers can find potential research areas according to their interest to utilize large-scale SO data.

In this paper, we present a novel cryptographic system that integrates Quantum Key Distribution (QKD) with classical encryption techniques to secure steganographic images. Our approach leverages the E91 QKD protocol to generate a shared secret key between communicating parties, ensuring the highest level of security against eavesdropping through the principles of quantum mechanics. This key is then hashed using the Secure Hash Algorithm (SHA) to provide a fixedlength, high-entropy key, which is subsequently utilized in symmetric encryption. We explore the use of AES (Advanced Encryption Standard) algorithms for encrypting steganographic images, which hide sensitive information within digital images to provide an additional layer of security through obscurity. The combination of QKD, hashing, and symmetric encryption offers a robust security framework that mitigates various attack vectors, enhancing the confidentiality and integrity of the transmitted data. Our experimental results demonstrate the feasibility and efficiency of the proposed system, highlighting its performance in terms of key generation rates, encryption/decryption speeds, and the computational overhead introduced by the hashing and steganographic processes. By integrating quantum and classical cryptographic methods with steganography, this work provides a comprehensive security solution that is highly resistant to both quantum and classical attacks, making it suitable for applications requiring stringent security measures. This paper contributes to the ongoing research in cryptographic systems, offering insights into the practical implementation and potential benefits of hybrid quantumclassical security protocols., Comment: 18 pages, 13 figures, 46 equations

An important class of dynamical systems with several practical applications is linear systems with quadratic outputs. These models have the same state equation as standard linear time-invariant systems but differ in their output equations, which are nonlinear quadratic functions of the system states. When dealing with models of exceptionally high order, the computational demands for simulation and analysis can become overwhelming. In such cases, model order reduction proves to be a useful technique, as it allows for constructing a reduced-order model that accurately represents the essential characteristics of the original high-order system while significantly simplifying its complexity. In time-limited model order reduction, the main goal is to maintain the output response of the original system within a specific time range in the reduced-order model. To assess the error within this time interval, a mathematical expression for the time-limited $\mathcal{H}_2$-norm is derived in this paper. This norm acts as a measure of the accuracy of the reduced-order model within the specified time range. Subsequently, the necessary conditions for achieving a local optimum of the time-limited $\mathcal{H}_2$ norm error are derived. The inherent inability to satisfy these optimality conditions within the Petrov-Galerkin projection framework is also discussed. After that, a stationary point iteration algorithm based on the optimality conditions and Petrov-Galerkin projection is proposed. Upon convergence, this algorithm fulfills three of the four optimality conditions. To demonstrate the effectiveness of the proposed algorithm, a numerical example is provided that showcases its ability to effectively approximate the original high-order model within the desired time interval.

Detecting falls among the elderly and alerting their community responders can save countless lives. We design and develop a low-cost mobile robot that periodically searches the house for the person being monitored and sends an email to a set of designated responders if a fall is detected. In this project, we make three novel design decisions and contributions. First, our custom-designed low-cost robot has advanced features like omnidirectional wheels, the ability to run deep learning models, and autonomous wireless charging. Second, we improve the accuracy of fall detection for the YOLOv8-Pose-nano object detection network by 6% and YOLOv8-Pose-large by 12%. We do so by transforming the images captured from the robot viewpoint (camera height 0.15m from the ground) to a typical human viewpoint (1.5m above the ground) using a principally computed Homography matrix. This improves network accuracy because the training dataset MS-COCO on which YOLOv8-Pose is trained is captured from a human-height viewpoint. Lastly, we improve the robot controller by learning a model that predicts the robot velocity from the input signal to the motor controller., Comment: Accepted to IEEE AIM-2024

The advent of Artificial intelligence has promising advantages that can be utilized to transform the landscape of software project development. The Software process framework consists of activities that constantly require routine human interaction, leading to the possibility of errors and uncertainties. AI can assist software development managers, software testers, and other team members by leveraging LLMs, GenAI models, and AI agents to perform routine tasks, risk analysis and prediction, strategy recommendations, and support decision making. AI has the potential to increase efficiency and reduce the risks encountered by the project management team while increasing the project success rates. Additionally, it can also break down complex notions and development processes for stakeholders to make informed decisions. In this paper, we propose an approach in which AI tools and technologies can be utilized to bestow maximum assistance for agile software projects, which have become increasingly favored in the industry in recent years.