Incorporating audio comprehension into language models represents a significant leap forward in the pursuit of more dynamic and contextually aware artificial intelligence systems. The novel integration of audio knowledge into the Mistral LLM offers an innovative approach to enhancing its interpretative capabilities, enabling it to process and understand auditory information alongside textual data. Through architectural modifications such as the introduction of an audio encoder and attention-based fusion mechanism, the model demonstrated marked improvements in tasks ranging from transcription accuracy to sentiment analysis and contextual summarization. The empirical evaluation revealed that the audio-augmented model achieved lower Word Error Rates across multiple datasets and higher BLEU scores for audio-textual comprehension, showcasing its ability to handle complex and diverse linguistic environments. Moreover, the model exhibited robustness to background noise and proficiency in cross-language transcription, further highlighting its versatility and potential applications. Although the integration of audio knowledge increased the computational demands of the model, the resultant enhancement in audio comprehension justifies this trade-off, presenting a valuable tool for applications requiring sophisticated spoken language understanding.

We demonstrate that data-driven system identification techniques can provide a basis for effective, non-intrusive model order reduction (MOR) for common circuits that are key building blocks in microelectronics. Our approach is motivated by the practical operation of these circuits and utilizes a canonical Hammerstein architecture. To demonstrate the approach we develop a parsimonious Hammerstein model for a nonlinear CMOS differential amplifier. We train this model on a combination of direct current (DC) and transient Spice (Xyce) circuit simulation data using a novel sequential strategy to identify the static nonlinear and linear dynamical parts of the model. Simulation results show that the Hammerstein model is an effective surrogate for the differential amplifier circuit that accurately and efficiently reproduces its behavior over a wide range of operating points and input frequencies.

This article introduces a novel strategy for addressing health insurance fraud through the implementation of Graph-Based Social Network Analysis (SNA). By capitalizing on the interconnectedness of coverage holders, healthcare practitioners, and relevant entities, our approach generates an all-encompassing graph structure that encapsulates the complex network dynamics inherent in the health insurance ecosystem. An anomaly detection algorithm and the meticulous construction of node and edge attributes to identify minor patterns are employed to detect fraudulent activity. The outcomes illustrate the effectiveness of the suggested methodology and establish its superiority in comparison to the available alternatives. The contributions of the study to approaches for detecting health insurance fraud enhance the cost-effectiveness and integrity of the system. In light of the persistent gravity of health insurance fraud, this study offers practitioners and regulators in the industry a comprehensive and pertinent response.

Abstract-As reinforcement learning agents become more popular in critical applications across both the public and private sectors, the need to reverse engineer, comprehend, and audit their decisions becomes crucial. While there are many traditional explainable AI methods to aid in this endeavor, many approaches assume direct access to the agent model, which is not available in many reverse engineering applications such as in-house recovery of legacy models or analysis of competitor, adversarial, or other external agents. To address this research gap, this paper presents LfRLD, a framework for reverse engineering reinforcement learning (RL) agents using learning from demonstrations (LfD) approaches. Empirical results demonstrated the proposed framework's potential to aid in generalizing, predicting, and summarizing agent behaviors using only observed demonstrations, and revealed many opportunities for future research. Within the wider scope of AI, this paper's findings have many implications for auditable, and therefore, trustworthy AI, aiding applications in a variety of areas such as business and finance, criminal justice, cybersecurity and defense, and Internet of Things (IoT). Impact Statement-Reinforcement learning agents are becoming increasingly popular in a variety of applications such as chatbots, economics, healthcare, and autonomous driving. Their ability to learn consequences through trial-and-error interaction enables them to explore potentially optimal routes with longterm gains despite possible short-term losses. However, over the years, such agents have become increasingly scrutinized as their decisions are seldom interpretable by others. Although recent advances in explainable AI have aided in addressing this challenge, they typically assume access to the agent models, which are often not public. The framework introduced in this paper helps address this research gap. While the proposed technology has shown promising results for reverse-engineering agent behaviors using only observed demonstrations, there are many opportunities for future developments. In addition to enhancing agent auditability and trustworthiness, the proposed work also offers an approach for reverse-engineering agents to gain an intelligence or resource advantage over external competitors or adversaries.

The security of AI systems becomes increasingly paramount, as artificial intelligence (AI) pervades various aspects of our lives. For instance, from autonomous driving to banking and medical diagnostics, AI is playing a pivotal role in powering such security-sensitive systems. However, attacks on these high-risk AI systems could have severe consequences, jeopardizing the lives, finances, and well-being of people. Thus, adherence to fundamental security properties, namely, Confidentiality, Integrity, and Availability (CIA), is essential for AI systems to comply with security standards. In this paper, we first define a simplified abstraction of AI systems to discuss four independent viewpoints: data, models, inputs, and deployment. Subsequently, we conduct a detailed analysis of attack vectors targeting each of these viewpoints, with a rigorous assessment of CIA properties. Proactive identification of attack vectors throughout the entire AI lifecycle is a requisite step toward establishing a secure-by-design framework for AI systems.

Low Earth Orbit (LEO) satellites are increasingly being used to provide connectivity for wide area Internet of Things (IoT) sensing applications. Since distributed IoT terminals are not able to coordinate their transmissions with each other to avoid packet collisions, Quality-of-Service (QoS) depends on the transmission attempt success statistics, which are time-varying. This paper presents asymptotic analysis results that characterize the IoT terminal's transmission attempt and success processes. We show that they converge to inhomogeneous Poisson processes, in the large population regime, and characterize the timedependent intensities as functions of the terminal locations and the attempt rate scheme. We also propose three terminal attempt rate schemes that are solutions to max-min optimization problems. The performance of the proposed schemes are compared in terms of individual terminal QoS as well as the populationwide QoS distribution. Performance for various IoT applications is also presented, using our asymptotic Poisson limit results.

Continuum robots, with their slender, flexible structures, are increasingly utilized in delicate tasks. These robots must navigate confined spaces with high precision, often employing follow-the-leader (FTL) motion to ensure minimal trauma. The baseline FTL motion planning algorithm can not adjust the continuum robot's configuration using all the segments, especially the tail of the robot. Furthermore, it has limitations in dynamic, obstacle-rich environments, where real-time re-planning and obstacle avoidance are essential but often not sufficiently addressed. To address the above gaps, we propose a novel FTL motion planning framework inspired by snake hole-digging motions that can configure its optimal shape during the insertion task. Our approach outperforms the baseline FTL by 75.36%. The framework incorporates real-time obstacle avoidance by integrating Control Barrier Functions (CBF) and Quadratic Programming (QP) into the motion re-planning process. To avoid deviations caused by obstacle avoidance, the Control Lyapunov Function (CLF) is also integrated, forming a CLF-CBF-QP framework for online motion re-planning. The target reaching error of using CLF-CBF-QP decreases by 69.15% compared to using only CBF-QP. We also provide the open-source code of this work online.

As cloud adoption rises, managing operational costs has become a critical challenge for organizations. Amazon Web Services (AWS), the leading cloud provider, offers various services with flexible pricing models. However, without effective cost optimization strategies, cloud expenses can escalate quickly. This paper presents a comprehensive approach to cost optimization in AWS, combining traditional BEST PRACTICES with cutting-edge automation techniques using Large Language Models (LLM). The paper explores strategies such as right-sizing compute resources, leveraging Savings Plans, utilizing Spot Instances, optimizing storage, minimizing data transfer costs, and cleaning up unused resources. In addition to AWS-native services, the paper delves into advanced LLM-based solutions for anomaly detection, resource management, and predictive scaling. These AI-driven tools provide real-time insights and automate cost-saving measures, offering organizations intelligent ways to reduce their cloud footprint. The paper also introduces lesser-known AWS services, such as Instance Scheduler, S3 Select, VPC Endpoints, and Glacier Deep Archive, which can be utilized for further cost savings. By employing these strategies in tandem with automated solutions, organizations can significantly reduce their AWS spending while maintaining the scalability and performance of their cloud infrastructure. The findings suggest that organizations using AI-powered optimization tools and AWS BEST PRACTICES can cut costs by up to 60% while improving resource utilization. This paper serves as a detailed guide for businesses looking to optimize their AWS infrastructure and prepare for the future of AI-driven cloud management.

The properties of Cylindrical Internal-Surface Acoustic Waves (CISAW) propagating on the inside surface of a high purity tubular fused quartz fiber are derived from basic principles using a variational method. The change in the wave vector due to gravity, of the CISAW is also derived. I was unable find descriptions in technical or scientific literature of the modes of CISAW propagating on the inside surface of a tubular fiber. There is also no description in the technical or scientific literature of the effect of gravity, on CISAWs. Therefore, these derivations are completely new. The group velocity of CISAW is less than the velocity of light, and the effect of gravity on the wave vector of the CISAW is much larger than the effect of gravity on the light wave vector. Therefore, gravity detectors based CISAW would be much smaller than the present km-sized gravity detecting LIGO. One can build an array of CISAW gravity wave detectors which would be able to detect gravity wave images. It would be interesting what new discoveries could be made with such an array.

Urbanization is an inevitable process linked to regional development. As cities and towns expand, they put more pressure on the environment and resources. Understanding the evolution of urban areas and its effects on landscape dynamics are crucial for sustainable urban development. Spatial urban expansion models, such as the Cellular Automata based Patch-generating Land Use Simulation (CA-PLUS) model, have proven to be effective in tracking urban growth and predicting future expansion. These models incorporate various spatial parameters to assess their influence on regional urbanization. This study forecasts urbanization trends for the years 2025 and 2030 in Bangalore City, using the CA-PLUS model. This model integrates a land expansion analysis strategy (LEAS) with Cellular Automata-based random patch seeds (CARS), along with multiple driving factors. A 16-year time series dataset (from 2006 to 2022) was used to analyze land use patterns. The results show significant expansion in Bangalore, initially driven by growth at its core and later by peri-urban development, which has now extended beyond the city's boundaries. Various driving factors influencing land use changes were also identified and the model projected land use demand for 2022, which was then evaluated against the actual LULC map for 2022. Findings indicate that urban population, proximity to metro stations and road networks have a greater impact on urban expansion than other variables. The calibrated CA-PLUS model achieved 83% accuracy and a kappa coefficient exceeding 74%, demonstrating its utility in understanding urban land use dynamics for sustainable planning.

Facial expression analysis has been shown to be an unintrusive and cheap method of player experience modelling. Output from such systems is then often closely tied to measures of basic discrete emotions. Despite this, little games user research attempts to predict the intensity of players' emotions collected in response to validated measures of discrete emotion, that can be more widely interpreted than those used in game specific settings. Prediction of experience as a function of discrete emotions using player facial expressions therefore presents opportunities for both the furthering of intelligent game adaptation, and the fields of serious games and emotional regulation. This study tests multiple approaches for the use, and selection, of features describing players' gameplay and facial activity during an online multiplayer game. Over 18 hours of visual and behavioural data collected from 37 participants is used to build predictors of the intensity for discrete emotions of relaxation, happiness, and desire as measured using the Discrete Emotions Questionnaire, reaching a model accuracy of 55%, 57% and 63% respectively. Models using facial expression features collected over the course of entire game levels show significantly better accuracy than models using gameplay or facial reaction features.

The human face is a silent communicator, expressing emotions and thoughts through it's facial expressions. With the advancements in computer vision in recent years, facial emotion recognition technology has made significant strides, enabling machines to decode the intricacies of facial cues. In this work, we propose ResEmoteNet, a novel deep learning architecture for facial emotion recognition designed with the combination of Convolutional, Squeeze-Excitation (SE) and Residual Networks. The inclusion of SE block selectively focuses on the important features of the human face, enhances the feature representation and suppresses the less relevant ones. This helps in reducing the loss and enhancing the overall model performance. We also integrate the SE block with three residual blocks that help in learning more complex representation of the data through deeper layers. We evaluated ResEmoteNet on three open-source databases: FER2013, RAF-DB, and AffectNet, achieving accuracies of 79.79%, 94.76%, and 72.39%, respectively. The proposed network outperforms state-of-the-art models across all three databases. The source code for ResEmoteNet is available at https://github.com/ArnabKumarRoy02/ResEmoteNet.

This paper presents a comprehensive study on the design, analysis, and performance of Metal-Insulator-Metal-Insulator-Metal (MIMIM) multilayer stacks. The behavior of these stacks was modeled using the Transfer Matrix Method (TMM), allowing for a detailed investigation into their optical properties under varying parameters such as refractive index, layer thickness, polarization, and angle of incidence. The results highlight significant trends, including the effects of changing the incidence angle and layer thickness on reflection dips and absorption peaks, which exhibit wavelength-dependent shifts and intensity variations. These finding are particularly relevant for optimizing selective emitters in thermophotovoltaic (TPV) systems, where specific resonant peaks are desirable for efficient energy conversion. By adjusting the thickness of the metal and insulator layers, the study demonstrates how precise control over multilayer parameters can enhance performance, particularly through the strategic tuning of Fabry-Perot cavities within the stacks. The insights provided pave way for future applications in photonic and optoelectronic devices.

Integrated Sensing and Communication (ISAC) technology is pivotal in advancing multifunctional radio applications for nextgeneration wireless communications. One of the primary challenges in ISAC systems lies in understanding and interpreting wireless channels for both sensing and communication purposes. A clear relationship between these channels would allow ISAC systems to reduce channel estimation complexity. This paper introduces a comprehensive framework for reconstructing communication channels using monostatic sensing channels within an ISAC system. We first consider the frequency spacing of an ISAC system to geometrically illustrate the synergies and differences between the sensing and communication regions. Subsequently, we construct a communication propagation channel based on overlap determined by this geometric relationship and a simplified monostaticto-bistatic equivalence theorem. Lastly, we combine multipaths located in the same delay bin to construct channel state information (CSI), taking bandwidth limitation into account. Simulations demonstrate that the proposed framework efficiently generates communication channels across various configurations, with numerical results highlighting the impacts of environmental and system constraints on key channel characteristics.

The detection of faults in photovoltaic (PV) systems plays a major role in extending the lifetime of the employed modules and maintaining the system's efficiency. Machine learning (ML) algorithms are one of the most popular approaches for fault detection in PV systems. In this paper, it is proposed a novel approach called Logistic Model Tree (LMT) algorithm for the analysis of I-V curves of line-to-line fault, shading fault, degradation fault, and open circuit fault on the DC side of the PV systems. At first, a 2x2 PV system is built in MATLAB Simulink environment to generate I-V curves under healthy and different fault conditions and as a result, an imbalanced fault dataset is created. The imbalanced fault dataset is divided into training and testing datasets and then k-means under sampling method is used to resolve the class imbalance problem. LMT and other popular machine learning algorithms that are used in PV fault detection are trained on the training dataset with the 5-fold cross-validation method with grid-search and their performances are evaluated on the testing dataset. In the end, Matthew's Correlation Coefficient (MCC), Cohen's Kappa, and Macro F-1 of the algorithms is computed from the confusion matrices, and the performances of the algorithms are compared with each other. According to the findings, the LMT algorithm emerges as the top performer with the highest MCC (0.8657443), Kappa (0.8609745), and F-1 scores (0.8539005), solidifying its place as the most effective machine learning algorithm for detecting faults in PV systems.

The article introduces Multidimensional Bayesian Networks (MBNs), an advanced extension of traditional Bayesian Networks (BNs) and Object-Oriented Bayesian Networks (OOBNs). While OOBNs are useful for modelling complex systems by modularizing subsystems into objects and classes, they struggle with heterogeneous components and inference computation in multi-dimensional spaces. MBNs address these issues by assigning multiple indices to variables, enabling the modelling of large systems across various dimensions, such as space and time. The article provides an overview of OOBNs, highlighting their advantages, such as modularity, reusability, and scalability. However, it also discusses limitations, including computational complexity and the challenges of hierarchical modelling and inference, particularly for large-scale systems. The proposed MBN model overcomes these challenges by expanding dimensionality, with key innovations like "virtual nodes" and a structured inference process that allows local computations within objects before propagating information across the system. The new algorithms and inference methods introduced allow for more efficient handling of complex relationships in multidimensional systems, making MBNs a powerful tool for decision-making in fields requiring large-scale, dynamic modelling.

An adaptive, a variable code rate,can respond to channel changes promptly channel coding method is proposed,a discrete memoryless binary source sequence 𝑿 is transformed losslessly to make it capable of error checking. After the transformation, 𝑿 has variable and fixed code rates through weighted probability arithmetic coding. Compared with a fixed code rate, a variable code rate can respond to channel changes promptly and effectively improve the transmission performance, thereby being suitable for more complex communication environments. We prove that when the code length is long enough, the decoding error probability of the weighted probability arithmetic coding is 0 and the encoding rate can reach 1. When the symbols in the sequence 𝑿 are uniformly distributed, the transmission rate can reach the channel capacity. In an additive-white-Gaussian-noise channel binary-phase-shiftkeying signal simulation experiment, the error correction performances under various fixed code rates were compared. When the fixed code rate was 0.5 and the block error rate (BLER) was 10 −3 the weighted probability arithmetic coding improved by 0.37 dB compared to that obtained using Polar CA-SCL, and when BLER was 10 −5 , it improved by 0.48 dB. The algorithm can be widely used in communication systems, data storage, and other fields.

This study investigated the physicochemical properties and glycemic potential of two honey samples from Ikwuano (Umuahia) and Umuabiara Amii Akabo (Imo state). The research aimed to determine the sugar content, moisture content, pH, specific gravity, and glycemic load of the honey samples, providing valuable insights for consumers, particularly those with dietary restrictions or preferences. The results revealed significant differences in the physicochemical properties of the two honey samples. The moisture content of the Ikwuano honey was 29.5933%, while that of Umuabiara Amii Akabo was 30.4338%. The pH of Ikwuano honey was 4.28, indicating a slightly acidic nature, while that of Umuabiara Amii Akabo was 3.45, indicating a more acidic nature. The specific gravity of Ikwuano honey was 1.2680, while that of Umuabiara Amii Akabo was 1.2705, indicating a slight difference in density. The reducing sugar content was determined using lead acetate and potassium oxalate solutions, and the titration values for the Ikwuano honey and Umuabiara Amii Akabo honey were 22.0 and 19.0, respectively. The glucose content was also determined, and the glycemic load of the Ikwuano honey was found to be 46.12, while that of the Umuabiara Amii Akabo honey was 50.72.These findings suggest that the two honey samples have different glycemic potentials, with the Umuabiara Amii Akabo honey having a higher glycemic load. This information is crucial for individuals with dietary restrictions, such as those with diabetes, who need to monitor their sugar intake. Additionally, the study highlights the importance of analytical techniques in determining the quality and properties of honey samples.The research advances our understanding of the physicochemical properties and glycemic potential of honey samples from different regions, providing valuable insights for consumers, producers, and regulatory agencies. The study’s findings can inform the development of guidelines for honey production and labeling, ensuring that consumers have access to accurate information about the products they consume. Furthermore, the research contributes to the growing body of knowledge on the nutritional and health benefits of honey, highlighting its potential as a natural sweetener and functional food ingredient.